The increasing deployment of digital healthcare systems has led to the continuous transmission of highly sensitive patient data, raising urgent concerns about data leakage in high-noise, high-loss, and dynamically changing. Existing privacy-preservation techniques often struggle to provide robustness, low overhead, and real-time responsiveness under high jitter and packet loss, limiting their effectiveness in rapid detection and accurate tracing of leaks. To address these challenges, we propose a Reinforcement Learning-Driven Joint Watermarking Framework (RLDJ-W). First, it utilizes a reinforcement learning strategy to adaptively modulate the watermark embedding interval, ensuring both invisibility and enhancing the watermark’s survivability in harsh channels. Then, it leverages Bi-LSTM to capture and model multi-granularity time-series features of network flows, thereby dynamically evaluating the invisibility of the watermark flows. Finally, a high-performance decoding network based on MLP is designed to achieve efficient and accurate watermark information extraction. Experimental results demonstrate that the watermarking capacity of RLDJ-W achieves 2.25 bit/s, requiring only an average of 5.88 packets per bit of watermark. It also maintains over 85% detection accuracy even under 100ms delay jitter and 40% packet loss, consistently outperforming state-of-the-art baselines.

Nowadays, federated learning prevalently adopt privacy-preserving collaborative learning approach to deal with data isolation for various purposes, but were found unauthorized use and leaking model privacy. To address this issue, this letter introduces a novel framework, tackling the malicious gradients in decentralized federated learning (DecFL) by reversible differential privacy and secure history-aware aggregation strategy. We develop a chaos-based reversible differential privacy to perturb the model weights during transmission to protect against gradient inversion attacks. Moreover, we also propose a history-aware decentralized aggregation strategy that utilizes bottom-up approach to detect and remove malicious attackers using temporal information. Extensive evaluations on two standard datasets validate that our framework achieves state-of-the-art security solutions across a spectrum of attacks. Our proposed system maintained backdoor accuracy and model accuracy of$\sim$4% and$\sim$90%, respectively, achieving outstanding performance in dealing with all manner of model poisoning, with attacker percentages being up to 50% of the total clients. Moreover, the computational cost of the aggregation step is 2.4% of the total cost. This highlights its promising defense solution for secure dissemination of multimedia signals in distributed networks at low cost.

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Nowadays, massive amounts of facial images have been tampered with and then widely spread through social networks. Many studies have developed algorithms for frame-level DeepFake detection. However, they have low robustness due to their focus on tamper-independent features during training. To this end, we propose a framework, namely MIF-Net, based on multi-information fusion for robust frame-level DeepFake detection. Specifically, key landmarks and the facial area are first detected in the original frame. Then, the graph convolutional network constructs biometric information from these landmarks. Meanwhile, the facial region is processed into multi-view inputs by noise and edge enhancement algorithms. Finally, these products are encoded as high-level features and classified as real or fake. Five benchmark datasets are utilized for testing our model through within-dataset and cross-dataset validations. Extensive experiment results demonstrate that our proposed MIF-Net is robust and has advantages over peer algorithms.

Abstract Data hiding has become a hot research topic in recent years due to increased attention placed on the copyright protection of ocean images and related digital records. Further, high image volumes put enormous pressure on transmission bandwidth and storage capabilities. This paper proposes an innovative deep learning‐based data‐hiding technique for ocean images. First, a down‐sampling scheme is applied to compress the secret mark before embedding it in the host media. Then, a convolutional neural network is used to embed and recover compressed marks into or from the host ocean image. Finally, a generative adversarial network‐based reconstruction network is used to reconstruct the high‐quality mark image. Our experiments show that the proposed work not only maintains high imperceptibility and robustness against many attacks but also provides better data‐hiding performance than related works.

This article proposes a cloud-edge–terminal collaborative resource allocation architecture that efficiently allocates resources. Traditional resource allocation often focuses solely on optimizing delay and service cost, making it less suitable for intensive real-world scenarios. In response, a comprehensive multiobjective resource allocation model is developed, encompassing delay, service cost, load balancing, and resource utilization. This article proposes a diversity-filling large-scale multiobjective evolutionary algorithm based on generative adversarial networks (DFGAN-LSMOEA). The Otsu-based grouping method in DFGAN-LSMOEA is employed to group decision variables and improve optimization performance. Compared with state-of-the-art algorithms, the proposed method validates its effectiveness and advantages in the applications, particularly when handling high-dimensional decision variables and dynamic demands.

Many people have been severely affected by the COVID-19 pandemic, which has caused intense anxiety, fear, and complex feelings or emotions. People’s emotions have changed and become more complicated since coronavirus vaccinations were introduced. Sentiment analysis of COVID-19 vaccination is critical for understanding public perception, vaccine hesitancy, monitoring vaccine impact, identifying adverse reactions, making policies, and allocating resources. Some artificial intelligence (AI)-based systems have been reported in the literature to analyze the sentiment of COVID-19 vaccination. However, most of them are end-to-end models that require explanation for their results in identifying COVID-19 vaccination sentiment. An explainable AI-based model can improve decision-making, transparency, and interpretability. It can enable users to comprehend how the model makes predictions and the factors that influence the outcome. Therefore, this study suggests a COVID-Twitter-BERT and LSTM (CT-BERT-LSTM) staking that is explicable for determining people’s views on the COVID-19 vaccination. The prediction of the proposed CT-BERT-LSTM model is then examined to determine where the suggested system successfully learned the context of the tweet and where it failed to do so. The proposed CT-BERT-LSTM model performed exceptionally well and outperformed the existing models with aF1-score of 0.88 in the sentiment identification of the COVID-19 vaccination.

Nowadays, the ownership of shared social images has attracted increasingly serious privacy violation concerns, thus the protection of these images is particularly important. Further, due to the increasing value of deep learning (DL) models in social media platforms, there is an urgent demand to protect their copyright and prevent privacy leakage. Currently, relatively limited research has been carried out in the field of ownership protection for deep neural network (DNN) and, at the same time, related social images. This article presents a robust copyright protection system and method for the DNN model and related social images to verify ownership. Initially, split-way training of our model was established, based on cover and encoded secret images, to reduce or eradicate privacy leakage. Then, the sender sends only encoded vector information instead of raw data to adversarial-based embedding and extraction networks. Next, we embed a secret mark in DL embedding and extraction models, using interpolation-based watermarking to verify the ownership of suspicious models if any piracy or infringements occur. Last, the intended receiver extracts the hidden information using the extraction networks. Extensive experiments show that the proposed system is more robust and secure against attacks than state-of-the-art methods, making it beneficial for social media and other practical applications. The results demonstrate that the proposed method outperforms state-of-the-art methods in terms of average peak signal-to-noise noise ratio, normalized correlation, number of pixel change rate, and unified average change intensity, with improvements of 47.25%, 43.25%, 17.89%, and 14.23%, respectively.

