editorial reviewed

The deep vision sensing has been a practical tool in early disease detection, and this work aims at an important branch of ocular disease recognition. Although a number of researchers had paid attention to it during past years, fine-grained ocular feature extraction always remains a challenge. To handle with this issue, this work benefits from comprehensive ability of the convolution-Transformer structure (Conformer), and proposes vision sensing-driven intelligent ocular disease detection using conformer-based dual fusion. On the one hand, the proposal combines technical advantages of convolution and visual Transformer to more accurately fuse local subtle features and global representation information in images. On the other hand, the proposal significantly improves accuracy and robustness of the model by optimizing depth and width. Simulation experiments on real-world ocular disease image datasets show that the proposed model exhibits higher performance in ocular disease detection compared to other methods. Numerical results show that it improves the detection accuracy by 1% to 3.7% compared to several mainstream baseline methods. This research result not only promotes the development of ocular disease detection, but also provides more reliable technical support for accurate diagnosis of ophthalmic diseases.

Modern transportation networks, with their complexity and dynamic nature, have a substantial demand for intelligent vehicles. Developing effective production strategies for smart vehicles is essential to reducing both production costs and energy consumption. Traditional vehicle production planning has largely depended on heuristic algorithms and solvers, which lack scalability and are susceptible to local optima. Furthermore, existing solutions do not concurrently address both dynamic and regular vehicle production planning. To overcome these limitations, this paper proposes an effective optimizing method for large-scale smart manufacturing within intelligent transportation networks using Federated Reinforcement Learning. In our proposal, the Gated Recurrent Unit and Asynchronous Advantage Actor Critic (A3C) reinforcement algorithms are employed to develop a Dynamic Optimizing Planning Module(DOPM), which can output an excellent solution of 1000 vehicles within 5 seconds. A High-Quality Processing Module(HQPM) is constructed by the Transformer with A3C, significantly enhancing the production plan’s quality. Finally, the proposed methods will integrate with Federated Learning (FL) to establish a scalable, privacy-preserving intelligent manufacturing scheduling framework for transportation networks. Experimental results demonstrate that our work significantly outperforms traditional solutions, achieving over a 93% improvement in solving speed and reducing constraint violations by more than 95%.

Mobile edge computing (MEC) has gained significant attention for enhancing computational capacity and resource efficiency in wireless networks, particularly in Internet of Things (IoT) ecosystem where massive device connectivity is critical. However, the limited computational and energy resources of mobile users, such as IoT sensors and actuators, remain key barriers to further improving system performance. To address this, we propose an energy harvesting MEC system integrated with reconfigurable intelligent surfaces (RIS), which optimizes phase shifts and enhances channel state information to improve channel quality and enable efficient task offloading to edge servers for real-time IoT data processing. This study introduces an integrated deep reinforcement learning-based optimization (IDBO) framework to maximize the system computation rate. By decomposing the joint optimization problem into two modules — RIS phase shift configuration and task offloading strategy, and incorporating a unified reward mechanism, the framework achieves collaborative optimization between signal control and task allocation. Simulation results present that the proposed IDBO algorithm improves the system computation rate by 8% compared to baseline methods, demonstrating superior global optimization capabilities and strong generalization performance across diverse IoT network scenarios.

The Internet of Vehicles (IoV) is playing a pivotal role in advancing intelligent transportation systems. Deploying the drone as edge nodes in IoV networks has emerged as a promising solution to enhance the communication coverage and energy efficiency (EE). However, the existing drone deployment and resource allocation strategies often lack the necessary intelligence and adaptability to respond to the dynamic traffic conditions. To address these challenges, we leverage machine learning (ML) technology to optimize EE by jointly optimizing small base station (SBS) dormancy and drones’ 3-D positioning—a problem recognized as NP-hard. To tackle this problem, we propose an energy-efficient multi-drone 3-D deployment with SBS dormancy (MUD-SBSD) algorithm, which decomposes the problem into two manageable phased issues. First, a dormant strategy based on the base station centrality (BSC) metric is developed to switch SBSs to a dormant state during low-traffic periods. Second, the horizontal positions of drones are optimized using the k-means algorithm, followed by determining the optimal drone heights via the genetic algorithm (GA). Extensive simulations validate the proposed algorithm can achieve a 41% improvement in EE and a 15% increase in communication coverage rate compared to the existing strategies. These results not only highlight the effectiveness of the proposed solution but also underscore its relevance in enhancing the performance and sustainability of IoV networks, paving the way for more intelligent and responsive transportation systems.