Along with the fast development of high-speed networks and advanced wearable and intelligent devices, a large number of multimedia contents are now widely used by social networking sites and content-sharing services as information carriers for various applications [1]. The integration of multimedia and social media, which we call social multimedia, supports new types of user interaction. Motivated by the tremendous growth of social media applications, social computing has emerged as a novel computing paradigm that involves studying and managing social behavior and organizational dynamics to produce intelligent applications [2]. However, the wide prevalence of social multimedia poses a significant challenge for social computing because many new issues involving social activity and interaction around multimedia must be addressed in a media-specific manner. Nevertheless, multimedia research still remains open, given the challenging nature of the research focus in this area. Social multimedia can help improve existing multimedia applications, so the term social multimedia computing to denote the more focused multidisciplinary research and application field between social sciences and multimedia technology. In computational social system, multimedia plays a vital role in comfortable communication and social interaction, and providing valued data for analysis. It enhances user experience, supports diverse content sharing, and drives the development of social multimedia computing. This special issue unites pioneering studies that confront these challenges and chart new directions in social multimedia computing research from quantitative and/or computational perspective. The contributions span innovative models, algorithmic approaches, and emerging applications. This issue focuses on the technical and practical challenges faced in analyzing social media, user-generated content, and multimedia data. Understanding the role of multimedia in social contexts, maintaining data reliability, privacy, and security, and developing artificial intelligence (AI)-based solutions are the key goals in this direction. Together, they advance both the theoretical foundations and practical implementations of social multimedia computing, fostering richer human–machine symbioses and promoting enhanced social well being in multimedia contexts.

There is significant interest in edge computing (EC) for computational social systems to process and store data at the edge of the network. One of the key applications of EC is to analyze the large-scale social data, received from multiple sources using machine learning/deep learning (ML/DL) models with minimum delay and higher accuracy. However, traditional models are often large and require significant computational resources, posing a challenge in resource-constrained edge networks. Besides that, traditional centralized ML/DL methods including collaborative learning have limitations such as data privacy and communication overhead. Federated learning (FL) is an alternative solution to overcome some of these limitations by allowing model training across multiple decentralized devices without sharing the actual data. However, the standard FL approaches face some challenges, including extended training times due to the heterogeneous devices and the risk of single-point failure. To address these challenges, in this article, we propose a novel Gossip-assisted DL model for resource-constraint edge devices problem by enabling decentralized and serverless training while mitigating the risk of single-point failure. Besides that, we develop a lightweight model extractor for local edge devices to train a DL model with the collaboration of neighboring devices that improves knowledge discovery with higher prediction accuracy. Extensive simulation results over two publicly available large-scale datasets demonstrate the effectiveness of the proposed approach over the state-of-the-art techniques.

Breast cancer is a devastating disease that affects women worldwide, and computer-aided algorithms have shown potential in automating cancer diagnosis. Recently Generative Artificial Intelligence (GenAI) opens new possibilities for addressing the challenges of labeled data scarcity and accurate prediction in critical applications. However, a lack of diversity, as well as unrealistic and unreliable data, have a detrimental impact on performance. Therefore, this study proposes an augmentation scheme to address the scarcity of labeled data and data imbalance in medical datasets. This approach integrates the concepts of the Gaussian-Laplacian pyramid and pyramid blending with similarity measures. In order to maintain the structural properties of images and capture inter-variability of patient images of the same category similarity-metric-based intermixing has been introduced. It helps to maintain the overall quality and integrity of the dataset. Subsequently, deep learning approach with significant modification, that leverages transfer learning through the usage of concatenated pre-trained models is applied to classify breast cancer histopathological images. The effectiveness of the proposal, including the impact of data augmentation, is demonstrated through a detailed analysis of three different medical datasets, showing significant performance improvement over baseline models. The proposal has the potential to contribute to the development of more accurate and reliable approach for breast cancer diagnosis.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

Abstract Deep learning has become a promising model in the industry due to its superior learning accuracy and efficiency. In addition to conventional applications, such as fraud detection, natural language processing, image classification and reconstruction, object detection and segmentation this model can be widely used for data hiding, that is, watermarking. Existing transformed‐domain‐based watermarking provided better robustness toward attacks. In this article, an interesting autoencoder convolutional neural network (CNN)‐based watermarking technique, AutoCRW, is proposed, which can prevent intellectual property theft of digital images. First, the autoencoder functionality of CNN generates two versions of the same image, namely positive and negative version of the images, which decompose by a transformed domain scheme. Then, watermark information is embedded into the output images, which can be extracted to realize copyright protection and ownership verification. Finally, a denoising convolutional neural network (DnCNN) is employed over the extracted mark to ensure the robustness of the watermarking system. Extensive experiments demonstrate that the proposed algorithm has high invisibility and good robustness against several attacks.

This work intends to enhance the standard of rehabilitative care provided to patients by optimizing the medical automation monitoring system enabled by wearable computing by edge cloud and Internet of Things technology. First, the recent research literature on edge cloud and wearable computing devices is analyzed. Recent studies in virtual reality and automated medical rehabilitation are used to analyze and contrast various data fusion techniques in wearable sensors. Subsequently, an edge cloud model is constructed to enable real-time tracking of patients’ vital signs, allowing for timely assessment of their health status and rehabilitation progress. Then, a wearable device information monitoring rehabilitation system is established to provide effective rehabilitation treatment for stroke patients. The monitoring module of the rehabilitation system incorporates an edge computing terminal device, which models the virtual circuit for enhanced functionality. Based on the findings, when the number of output codes from the network structure is set to 1000, the average conversion energy of the dynamic conversion system is 140, the first conversion energy of the proposed model is 160, and the second conversion energy is 220. Regarding the rehabilitation treatment effectiveness, the system developed here demonstrates superior operational efficiency and delivers improved outcomes in rehabilitation treatment. This work serves as a practical reference for advancing the intelligent transformation of the medical service system.Note to Practitioners—The goal of this work is to examine the viability of an automated virtual rehabilitation system for use in medical rehabilitation training for stroke, auto accident, and post-operative stroke rehabilitation. Combining the current research status of edge cloud and wearable devices reveals that the majority of existing rehabilitation training approaches are primarily reflected at the theoretical level, with only a few instances of practical applications highlighted. Therefore, this work presents a novel method for designing a wearable medical information monitoring system that operates on the edge cloud with the wearable device computing automation service as its foundation. It can track the patient’s vitals in real time to gauge the rehabilitation’s success. When compared to other cutting-edge rehabilitation options, the validated established edge cloud model clearly stands out as superior and can aid patients in achieving better outcomes from their rehabilitation treatment. The result demonstrates that the method is effective. In the future, it is planned to apply this rehabilitation system to more treatment cases and improve the overall design of the system.

Hate speech on social media has become a big problem, making regular users very upset and giving victims depression and suicidal thoughts. Early identification of the user spreading this type of hate speech may be a better solution, allowing hate speech to be stopped at source. In this article, we attempt to identify these hate speech spreaders by finding a representation for each user. Each user’s comments are aggregated and fed to an auto-encoder to train it. The encoder part of the auto-encoder is used to get an encoded vector for each user. The encoded vector is used with different machine learning (ML) classifiers to determine if a user is spreading hate speech. The proposed model was tested using the dataset released by PAN 2021 (https://pan.webis.de/data.html) hate speech spreader profiling competition in English and Spanish. The experimental results show that support vector machine (SVM) with encoded vectors as features outperforms existing models with an accuracy of 92% for both English and Spanish dataset. The proposed features extraction technique is found to be equally effective at identifying fake news spreaders on fake news datasets provided by PAN 2020 yielding accuracy values of 95% and 83% for English and Spanish, respectively.