The next generation of industrial internet of things (IoT) dominated by unmanned aerial vehicle (UAV) relies on the coordinated operation of heterogeneous UAV-mounted transceiver cluster (HUTC) in constrained environments. However, the electromagnetic resources available to these transceivers deployed in crowded spaces are limited, and the resulting spectrum conflicts can easily lead to difficulties in aerial sensing, computing, and networking. Beyond interference from external sources, spectrum allocation in dense spaces is further complicated by interference from frequency-domain neighbors, making efficient resource allocation challenging. Thus, this article utilize the electromagnetic interference (EMI) characteristics of heterogeneous transceivers as prior knowledge and proposes an innovative joint spectrum and power allocation based on better response (JSPA-BR) method to tackle the EMI problem in HUTC composed of heterogeneous transceivers. More specifically, we construct a game-theoretic model for joint spectrum and power allocation, and it is proved that the model constitutes an exact potential game (EPG) with at least one Nash equilibrium (NE) point. We then design the JSPA-BR algorithm which can converge quickly and approach the global optimal solution. Simulations and measurements show that this method maximizes the use of limited spectrum resources. It also mitigates EMI between transceivers and the external radiation of the system. It achieves electromagnetic compatibility of HUTC, thereby demonstrating the effectiveness and accuracy of the proposed approach.

The Industrial Internet of Things coupled complex networks (IIoT-CCNs) contain a large and diverse number of nodes, whose states are inherently random and complex. In this context, federated learning can play a significant role. It allows different nodes or subsets of the IIoT-CCNs to train local models without the need to transfer all their raw data to a central server. This paper firstly establishes a more general IIoT-CCNs model, where the communication channel may not be completely opened between coupled nodes. When integrating federated learning, the nodes can collaboratively train a global model while safeguarding their own data sovereignty. Secondly, a pinning impulsive controller is designed to make IIoT-CCNs realize synchronization. Thirdly, by utilizing regroup method and step-function methods, some useful and novel synchronization stability conditions have been obtained. In the federated learning framework, these stability conditions can be adapted to ensure the convergence of the collaborative learning process. Then, a simulation is conducted to make sure the correctness of results. Finally, the application of synchronization with regard to responsible image communication is executed. The feature values of node data can be extracted through the image encryption algorithm, and then using synchronized chaotic sequences obtained from encryption to ensure the security of image information in the transmission process. Even in a federated learning-enabled IIoT-CCNs, the encrypted data and synchronized sequences can be managed in a way that respects the privacy and security requirements. It also ensures that legitimate recipients can simultaneously obtain the correct keys according to the image encryption algorithm. By utilizing the histogram and adjacent pixel correlation analysis to verify the effectiveness of the encryption scheme.

The distributed network utilizing the integrity-based resilient machine learning (DRML) approach offers a robust methodology to address challenges associated with complex Internet of Things (IoT) applications, computational resource limitations, and environmental sustainability. In heterogeneous cloud computing environments, where edge and central clouds collaborate to meet diverse demands in various sectors, significant challenges arise in task offloading. This research proposes an IoT and cloud computing framework integrated with a distributed and resilient deep learning (DLRTO) model to optimize system utility and bandwidth allocation for local controller devices (LCDs). The DLRTO model is designed to generate near-optimal offloading decisions for LCDs, edge cloud servers, and central cloud servers, thus maximizing both system utility and bandwidth allocation. Additionally, we introduce the RL-NN integrated round robin-based dynamic task scheduling (RNRRDTS) algorithm on edge and cloud servers to address job scheduling issues, with a focus on renewable energy generation and job migration. Extensive simulations were conducted using real-world IoT application tasks, renewable energy data, and grid electricity pricing data to evaluate the proposed model. The experimental results demonstrate the superior performance of the DLRTO model compared with existing methods. This research provides valuable insights and advances toward the creation of sustainable, resilient, and optimized IoT systems applicable across various domains.