Breast cancer is a significant global health concern, with Invasive Ductal Carcinoma (IDC) being a significant subtype. Detecting IDC is a challenging task that can be hindered by the oversight of important contextual cues within Whole Slide Images (WSIs). To address this issue, we present the SequentialLSTM Invasive Ductal Carcinoma Detection with EfficientNet (SLIDE-Net) framework. SLIDE-Net synchronizes tissue image patch locations within WSIs, allowing for comprehensive and subtle identification of IDC. The inherent issue of data imbalance in IDC datasets, particularly the variable density of positive IDC patches concerning WSI size, is effectively tackled through the introduction of Adaptive Fuzzy C-Mean as a Data Balancing Policy. This novel approach enhances model efficacy and robustness, resulting in superior performance across key metrics such as accuracy (86%), balanced accuracy (86%), sensitivity (87%), specificity (86%), and GMean (86%). Our findings were substantiated by a comprehensive analysis revealing the significant cross-patch dependencies observed among patches of similar classes. When tested on the balanced PatchCAM dataset, SLIDENet once again showcased superiority with accuracy (89%), balanced accuracy (89%), F1-Score (87%), sensitivity (87%), and specificity (90%). This underscores the efficient utilization of shared contextual information within WSIs. These results firmly establish SLIDE-Net as a robust and reliable solution for accurate IDC detection. Our work not only advances the precision of IDC detection but also contributes valuable insights into the intricate dynamics of histopathology image analysis, paving the way for enhanced diagnostic accuracy in the ongoing battle against breast cancer. Index Terms—Breast Cancer, Sequential-Modeling, Data Balancing Policy, WSI, Adaptive Fuzzy C-Mean, Deep Learning.

The industrial sector has experienced a tremendous advancement in deep supervised learning due to its representation ability, but it comes with high computing and labeled data demands. Recently, the demand for intelligent IoT devices on assembly and disassembly lines has surged. This necessitates algorithms that can use data to make intelligent decisions and a framework that can enable multiple IoT devices to learn collaboratively. Further, huge image generation using IoTs also needs an efficient data annotation scheme for classification problems. WeCollab is one such framework in federated learning that significantly reduces human efforts in data annotation with breakthroughs in self-supervised learning. The proposed framework is generic and can be adapted to any specific image data generated by industrial robots involved in assembly and disassembly lines. Our method outperforms supervised learning by 25% and 20% on the CIFAR-10 and CINIC-10 datasets, respectively, for the labeling task. We generate pseudo labels for the unlabeled part of the data and train a model to achieve 30% better test accuracy on CIFAR-10 and 20% better test accuracy on the CINIC-10 dataset as compared to supervised learning. Extensive experiments unveil the effectiveness of the method and proposed combination of loss functions used by WeCollab.

Recent advances in Artificial Intelligence (AI) have revolutionized the area of biomedical imaging, providing unprecedented prospects for precision diagnoses. This special issue offers an overview of the integration of AI into biomedical imaging systems and its tremendous impact on improving diagnostic accuracy and efficiency. The combination of AI and biomedical imaging has resulted in intelligent systems capable of deciphering complex medical pictures with amazing precision. Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown exceptional capabilities in recognising patterns and extracting meaningful information from a variety of imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) [1].

Low hemolytic therapeutic peptides have gained an edge over small molecule-based medicines. However, finding low hemolytic peptides in laboratory is time-consuming, costly and necessitates the use of mammalian red blood cells. Therefore, wet-lab researchers often performin-silicoprediction to select low hemolytic peptides before proceeding with in-vitro testing. Thein-silicotools available for this purpose have following limitations: (i) They do not provide predictions for peptides having N/C terminal modifications. (ii) Data is food for AI; however, datasets used to create existing tools do not contain peptide data generated over past eight years. (iii) Performance of available tools is also low. Therefore, a novel framework has been proposed in current work, which utilizes recent dataset and uses ensemble learning technique to combine the decisions produced by bidirectional long short-term memory, bidirectional temporal convolutional network, and 1-dimensional convolutional neural network deep learning algorithms. Deep learning algorithms are capable of extracting features themselves from data. However, instead of relying solely on deep learning-based features (DLF), handcrafted features (HCF) were also provided so that deep learning algorithms can learn features that are missing from HCF, and a better feature vector can be constructed by concatenating HCF and DLF. Additionally, ablation studies were carried out to understand the roles of an ensemble algorithm, HCF, and DLF in the proposed framework. Ablation studies found that the ensemble algorithm, HCF and DLF are crucial components of proposed framework, and there is a decrease in performance on eliminating any of them. Mean value of performance metrics, namely$A_{cc}$,$S_{n}$,$P_{r}$,$F_{s}$,$S_{p}$,$B_{a}$, and$M{cc}$obtained by proposed framework for test data is$\approx$87, 85, 86, 86, 88, 87, and 73, respectively. To aid scientific community, model developed from proposed framework has been deployed as a web server athttps://endl-hemolyt.anvil.app/.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Copyright violation issues have a growing impact on applications of the digital era, especially images. It is not easy to guarantee the copyright protection of essential information. This paper presents a robust copyright protection technique with high embedding capacity for color images. The technique first fuses the multi-focus images using non-subsampled contourlet transform (NSCT) to ensure that the fused images have more rich information than a single image. Further, hash value of the cover media is computed for authentication purposes. The fused image and hash value of the cover is then embedded into the cover media with the help of transformed-domain schemes. To achieve higher security, we apply an encryption scheme on a fused media watermark before embedding it into the cover. Lastly, a hybrid optimization algorithm is employed to compute an optimal factor, which makes a good imperceptibility and robustness at the same time. We demonstrate that the proposed technique is effective and resistant to common attacks on image datasets. Compared to existing works, this work increases the 9.5% robustness and 8.8% quality with high embedding capacity.

Internet of Things (IoT) systems cannot successfully realize the notion of ubiquitous connectivity of everything if they are not capable to truly include 'multimedia things'. However, the current research and development activities in the field do not ...

Summary Due to the remarkable development of Internet technologies, a great deal of valuable digital data is now transmitted over public networks. To guarantee the security of this data during the transfer process, the authentication of its integrity is extremely important. This paper introduces a robust and secure dual‐watermarking‐based fusion of watermarking, optimization, and a compression method utilizing non‐sub‐sampled contourlet transform (NSCT), redundant discrete wavelet transform (RDWT), and singular value decomposition (SVD). In our method, we first apply the NSCT to a higher entropy sub‐band of the host image. Then, our method uses RDWT‐SVD on higher frequency coefficients of the NSCT image. A similar procedure is followed for both mark images. Finally, an appropriate scaling factor, as obtained by fuzzy inference system, is used to invisibly embed the singular values of both mark data into the host image. Here, any more important mark data are scrambled before the embedding process. The simulation tests reveal that the proposed technique is not only imperceptible and secure but also robust against common attacks. The suggested method has a superior ability to extract hidden information than previous conventional techniques.

Today, in the era of big data, an increasingly serious problem is the security of digital media in the healthcare domain. Encryption is a popular technique to resolve the security concern of medical data. In the paper, the authors propose a key-based encryption algorithm – namely, SeMIE, designed by RDWT and RSVD for healthcare applications – which can guarantee the security of the medical images. Initially, the image normalisation procedure along with RDWT-RSVD is followed to generate hash value. Here, image normalisation is used to ensure the high resistance against the geometric modifications. Then, a key expansion process is utilised with the hash value for generating the secure keys. Finally, the encryption process uses Feistel structure along with constant substitution-permutation functions to provide sufficient confusion and diffusion of cipher data. Experimental evaluation indicates that the SeMIE algorithm is secure against several attacks. From the simulation findings, it is inferred that the algorithm exhibits improved security compared to existing methods.

With the growth and popularity of the utilization of medical images in smart healthcare, the security of these images using watermarks is one of the most recent research topics. This algorithm is based on the joint use of dual watermarking, nature-inspired optimization, and encryption schemes utilizing redundant-discrete wavelet transform (RDWT) and randomized-singular value decomposition (RSVD). The key idea of the proposed method is to embed system encoded media access control (MAC) address in patient’s ID card image via discrete wavelet transform (DWT) to generate the final mark. Afterward, embed the generated watermark into computed tomography (CT) scan images of the COVID-19 patient and general images through employing the RDWT and RSVD. Further, we use a hybrid of particle swarm optimization (PSO) and Firefly optimization techniques to determine the optimal scaling factor for embedding purposes. After that, the watermarked CT scan image is encrypted using an encryption technique based on a nonlinear-chaotic map, random permutation, and singular value decomposition (SVD). Extensive evaluations establish the benefit of our proposed algorithm over the traditional schemes. The optimal robustness is more effective than the five traditional schemes at lower computational efficiency.

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme.

With the development of the industrial internet technology, the medical data exchange in IoT systems has become more prosperous. Specially, more and more medical images produced by industrial and intelligent devices are outsourced to the cloud for convenient use. However, IoT systems deployment poses several medical data security challenges. To address this issue, in this article, a robust medical data hiding scheme based on secure hybrid optimization for industrial scenario image is presented. Specifically, the marked image is obtained through non-subsampled shearlet transform-multiresolution singular value decomposition. In order to generate the dual marks, we employ the Fisher–Yates permutation to produce the scrambled system watermark address for embedding into the mark image. Afterward, the generated mark image is embedded in the chosen coefficients of the cover in an invisible way. After the watermarking, a hybrid optimization-based encryption scheme is utilized to secure the marked image. Extensive experiments demonstrate the invisibility, security, and robustness of our scheme. Further, the superiority of the scheme is elaborated through making the comparison with the other similar algorithms. The solution not only performs the robust exchange of medical data but also protects the privacy of patients.

Over the recent years, volume of medical images and related digital records, called electronic medical records, generated, shared, and stored by different intelligent devices, sensors, and Internet of medical things networks, to name a few, has drastically increased. Such records are shared by cloud providers for storage and further processing. However, an increasingly serious concern is the illegal copying, modification, and forgery of medical records. This article presents a visible and imperceptible medical data hiding technique, namely ViMDH, which can prevent to intellectual property theft of medical records. The carrier image is visibly marked with logo mark, which is suitable for owner identification and avoid illegal duplication, and then an imperceptible data hiding based on nonsubsampled shearlet transform (NSST), redundant discrete wavelet transform (RDWT), and multiresolution singular value decomposition is introduced. Finally, key-based encryption scheme designed by RDWT-RSVD ensure the security of the watermarking system. Under the experimental evaluation, our ViMDH is not only visible and imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.

Recently, artificial intelligence approaches are widely suggested to optimize numerous offloading task-scheduling purposes. However, they confront difficulties in maintaining data privacy regarding the context of the data offloading during the course of offloading in the different stages. To address this problem, in this article we proposeC-fDRL, a framework to provide context-aware federated deep reinforcement learning (fDRL) to maintain the context-aware privacy of the task offloading. We perform this in three stages (CloudAI, EdgeAI, and DeviceAI) of the overall system.C-fDRLchecks whether the privacy of high-context-aware data with the task being offloaded is maintained locally at the DeviceAI, and low-context-aware data distributedly at the EdgeAI. When there is an offloading task request or a user needs to offload the data,C-fDRLuses a context-aware data management approach to decouple the context-aware (privacy) data from the tasks. This separates the context-aware data from the task for local computation and allows a new scheduling technique called “context-aware multilevel scheduler.” This places high-context-aware data on local devices and low-context-aware data at the edge device for computation before the actual task execution. We performed experiments to evaluate the data privacy with the offloading tasks and the federated DRL. The results show that the proposedC-fDRLperforms better than the existing framework.

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Four-chamber (FC) views are the primary ultrasound(US) images that cardiologists diagnose whether the fetus has congenital heart disease (CHD) in prenatal diagnosis and screening. FC views intuitively depict the developmental morphology of the fetal heart. Early diagnosis of fetal CHD has always been the focus and difficulty of prenatal screening. Furthermore, deep learning technology has achieved great success in medical image analysis. Hence, applying deep learning technology in the early screening of fetal CHD helps improve diagnostic accuracy. However, the lack of large-scale and high-quality fetal FC views brings incredible difficulties to deep learning models or cardiologists. Hence, we propose a Pseudo-Siamese Feature Fusion Generative Adversarial Network (PSFFGAN), synthesizing high-quality fetal FC views using FC sketch images. In addition, we propose a novel Triplet Generative Adversarial Loss Function (TGALF), which optimizes PSFFGAN to fully extract the cardiac anatomical structure information provided by FC sketch images to synthesize the corresponding fetal FC views with speckle noises, artifacts, and other ultrasonic characteristics. The experimental results show that the fetal FC views synthesized by our proposed PSFFGAN have the best objective evaluation values: SSIM of 0.4627, MS-SSIM of 0.6224, and FID of 83.92, respectively. More importantly, two professional cardiologists evaluate healthy FC views and CHD FC views synthesized by our PSFFGAN, giving a subjective score that the average qualified rate is 82% and 79%, respectively, which further proves the effectiveness of the PSFFGAN.

The papers in this special section (Part II) focus on advanced machine learning algorithms for biomedical data and imaging. The papers in Part II aim at bringing together contributions from both academia and industry to highlight the recent progress of machine-learning algorithm specific to medical data.

Software-defined vehicular networks (SDVN) can help analyze and reconfigure networks. Massive data generation in autonomous vehicles can lead to issues in network configuration, routing, network characteristics, and system load factors. Load balancing in vehicle sensors helps reduce delays and improve resource utilization. In this paper, we propose a load balancing algorithm to map sensor data, vehicles and data centers performing tasks. A dynamic convergence method is proposed to help identify vehicle system load factors and compare their termination criteria. We also propose a packet-level intrusion detection model. After all load balancing, the model can track the attack on the network. The proposed model further combines the entropy-based active learning and the attention-based model to efficiently identify the attacks. Experiments are then conducted on the standard KDD data to validate the developed models with and without an attention-based active learning mechanism. Our experimental results show that the load balancing mechanism is able to achieve more performance gains than previous techniques. Moreover, the results show that the developed model can improve the decision boundary by using a pooling strategy and an entropy uncertainty measure.