Amid a rapidly evolving information era, large-scale multimodal knowledge discovery in computational social systems emerges as a key research domain. Large language models (LLMs) play a crucial role in this field, providing contextual understanding and task adaptability. Yet, centralized training of LLM raises privacy concerns. Federated learning (FL) offers a distributed alternative, but it struggles with data heterogeneity and security issues related to model parameters. To this end, we propose a robust aggregation method that leverages the relative total distance of models to improve global model performance in heterogeneous settings, complemented by Cheon-Kim-Kim-Song (CKKS) encryption to secure parameters against parameter stealing without performance loss. Extensive numeric results show our approach excels in LLM testing, scoring 3.74 on MTBenchmark and 8.17 on Vicuna, outperforming state-of-the-art FL methods against data heterogeneity challenges. It also achieves consistent gains on image datasets such as SVHN, CIFAR10, MNIST, TinyImageNet200, and CIFAR100, TinyImageNet200. In summary, our method offers an effective solution for secure multimodal data analysis in computational social systems.

This paper introduces an innovative approach for automated polyp segmentation in colonoscopy images, deploying an enhanced Pix2Pix Generative Adversarial Network (GAN) equipped with an integrated attention mechanism in the discriminator. Addressing prevalent challenges in conventional segmentation methods, such as variable polyp appearances, inconsistent image quality, and limited training data, our model significantly augments the precision and reliability of polyp segmentation. The integration of an attention mechanism enables our model to meticulously focus on the intricate features of polyps, improving segmentation accuracy. A unique training strategy, employing both real and synthetic data, is adopted to ensure the model's robust performance under a variety of conditions. The results, validated through rigorous tests on multiple public colonoscopy datasets, indicate a notable improvement in segmentation performance over existing state-of-the-art methods. Our model's enhanced ability to detect critical details early plays a pivotal role in proactive colorectal cancer detection, a key aspect of smart healthcare systems. This work represents an effective amalgamation of advanced AI techniques and the Internet of Medical Things (IoMT), signifying a noteworthy contribution to the evolution of smart healthcare systems. In conclusion, our attention-enhanced Pix2Pix GAN not only offers efficient and reliable polyp segmentation, but also showcases considerable potential for seamless integration into remote health monitoring systems, underlining the increasing relevance and efficacy of AI in advancing IoMT-enabled healthcare.

In recent years, data-driven remote medical management has received much attention, especially in application of survival time forecasting. By monitoring the physical characteristics indexes of patients, intelligent algorithms can be deployed to implement efficient healthcare management. However, such pure medical data-driven scenes generally lack multimedia information, which brings challenge to analysis tasks. To deal with this issue, this paper introduces the idea of ensemble deep learning to enhance feature representation ability, thus enhancing knowledge discovery in remote healthcare management. Therefore, a multiview deep learning-based efficient medical data management framework for survival time forecasting is proposed in this paper, which is named as "MDL-MDM" for short. Firstly, basic monitoring data for body indexes of patients is encoded, which serves as the data foundation for forecasting tasks. Then, three different neural network models, convolution neural network, graph attention network, and graph convolution network, are selected to build a hybrid computing framework. Their combination can bring a multiview feature learning framework to realize an efficient medical data management framework. In addition, experiments are conducted on a realistic medical dataset about cancer patients in the US. Results show that the proposal can predict survival time with 1% to 2% reduction in prediction error.

As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.

Federated learning (FL) has emerged in sparse low earth orbit (LEO) satellite constellations as a promising architecture for on-board machine learning (ML) model training, aimed at preserving Internet of Things (IoT) data privacy in specialized and sophisticated tasks. However, the user in FL who spends the longest time in a FL round significantly hinders efficiency. Furthermore, intermittent satellite connectivity, rapidly changing network topologies of sparse LEO satellite constellations and a dearth of information including computation capabilities and positions of satellites greatly obstacle the efficient implementation of FL. To address this challenge, we propose a deep reinforcement learning (DRL)-based server selection scheme for FL in sparse LEO satellite constellations. The optimization problem to minimize the overall FL latency is formulated. A Markov decision process (MDP) is subsequently established and the corresponding double Q-learning agent is trained to make sequential FL server selection decisions to figure it out. Simulation results demonstrate that the proposed scheme reduces latency compared to other server selection schemes.