The present work expects to explore the application effect of biologically inspired Plasticity Neural Network in the industrial intelligent dispatching energy storage system, and highlight the intelligence and fault detection performance of the control system. To address the faults in intelligent dispatching energy storage system, the present work implements a fault diagnosis model of intelligent dispatching energy storage system based on Deep Belief Network (DBN), and simulates and analyzes the model. The results show that the transmission probability of the fault diagnosis model of the constructed intelligent energy storage scheduling system is 100% and when the parameters$\lambda $is between 0.01 and 0.05, the real-time performance of data transmission is the highest. Compared with other classical algorithm models, the success rate and detection accuracy of the proposed algorithm are about 85%, the energy consumption is lower, and the detection effect is more obvious. Therefore, the constructed system obviously has higher real-time performance and more accurate fault detection performance, and significantly better system detection and protection performance. The results provide an experimental basis for the operation and fault detection of intelligent dispatching energy storage system.

Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities, and researchers against illegal access and fraudulent usage. In this article, a secure medical image encryption algorithm, EiMOL , based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed EiMOL encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.

Fusing the single modality medical images to obtain a distinct multimodality image is needed to ensure a better clinical experience. However, the distribution of these fused images brings the issues of ownership and authentication and has attracted many researchers. Presently, large volumes of medical data are stored on cloud platforms. However, outsourcing medical data to this popular platform may introduce security issues. Following this, we introduce a secure data hiding in fused medical image for smart healthcare, since it is also suitable for applications in the cloud. To achieve this, we first create a fused medical image as a cover by nonsubsampled contourlet transform (NSCT). The method, which is based on NSCT, QR, and Schur decomposition, allows concealing the image and electronic patient records (EPR) mark into the fused image. Importantly, EPR watermark includes a hash value of cover is created first and then embedded into the cover via magic cube-based procedure. Finally, the marked image is encrypted using deoxyribonucleic acid (DNA), chaotic maps, and a hash function-based encryption scheme. The introduced watermarking scheme has been evaluated using 25 pairs of medical images and several embedding/extracting parameters. Apart from being satisfactorily imperceptible, the proposed work is also robust and secure against well-known signal processing attacks and promising results are obtained when compared with similar techniques. It indicates a considerable improvement in robustness of 66.7% and 99.7% over existing discrete wavelet transform (DWT)–singular value decomposition (SVD)-based watermarking schemes.

Nowadays, there is an increasing tendency to fuse multi-modal medical image due to the high diagnostic accuracy and assessment. However, the distribution of these images and more generally, health records may bring serious copy-violation issues in healthcare applications. Further, a massive amount of electronic health records is uploaded on the cloud due to the low cost and high usability. However, outsourcing these records to the cloud may greatly increase security and privacy issues. The present article develops medical image fusion-based watermarking scheme in dual tree complex wavelet transform (DTCWT)-singular value decomposition (SVD) domain. Here, computed tomography (CT) and magnetic resonance imaging (MRI) images are partitioned into a sequence of high frequencies and low frequency by non-subsampled shearlet transform (NSST) to obtain a fused image as a carrier. To enhance the authenticity, spatial and transform domain-based embedding is conducted to conceal multi-marks into carrier image. Finally, improved encryption scheme is employed to obtain a secure marked carrier image. Objective and subjective evaluations indicate a strong robustness effect (43.64%) of the proposed algorithm compared to other methods with remarkable invisibility, payload, and security. To the best of our knowledge, studies of image fusion-based dual watermarking and encryption have not yet been developed in combination with spatial and transform domain schemes for copy protection and ownership of health records, since it is also suitable for applications in the cloud.

The authentication and content protection of multimedia data is a challenging task in the present scenario. One solution is to generate the perceptual hash which essentially authenticates the multimedia data and can also be dealt with image database search problems. In this article, a novel system for generating an image hash is presented. The proposed system utilizes the global and local features in the hash generation process. The local features are obtained from non-linear scale-space based KAZE features. These KAZE features have the ability to capture the most stable point under several content preserving distortions. In contrast, first the input image is converted to a normalized image, it is transformed to the log-polar coordinate system, and then a reference image using the local contrast in the wavelet domain is obtained to extrcat the global features. An intermediate hash sequence is achieved by extracting the significant information from the reference image using the singular value decomposition. The final hash sequence combines both the vectors followed by the randomization process. Extensive experiments are conducted to demonstrate the feasibility and the robustness of the proposed hashing system against a wide range of intentional/unintentional distortions. Further, the comparative analysis with some state-of-the-art techniques validates the better discrimination of the proposed work.

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.

Since smart healthcare systems are highly connected to advanced wearable devices, internet of things (IoT) and mobile internet, valuable patient information and other significant medical records are easily transmitted over the public network. The patient information and clinical records are also stored on the existing databases and local servers of hospitals and healthcare centres. These materials not only provide a reference for healthcare professionals to make correct decisions on the patients, but also provide a strong basis for other professionals to undertake effective treatment and develop plans for correct diagnosis. Furthermore, the databases may be used by various research communities for research, without any possibility of privacy violations. However, leaking of healthcare data is highly likely. Therefore, medical data security is becoming very important in smart healthcare.

Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this article, we proposeDRLTrack, a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. InDRLTrack, a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks.DRLTrackis said to betrustworthyas it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance ofDRLTrackconsidering the objectives through simulations and it demonstrates superior performance compared with existing work.

The analysis of molecular cavities, where ligands interact with protein structures, plays a critical role in protein structure-based drug design. However, it is a challenge because of the ambiguous definition of the cavity boundaries in most cavity detection methods. The cavities are mostly calculated by input parameters, which are difficult for users to visualize cavities in interactive ways. In this paper, we propose a novel method for the interactive exploration of cavity calculation and visualization. Firstly, the proposed method combines the two solvent-excluded surfaces (SES) models of a given protein to define the boundaries and provides cavity emission points. Secondly, the system provides a user-guided interactive method to allow users to select cavities by simply clicking operations and to track the cavity identify and filling process based on position constraints. Finally, the selected cavities are represented with the colorful depth perception method. Experiments show that our work can effectively identify and calculate cavities.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

The papers in this special section focus on advanced machine learning algorithms for biomedical data and image processing. Researchers in machine learning including those working in computer vision, image processing, biomedical analysis, and related fields when tied with experienced clinicians can play a significant role in understanding and working on complex medical data which ultimately improves patient care. Developing a novel machine-learning algorithm specific to medical data is a challenge and need of the hour. Healthcare and biomedical sciences have become data-intensive fields, with a strong need for sophisticated data mining methods to extract the knowledge from the available information. Biomedical data contains several challenges in data analysis, including high dimensionality, class imbalance, and low numbers of samples. Although the current research in this field has shown promising results, several research issues need to be explored as follows. There is a need to explore novel feature selection methods to improve predictive performance along with interpretation and to explore large-scale data in biomedical sciences.

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability.

Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

Deep learning-based methods have been popular in multimedia analysis tasks, including classification, detection, segmentation, and so on. In addition to conventional applications, this model can be widely used for cover communication, i.e., information hiding. This article presents a review of deep learning-based covert communication scheme for protecting digital contents, devices, and models. In particular, we discuss the background knowledge, current applications, and constraints of existing deep learning-based information hiding schemes, identify recent challenges, and highlight possible research directions. Further, major role of deep learning in the area of information hiding are highlighted. Then, the contribution of surveyed scheme is also summarized and compared in the context of estimation of design objectives, approaches, evaluation metric, and weaknesses. We believe that this survey can pave the way to new research in this crucial field of information hiding in deep-learning environment.

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this article, we construct an new image attribute dataset called aesthetic mixed dataset with attributes (AMD-A) and design external attribute features for fusion. Besides, we propose an efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring, and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment.

As digital images are consistently generated and transmitted online, the unauthorized utilization of these images is an increasing concern that has a significant impact on both security and privacy issues; additionally, the representation of digital images requires a large amount of data. In recent years, an image compression scheme has been widely considered; such a scheme saves on hardware storage space and lowers both the transmission time and bandwidth demand for various potential applications. In this article, we review the various approaches taken to consider joint encryption and compression, assessing both their merits and their limitations. In addition to the survey, we also briefly introduce the most interesting and most often utilized applications of image encryption and evaluation metrics, providing an overview of the various kinds of image encryption schemes available. The contribution made by these approaches is then summarized and compared, offering a consideration of the different technical perspectives. Lastly, we highlight the recent challenges and some potential research directions that could fill the gaps in these domains for both researchers and developers.

Summary This paper discusses a lightweight encryption–based secure digital watermarking technique for medical applications. The technique uses redundant discrete wavelet transform (RDWT) and singular value decomposition (SVD) along with nonsubsampled contourlet transform (NSCT) to improve robustness and imperceptibility. The security of the proposed technique is further improved by incorporating a lightweight (low‐complexity) cryptographic mechanism that is applied after embedding multiple watermarks. The proposed scheme first partitions the host image into subcomponents and then calculates the entropy values for it. To the maximum entropy value, the NSCT is applied, followed by RDWT decomposition. Finally, SVD is applied to obtain a singular vector of RDWT‐decomposed components. The watermark images are also processed using the same procedure mentioned above. The method uses singular values to hide watermarks into a host image. The experimental outcome shows that the combined technique makes our proposed approach more robust and imperceptible, while it is evaluated for various wavelet filters and 10 different types of medical and five different types of nonmedical cover images. Furthermore, the strength of the cryptographic mechanism is tested using standard performance measures and confirms its effectiveness in security. Moreover, it is evident from the results that our method shows improvement in robustness in comparison to previously reported techniques under consideration.

With the development of cloud computing, the storage and processing of massive visual media data has gradually transferred to the cloud server. For example, if the intelligent video monitoring system cannot process a large amount of data locally, the data will be uploaded to the cloud. Therefore, how to process data in the cloud without exposing the original data has become an important research topic. We propose a single-server version of somewhat homomorphic encryption cryptosystem based on confused modulo projection theorem named CMP-SWHE, which allows the server to complete blind data processing withoutseeingthe effective information of user data. On the client side, the original data is encrypted by amplification, randomization, and setting confusing redundancy. Operating on the encrypted data on the server side is equivalent to operating on the original data. As an extension, we designed and implemented a blind computing scheme of accelerated version based on batch processing technology to improve efficiency. To make this algorithm easy to use, we also designed and implemented an efficient general blind computing library based on CMP-SWHE. We have applied this library to foreground extraction, optical flow tracking, and object detection with satisfactory results, which are helpful for building smart cities. We also discuss how to extend the algorithm to deep learning applications. Compared with other homomorphic encryption cryptosystems and libraries, the results show that our method has obvious advantages in computing efficiency. Although our algorithm has some tiny errors ($10^{-6}$) when the data is too large, it is very efficient and practical, especially suitable for blind image and video processing.

Recent advances in artificial intelligence (AI), especially convolutional neural networks (CNNs), alongside the digitization of histopathological images, have made the computer-aided diagnosis of breast cancer a reality. However, deep learning-based approaches are computationally expensive and have huge parameters, which makes them less affordable for edge devices. In order to make them affordable for edge devices, the whole classification model needs to be compressed while maintaining accuracy. Providing a low-cost solution for histopathological diagnosis in the recent edge-computing world is of utmost importance. Therefore, in this study, we propose “MobiHisNet,” an efficient and lightweight CNN model for histopathological image classification (HIC) based on MobileNet. MobiHisNet was successfully deployed on a Raspberry Pi, as well as three mobile devices, demonstrating its ability to run on a lightweight and portable processor. Our studies indicated that a depth parameter ($\gamma = 0.5$) and a 16-bit quantization are the optimum parameters for the proposed model while balancing the accuracy, inference time, and memory peak requirements. Compared to the state-of-the-art, pretrained models, MobiHisNet has fewer parameters and calculations, resulting in faster image classification. This renders it more viable for production purposes and applications on edge devices. In addition, MobiHisNet is computationally faster than VGG16, ResNet50, Xception, and InceptionV3 by twenty-seven, eight, six, and five times, respectively. This also outperforms all the baseline models with the moderate model size and FLOP counts. Experiments on breast cancer HIC (BreakHis) data sets show superior performance of MobiHisNet on edge devices in terms of higher accuracy, lesser complexity, and lesser memory requirements. Thus, it has a high potential for deployment in mobile edge devices.

The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data more effectively and provide high-efficiency, low-energy, and wide-coverage technical services for terminals. The channel model and energy loss model analyze the devices' access performance, data transmission path delay, energy consumption in the IoT, and large-scale devices' access in the cellular narrowband IoT (NB-IoT) based on big data analysis technology are also discussed. The results show that in the access success rate analysis, the access success rate is the highest with an access time ( T) of 5 s and a preamble resource number ( K) of 25. The restriction factor is inversely proportional to the access success rate. In the node utilization analysis, different transmission node priorities result in different node utilization, and priority 2's node utilization is better than that of priority 1. Moreover, local data makes data analysis and transmission faster. The search time is prolonged, and the corresponding energy consumption is also higher without local data. In the energy consumption analysis, with the 6-generation (6G) technology, different interference thresholds lead to the different energy efficiency of data transmission. The larger the interference threshold, the higher the energy efficiency. Therefore, the 6G-based big data analysis technology can significantly improve large-scale IoT devices' access success rate and enable the system to meet the requirements of low energy consumption and high access success rate, significant for research on more devices' access data analysis.

The Internet of Things (IoT) is one of the fastest growing areas of research. Considering the IoT and healthcare simultaneously, classifying brain signals using smart IoT sensors is one of the standing nontrivial problems of literature. The issue is further exacerbated by noise in brain signals, and there is no efficient solution for classifying brain signals as seizorous or nonseizorous, yet. Moreover, research has mostly ignored the security and privacy aspect of this problem. Therefore, in this article, we try to bridge this gap and present a secure privacy-preserving technique for brain signal classification. We first transform a brain signal into an image. Subsequently, we apply transfer learning to solve the classification problem. To do that, we use the pretrained VGG-19 as a base model. In addition, we discuss a scheme to store images in a blockchain so as to make the overall architecture privacy aware. By conducting comprehensive numerical simulations on a supercomputer and using the famous TUH Abnormal EEG data set, we show the efficacy of the proposed work. The work presented here not only makes the storage of patient data secure and private but also outperforms all existing techniques in terms of classification accuracy.