In context of 6G mobile computing, the combination of Industrial Internet of Things (IoT) and machine learning extends intelligent sensing ability to improve industrial operation efficiency. In conventional operation of sewage treatment process (STP), manipulators often made excessive aeration amount in treatment process, in order to reach environmental standard. However, such rough operation mode will bring redundant energy consumption. To deal with this issue, this work employs industrial 6G-IoT environment provide basic data conditions for intelligent sensing scheme. On this basis, an industrial 6G-IoT sensing and machine-learning-supported intelligent sensing framework is established for indicator control strategy in STP. In particular, the amount of dissolved oxygen (DO) is selected as the main control object. Then, given inlet conditions and expected outlet conditions, the support vector regression model is formulated to predict the appropriate DO amount values. The proposed approach is evaluated on data collected from a real-world industrial 6G-IoT-based STP. And it is compared with several typical machine-learning-based prediction methods. Numerical results show that the method proposed is 5% better than the typical methods with a deviation of less than 0.6 and can achieved prediction precision about 80%.

Healthcare systems, empowered by the integration of Artificial Intelligence (AI) and Internet of Things networks, are undergoing significant advancements, ushering in a new era of enhanced treatment experiences and improved quality of life. Edge computing plays a pivotal role as an architectural enabler; however, it also presents numerous energy-related challenges spanning sensors, communication, and edge devices. One of the most formidable challenges is the proliferation of complex communication protocols across various devices, including sensors, reconfigurable intelligent surfaces, smart devices, and edge servers, leading to substantial carbon emissions and energy consumption. To address this challenge, this paper introduces a low-carbon, sustainable edge architecture leveraging AI techniques. Specifically, we develop a deep learning-based radio frequency fingerprint access protocol to facilitate real-time and energy-efficient device access between smart devices and edge gateways. Building upon this foundation, we propose a hybrid quantum-classical optimization algorithm to achieve green data transmission at lower layers for artificial intelligence of things healthcare systems. Simulation results demonstrate that our optimized architecture achieves over 99% identification accuracy using a signal dataset of 50GB obtained from real-world smart devices and practical gateways in a real-world environment, all while maintaining energy-efficient data delivery.

Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, and long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizures. Impact Statement- This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detection is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, and sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summarizing it will give a new prospective to the reader.

Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, the demand for healthcare facilities is high. Yet there is scarcity of medical experts, technicians for providing healthcare to the people affected with some medical problem. This article presents an Internet of Things (IoT) system architecture for health monitoring and how data analytics can be applied in the health sector. IoT is employed to integrate the sensor information, data analytics, machine intelligence and user interface to continuously track and monitor the health condition of the patient. Considering data analytics as the major part, we focused on the implementation of stress classification and forecasted the future values from the recorded data using sensors. Physiological vitals like Pulse, oxygen level percentage (SpO2), temperature, arterial blood pressure along with the patients age, height, weight and movement are considered. Various traditional and ensemble machine learning methods are applied to stress classification data. The experimental results have shown that a hypertuned random forest algorithm has given a better performance with an accuracy of 94.3%. In a view that knowing the future values in prior helps in quick decision making, critical vitals like pulse, oxygen level percentage and blood pressure have been forecasted. The data is trained with ML and neural network models. GRU model has given better performance with lower error rates of 1.76, 0.27, 5.62 RMSE values and 0.845, 0.13, 2.01 MAE values for pulse, SpO2 and blood pressure respectively.

This article addresses the explainable machine learning for data extraction on diverse datasets. In many cases, individual or specific approaches have been developed for feature selection (FS) on a certain dataset, but collecting the diversity dataset and demonstrating it through different FS methods are challenging. Thus, this article proposed multiapproaches for FS with the classification of diverse datasets. The proposed framework is developed using various methods, such as extendable particle swarm optimization (PSO), global and local searching, feature ranking, feature clustering, computational cost-based FS, and multiobjective optimization. We effectively used these methods in our proposed work in a single-setting framework. We focused on three essential computational items in our framework: classification accuracy, selected features, and computational times. Due to the diverse dataset, few methods have been considered challenging during computational evaluation for classification accuracy with test cost. We tried to manage the classification accuracy based on total cost and high accuracy with less cost. The proposed framework is experimented with the above methods and analyzed through comparative results on diversity datasets. For example, when regular parameter values are in the range of 2⁻¹³-2⁻⁶, the evaluation result affects all items, i.e., decreasing during this range; other values do not affect results. We used thresholds ranging from 0.6 to 0.9 for highly correlated feature pairs as per the support vector machine (SVM) method for recursive feature elimination.