Accurate breast cancer detection using automated algorithms remains a problem within the literature. Although a plethora of work has tried to address this issue, an exact solution is yet to be found. This problem is further exacerbated by the fact that most of the existing datasets are imbalanced, i.e., the number of instances of a particular class far exceeds that of the others. In this paper, we propose a framework based on the notion of transfer learning to address this issue and focus our efforts on histopathological and imbalanced image classification. We use the popular VGG-19 as the base model and complement it with several state-of-the-art techniques to improve the overall performance of the system. With the ImageNet dataset taken as the source domain, we apply the learned knowledge in the target domain consisting of histopathological images. With experimentation performed on a large-scale dataset consisting of 277,524 images, we show that the framework proposed in this paper gives superior performance than those available in the existing literature. Through numerical simulations conducted on a supercomputer, we also present guidelines for work in transfer learning and imbalanced image classification.

With the increasing size of data, massive amounts of data are being generated continuously. It is hoped to find a cognitive computing technology that can effectively learn and process large-scale data. The decision tree algorithm is optimized from the perspective of machine learning. A cognitive computing model based on context-aware data flow is constructed. Classification and regression tree (CART) algorithm is used in the data computing layer of the cognitive model. In addition, the clustering effectiveness index based on frequent patterns optimizes the K-means clustering method. The performance of the algorithm is analyzed through simulation experiments. The results show that the CART algorithm requires fewer training data sets while guaranteeing classification accuracy. Also, the algorithm has obvious advantages under large-scale data. In the application of actual data set, on Over, Over+Noise, and Bridge, only the clustering validity index based on frequent pattern (FPCVI) index proposed finds the correct number of clusters. The application of DPCK-K-means clustering algorithm can ensure the accuracy and stability of behavior classification, which is greatly significant for operators to analyze user behavior and develop personalized services.

Machine learning (ML) has emerged as a powerful tool for pattern recognition. Traditional ML algorithms have limited ability to reveal the most sophisticated features of cancer histopathological images, but their robustness and fault tolerance can be enhanced by using fuzzy modeling to capture the uncertainty in image data. Therefore, this article proposes a novel CoMHisP framework based on a fuzzy support vector machine with within-class density information (FSVM-WD). It utilizes a novel feature extraction technique by optimizing the block size to extract image micropatterns and computing center of mass (CoM) for each pixel to extract feature vectors. The performance of the proposed framework is evaluated using a CMTHis dataset comprising histopathological images of canine mammary tumor (CMT), a prevalent neoplastic disease in female dogs, and an established model for human breast cancer. Data analysis reveals that stain normalization and magnification influence the performance of the CoMHisP framework, with the best results achieved at lower magnifications after stain normalization. The proposed framework achieves a classification accuracy of 97.25% ($\pm$1.80%) using a FSVM-WD classifier, outperforming both traditional ML and deep FE-VGGNET16-based feature descriptors. To the best of our knowledge, this is the first time a CoM-based feature descriptor has been proposed for histopathological image analysis of CMTs and its performance was evaluated using a fuzzy SVM-based classifier. The proposed method performs well with datasets of limited size and low-magnification images and, therefore, has the potential to provide rapid and accurate diagnosis in low-cost clinical settings.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

Intelligent control of traffic has significant influence on the scheduling efficiency of urban traffic flow. Therefore, in order to improve the efficiency of vehicles at intersections, first, the Back Propagation (BP) neural network is used to propose a vehicle passing model at the intersection, and based on the intelligent traffic control system model, the Earliest Deadline First (EDF) dynamic scheduling algorithm is used to improve the Controller Area Network (CAN) communication network. Finally, the simulation test is used to evaluate the effectiveness of the proposed model and the improved CAN bus communication network. The results show that the neural network model can be used to predict the passage time of vehicles queuing at intersections with an error of less than 10%. The improved CAN bus communication can improve the data transmission rate, and the success rate of data transmission under different load rates is above 95%. In conclusion, the application of artificial intelligence technology in intelligent traffic system can improve the efficiency of vehicle scheduling and the efficiency of communication system. This research is of great significance to improve the communication performance of the transportation system and scheduling efficiency.

With the construction and improvement of 5G infrastructure, more devices choose to access the Internet to achieve some functions. People are paying more attention to information security in the use of network devices. This makes lightweight block ciphers become a hotspot. A lightweight block cipher with superior performance can ensure the security of information while reducing the consumption of device resources. Traditional optimization tools, such as brute force or random search, are often used to solve the design of Symmetric-Key primitives. The metaheuristic algorithm was first used to solve the design of Symmetric-Key primitives of SKINNY. The genetic algorithm and the simulated annealing algorithm are used to increase the number of active S-boxes in SKINNY, thus improving the security of SKINNY. Based on this, to improve search efficiency and optimize search results, we design a novel metaheuristic algorithm, named particle swarm-like normal optimization algorithm (PSNO) to design the Symmetric-Key primitives of SKINNY. With our algorithm, one or better algorithm components can be obtained more quickly. The results in the experiments show that our search results are better than those of the genetic algorithm and the simulated annealing algorithm. The search efficiency is significantly improved. The algorithm we proposed can be generalized to the design of Symmetric-Key primitives of other lightweight block ciphers with clear evaluation indicators, where the corresponding indicators can be used as the objective functions.

In order to calculate the node big data contained in complex networks and realize the efficient calculation of complex networks, based on voluntary computing, taking ICE middleware as the communication medium, the loose coupling distributed framework DCBV based on voluntary computing is proposed. Then, the Master, Worker, and MiddleWare layers in the framework, and the development structure of a DCBV framework are designed. The task allocation and recovery strategy, message passing and communication mode, and fault tolerance processing are discussed. Finally, to calculate and verify parameters such as the average shortest path of the framework and shorten calculation time, an improved accurate shortest path algorithm, the N-SPFA algorithm, is proposed. Under different datasets, the node calculation and performance of the N-SPFA algorithm are explored. The algorithm is compared with four approximate shortest-path algorithms: Combined Link and Attribute (CLA), Lexicographic Breadth First Search (LBFS), Approximate algorithm of shortest path length based on center distance of area division (CDZ), and Hub Vertex of area and Core Expressway (HEA-CE). The results show that when the number of CPU threads is 4, the computation time of the DCBV framework is the shortest (514.63 ms). As the number of CPU cores increases, the overall computation time of the framework decreases gradually. For every 2 additional CPU cores, the number of tasks increases by 1. When the number of Worker nodes is 8 and the number of nodes is 1, the computation time of the framework is the shortest (210,979 ms), and the IO statistics data increase with the increase of Worker nodes. When the datasets are Undirected01 and Undirected02, the computation time of the N-SPFA algorithm is the shortest, which is 4520 ms and 7324 ms, respectively. However, the calculation time in the ca-condmat_undirected dataset is 175,292 ms, and the performance is slightly worse. Overall, however, the performance of the N-SPFA and SPFA algorithms is good. Therefore, the two algorithms are combined. For networks with less complexity, the computational scale coefficient of the SPFA algorithm can be set to 0.06, and for general networks, 0.2. When compared with other algorithms in different datasets, the pretreatment time, average query time, and overall query time of N-SPFA algorithm are the shortest, being 49.67 ms, 5.12 ms, and 94,720 ms, respectively. The accuracy (1.0087) and error rate (0.024) are also the best. In conclusion, voluntary computing can be applied to the processing of big data, which has a good reference significance for the distributed analysis of large-scale complex networks.

Smart cities fully utilize the new generation of Internet of Things (IoT) technology in the process of urban informatization to optimize the urban management and service. However, in the IoT system, while information exchange and communication, wireless sensor network devices may not be able to resist all forms of attacks, which may lead to security issues such as user data disclosure. Aiming at the information security risks in smart city, the typical technologies in IoT is analyzed from the perspective of IoT perception layer and provides corresponding security solutions for the existing security threats. Regarding the communication security, the emerging wireless technology, long range (LoRa), is discussed, and the performance of wireless communication protocol is analyzed through simulation experiments, to verify that the IoT technology based on LoRa communication technology can improve the security of the system in the construction of smart city. The results show that REBEB, a new backoff algorithm, is similar to the binary exponential backoff algorithm in terms of throughput performance. REBEB focuses more on fairness, which is up to 0.985, and to a certain extent, its security is significantly improved. The fairness of REBEB algorithm is more than 0.4 in different nodes and competing windows, and the fairness of the system is better when the number of nodes is small. To sum up, the IoT system based on LoRa communication can effectively improve the security performance of the system in the construction of smart city and avoid the security threats in the IoT signal transmission.

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated by simulation experiments. The results show that, in the energy efficiency analysis, with the increase of power split factor ρ, the information transmission ability of the system increases. Whereas, the energy collection ability decreases, so the energy efficiency is reduced. Thus, choosing a more suitable power split factor for the energy efficiency of IoT is important. By analyzing OP and bit error rate (BER), as the values of m (Nakagami, the fading index of the fading distribution) and multi-hop paths increase, the OP and BER are reduced while the system performance is increased. Therefore, this article uses wireless relay cooperative transmission technology to integrate big data analysis into the IoT system. Finally, by adding multi-hop path and other methods to reduce the OP and BER of system, the system performance is improved. It provides experimental basis for the development of IoT systems.

Recently, due to the increase in popularity of the Internet, the problem of digital data security over the Internet is increasing at a phenomenal rate. Watermarking is used for various notable applications to secure digital data from unauthorized individuals. To achieve this, in this article, we propose a joint encryption then-compression based watermarking technique for digital document security. This technique offers a tool for confidentiality, copyright protection, and strong compression performance of the system. The proposed method involves three major steps as follows: (1) embedding of multiple watermarks through non-sub-sampled contourlet transform, redundant discrete wavelet transform, and singular value decomposition; (2) encryption and compression via SHA-256 and Lempel Ziv Welch (LZW), respectively; and (3) extraction/recovery of multiple watermarks from the possibly distorted cover image. The performance estimations are carried out on various images at different attacks, and the efficiency of the system is determined in terms of peak signal-to-noise ratio (PSNR) and normalized correlation (NC), structural similarity index measure (SSIM), number of changing pixel rate (NPCR), unified averaged changed intensity (UACI), and compression ratio (CR). Furthermore, the comparative analysis of the proposed system with similar schemes indicates its superiority to them.

introduction Share on Introduction to the Special Section on Security and Privacy of Medical Data for Smart Healthcare Editors: Amit Kumar Singh National Institute of Technology Patna, India National Institute of Technology Patna, IndiaSearch about this author , Jonathan Wu University of Windsor, Canada University of Windsor, CanadaSearch about this author , Ali Al-Haj Princess Sumaya University for Technology, Jordan Princess Sumaya University for Technology, JordanSearch about this author , Calton Pu Georgia Institute of Technology, USA Georgia Institute of Technology, USASearch about this author Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 3August 2021 Article No.: 53pp 1–4https://doi.org/10.1145/3460870Online:09 June 2021Publication History 3citation88DownloadsMetricsTotal Citations3Total Downloads88Last 12 Months88Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

With the remarkable development of internet technologies, the popularity of smart healthcare has regularly come to the fore. Smart healthcare uses advanced technologies to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. Unfortunately, medical data security is a serious issue in the smart healthcare systems. It becomes a fundamental challenge that requires the development of efficient innovative strategies towards fulfilling the healthcare needs and supporting secure healthcare transfer and delivery. This article provides a comprehensive survey on state-of-the-art techniques for health data security and their new trends for solving challenges in real-world applications. We survey the various notable cryptography, biometrics, watermarking, and blockchain-based security techniques for healthcare applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their objective, methodology, type of medical data, important features, and limitations. At the end, we discuss the open issues and research directions to explore the promising areas for future research.

introduction Share on Introduction to the Special Issue on Recent Trends in Medical Data Security for e-Health Applications Editors: Amit Kumar Singh Search about this author , Zhihan Lv Search about this author , Hoon Ko Search about this author Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 17Issue 2sJune 2021 Article No.: 58pp 1–3https://doi.org/10.1145/3459601Published:18 May 2021Publication History 6citation187DownloadsMetricsTotal Citations6Total Downloads187Last 12 Months106Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build a multi-level fine-grained image feature categorization model. Finally, the TensorFlow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.

Summary Image watermarking in wavelet domain is a challenging problem as it includes a proper selection of wavelet transforms as well as sub‐bands for accurate embedding and recovery of watermarks. However, selection of a suitable wavelet transform for multiple image watermarking is an area of great interest. Therefore, in this work, a multiple watermarking technique using combination of redundant discrete wavelet transforms (RDWT), non‐subsampled contourlet transform (NSCT), set partitioning in hierarchical tree (SPIHT), and singular value decomposition (SVD) is proposed. The proposed scheme is aimed to achieve high capacity, robustness, and imperceptibility. The security aspect of the proposed method is enhanced by using Arnold transform. Since RDWT and NSCT are both shift invariant in nature, therefore they are suitable for multiple image watermarking. Furthermore, the proposed method provides flexibility in selection of appropriate sub‐bands for watermark embedding and recovery. Experimental results and analysis reveals that the proposed technique gives maximum PSNR, NC, and SSIM value up to 40.97 dB, 1, and 0.9994, respectively. Furthermore, the performance estimation of our technique is found superior to previously reported techniques under consideration.

With the widespread growth of digital information and improved internet technologies, the demand for improved information security techniques has significantly increased due to privacy leakage, identity theft, illegal copying, and data distribution. Because of this, data hiding approaches have received much attention in several application areas. However, those approaches are unable to solve many issues that are necessary to measure in future investigations. This survey provides a comprehensive survey on data hiding techniques and their new trends for solving new challenges in real-world applications. The notable applications are telemedicine, 3D objects, mobile devices, cloud/distributed computing and data mining environments, chip and hardware protection, cyber physical systems, internet traffic, fusion of watermarking and encryption, joint compression and watermarking, biometric watermarking, watermarking at the physical layer, and many other perspectives. Further, the potential issues that existing approaches of data hiding face are identified. I believe that this survey will provide a valuable source of information for finding research directions for fledgling researchers and developers.

Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. The proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum‐rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance‐based face recognition algorithms has been done at each level.

In this paper, an algorithm for digital watermarking based on discrete wavelet transforms (DWTs) and singular value decomposition (SVD) has been proposed. In the embedding process, the host colour image is decomposed into third-level DWT. Low frequency band (LL3) is transformed by SVD. The watermark image is also transformed by SVD. The S vector of watermark information is embedded in the S component of the host image. Watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DWT. Watermark is extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermark, back propagation neural network (BPNN) is applied to the extracted watermark to reduce the effects of different noise applied on the watermarked image. Results are obtained by varying the gain factor and size of the cover and watermark image, experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks and has been found to be giving superior performance for robustness and imperceptibility compared to existing methods suggested by other authors.