Text steganography has received a lot of attention in the application of covert communication. How to ensure desirable capacity and imperceptibility has become a key issue in text steganography. There are two typical approaches, i.e., text-selection-based steganography and text-generation-based steganography. However, the text-selection-based approaches generally have the very low hidden capacity and are not applicable in practical scenarios. Although the text-generation-based approaches can embed secret messages with higher capacity during text generation, they are prone to semantic incoherence and semantic errors when generating long texts. To address the abovementioned issues, this article proposes a novel text steganography based on long readable text generation. It first determines the topic of the stego-text according to the scenarios of the communication parties. Then, the plug and play language model (PPLM) is explored to generate the long readable stego-text conforming to the topic with semantic coherency. A given secret message is hidden during text generation by selecting proper words in an established embeddable candidate word pool (ECWP). Establishing the ECWP prevents the language model (LM) from selecting words with low probability in the text generation, thereby avoiding the generation of low-quality or even grammatically incorrect stego-text. Experimental results show that the proposed approach significantly increases hidden capacity while maintaining good imperceptibility compared with the existing approaches.

The coronavirus disease 2019 (COVID-19) preventive measures have resulted in significant lifestyle changes. One of the COVID-19 new normal is the usage of face masks for protection against airborne aerosol which creates distractions and interruptions in voice communication. It has a different influence on speech than the standard concept of noise affecting speech communication. Furthermore, it has varied effects on speech in different frequency bands. To provide a solution to this problem, a three-stage adaptive speech enhancement (SE) scheme is developed in this article. In the first stage, the tunable$Q$-factor wavelet transform (TQWT) features are extracted by properly setting the quality factor values and the number of levels from the input speech signal. In the second stage, the adjustable parameters of the preemphasis filter and modified multiband spectral subtraction (MBSS) are determined using bio-inspired techniques for different masking and signal-to-noise ratio (SNR) conditions. In the third stage, the weights, center values, standard deviation of the Gaussian radial basis functions, and input patterns of the radial basis function neural networks (RBFNNs) are updated to predict the optimized parameters from the input TQWT-based cepstral features (TQCFs). In the end, the performance of the proposed algorithm is compared with the standard SE algorithms using two speech datasets.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.

Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc –based CPS provides a promising architecture due to its decentralized nature and destructive-resistance. A growing number of information leakage events in CPSs and the following serious consequences have aroused ubiquitous concern about information security. In this article, we combine physical layer security solutions and millimeter-wave (mmWave) techniques to safeguard the ad hoc network and investigate the reliability-security tradeoff by taking user demands for the network into account, where eavesdroppers attempt to intercept messages. For the secrecy enhancements, we adopt an artificial noise (AN) assisted transmission scheme, in which AN is employed to create non-cancellable interference to eavesdroppers. The reliability and security are correspondingly characterized by the connection outage probability and secrecy outage probability, and their analytical expressions of them are attained through theoretical analysis for the purpose of the tradeoff issue discussion. Our results reveal that secrecy performance in mmWave ad hoc networks gains significant improvement through the use of AN. It also shows that given total transmit power, there exists a tradeoff between reliability and security to achieve optimal outage performance.

Abstract Role Back Access Control model (RBAC) allows devices to access cloud services after authentication of requests. However, it does not give priority in Big Data to devices located in certain geolocations. Regarding the crisis in a specific region, RBAC did not provide a facility to give priority access to such geolocations. In this paper, we planned to incorporate Location Time‐ (GEOTime) based condition alongside Priority Attribute role‐based access control model (PARBAC), so requesters can be allowed/prevented from access based on their location and time. The priority concept helped to improve the performance of the existing access model. TIME‐PARBAC also ensures service priorities based on geographical condition. For this purpose, the session is encrypted using a secret key. The secret key is created by mapping location, time, speed, acceleration and other information into a unique number, that is, K(Unique_Value) = location, time, speed, accelerator, other information. Spatial entities are used to model objects, user position, and geographically bounded roles. The role is activated based on the position and attributes of the user. To enhance usability and flexibility, we designed a role schema to include the name of the role and the type of role associated with the logical position and the rest of the PARBAC model proposed using official documentation available on the website for Azure internet of things (IoT) Cloud. The implementation results utilizing a health use case signified the importance of geology, time, priority and attribute parameters with supporting features to improve the flexibility of the existing access control model in the IoT Cloud.

Fastener needs to be monitored and inspected periodically to ensure the rail’s safety due to its easily damaged accessory for railway infrastructure. Recently, Industrial Internet of Things (IIoT) and artificial intelligence (AI)-based visual inspection techniques have been exploited to realize the online inspection of fastener’s fault by using a fault detection IoT vehicle that is mounted with multitype sensors and cameras according to the design of our research team. However, instead of traditional artificial inspection, the AI-based automatic fastener inspection approach is still faced with some challenges, for example, collection of enough samples of faulted fastener. In this article, we propose a cascade learning embedded vision inspection method of rail fastener based on the deep convolutional neural network (DCNN). The proposed method has two steps: 1) region position and 2) fault detection. First, a modified single shot multibox detector (SSD) model is adopted to locate the fastener regions from the captured railway images. Then, a key component detection (KCD) method based on the improved faster region convolutional neural network (RCNN) is proposed to realize the detection of faulted fastener. Extensive experiments are conducted to demonstrate the performance of the proposed method. The experiment results show that the proposed method achieves an average precision of 95.38% and an average recall of 98.62% on fastener detection, which is much better than the manual operation.

Multiaccess edge computing (MEC) network, as one of the key infrastructures of IoT, provides cloud computing capabilities at the edge of the radio access network (RAN) by integrating telecommunication and IT services. Integrating blockchain into the MEC network can provide users with secure, private, and traceable edge computing services at the near end, thereby improving IoT security, privacy, and automated use of resources. Due to some characteristics of the MEC network, there are still some challenges to integrate blockchain and edge computing into one system, especially the consensus algorithm of blockchain. The resources of edge computing nodes are limited, and the scale of the network is constantly expanding. Therefore, the blockchain consensus algorithm for the MEC network should occupy as little computing resources as possible, be green, and be permissionless. This article proposes a permissionless and scalable consensus algorithm “Hedera” for MEC network, which has the advantages of permissionless, security, decentralization, scalability, and greenness. The Hedera consensus algorithm is a hybrid blockchain consensus algorithm that combines the permissionless Proof-of-Capacity algorithm and the permissioned asynchronous Byzantine algorithm. This article tests the fairness, throughput, scalability, latency, and resource consumption of the algorithm by developing and deploying a prototype system. The experimental results show that the Hedera algorithm proposed in this article is fair, the throughput reaches 13986.3 TPS, and the resource consumption is much lower than the PoW consensus. By analyzing its security and liveness, it can resist the sybil attack, nothing-at-stake attack, selfish mining attack, and message hijacking attack, and has good liveness.

Coronavirus disease (COVID-19) pandemic has intensively damaged human socio-economic lives and the growth of countries around the world. Many efforts have been made in the direction of artificial intelligence (AI) techniques to detect the corona at an early stage and take necessary precautions to stop it from spreading or recovery from the infection. However, the situation and solutions are still challenging. In this paper, we proposed various technological aspects, solutions using a supervised/unsupervised manner and continuous health monitoring with physiological parameters. Finally, the performance of COVID-19 detection with Gaussian mixture model-universal background model (GMM-UBM) technique using the voice signal has been demonstrated. The developed system achieves the COVID-19 detection performance in terms of areas under receiver operating characteristic (ROC) curves in the range 60–67%. Moreover, the various lessons learned from the current COVID-19 crisis are presented for future directions.

The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies.

Abstract Parkinson’s disease (PD) is a neurological disease that has been reported to have affected most people worldwide. Recent research pointed out that about 90% of PD patients possess voice disorders. Motivated by this fact, many researchers proposed methods based on multiple types of speech data for PD prediction. However, these methods either face the problem of low rate of accuracy or lack generalization. To develop an approach that will be free of these issues, in this paper we propose a novel ensemble approach. These paper contributions are two folds. First, investigating feature selection integration with deep neural network (DNN) and validating its effectiveness by comparing its performance with conventional DNN and other similar integrated systems. Second, development of a novel ensemble model namely EOFSC (Ensemble model with Optimal Features and Sample Dependant Base Classifiers) that exploits the findings of recently published studies. Recent research pointed out that for different types of voice data, different optimal models are obtained which are sensitive to different types of samples and subsets of features. In this paper, we further consolidate the findings by utilizing the proposed integrated system and propose the development of EOFSC. For multiple types of vowel phonations, multiple base classifiers are obtained which are sensitive to different subsets of features. These features and sample-dependent base classifiers are integrated, and the proposed EOFSC model is constructed. To evaluate the final prediction of the EOFSC model, the majority voting methodology is adopted. Experimental results point out that feature selection integration with neural networks improves the performance of conventional neural networks. Additionally, feature selection integration with DNN outperforms feature selection integration with conventional machine learning models. Finally, the newly developed ensemble model is observed to improve PD detection accuracy by 6.5%.

Data-driven ontology-based knowledge (OK) presentation and computational linguistics for evolving semantic Asian social networks (ASNs) can make one of the most important platforms that provide robust and real-time data mapping in massive access across the heterogeneous big data sources in the web that is named OK-ASN. It benefits from computational intelligence, web-of-things (WoT) architecture, semantic features, statistical learning and pattern recognition, database management, computer vision, cyber-security, and language processing. OK-ASN is a critical strategy for WoT big data mining and enterprises from social media to medical and industrial sectors.

World Wide Web (WWW) is playing a vital role for sharing dynamic knowledge in every field of life. The information on web comprises a huge amount of data in different forms such as structured, semi structured, or few is totally in unstructured format. Due to huge size of information, searching from larger textual data about the specific topic or getting precise information is a challenging task. All this leads to the problem of word sense ambiguity (WSA). Urdu language-based information retrieval system using different techniques related to Web Semantic Search Engine architecture is proposed to efficiently retrieve the relevant information and solve the problem of WSA. The proposed system has average precision ratio 96% as compared to average precision ratio of 74% and 75% average precision Google for single word query. For the long text queries, our system outperforms the existing famous search engines with 92% accuracy such as Bing and Google having 16.50% and 16% accuracy, respectively. Similarly, the proposed system for single word query, the recall ratio is 32.25% as compared to 25% and 25% of Bing and Google. The results of recall ratio for long text query are improved as well, showing 6.38% as compared to 6.20% and 4.8% of Bing and Google, respectively. The results showed that the proposed system gives better and efficient results as compared to the existing systems for Urdu language.

The current advances in wearable sensors show the shining future of socially implemented Internet-of-Medical-Things (IoMT) devices (e.g., smartwatches). However, the recent machine learning approaches cannot be applied well in these devices, because almost all the processing in the IoMT devices is now being performed in classic forms (mainly as centralized computing) or based on cloud services. This topical collection has tried to extend our knowledge about how to apply collaborative learning to IoMT considering social edge/fog nodes’ facilities.

In future smart cities supported by cyber-physical social intelligence, autonomous behavioral decision for vehicular agents is going to become a general demand. Despite much progress achieved in autonomous behavioral decision of vehicular agents, the existing works can just be used in scenarios of short-distance behavioral decision. Naturally, they are not well suitable for long-distance behavioral decision tasks, posing much challenge in realistic cyber-physical environment. To bridge the existing gaps, this article proposes an autonomous behavioral decision framework for vehicular agents using cyber-physical social intelligence. First, it is expected to establish a dynamic planning model with multiple objectives and constraints. This can be embedded into the control unit of a vehicular agent to endow it with proper social intelligence. On this basis, an iterative search algorithm is specifically designed for it to find the optimal solutions from the whole solution space. Finally, two typical situation cases are implemented with use of simulation modeling to display the working architecture of the proposed method. In addition, a universal optimization search algorithm is selected as the baseline to be compared with the proposed method. The comparison results reveal both planning utility and running efficiency of the proposed method.

Offloading the dynamic tasks with fog computing is envisioned as a viable option for prolonging resource-limited constraints and improving the computational and communicational latency for delay-sensitive IoT applications. Besides, the priority of tasks and the target layers for offloading them to minimize the incurred service latency is a prime concern in layered computing architecture. To leverage the efficiency of the underlying computing nodes for the tasks’ heterogeneity and computational requirements with deadline constraints, this article presents a fuzzy logic technique to prioritize the tasks based on their resource requirements and associated deadline. For efficient scheduling, an elitism-based multipopulation Jaya is proposed to map these disparate groups of tasks to a cluster amalgamation of computational-rich heterogeneous computing nodes. Moreover, a compatibility-based heuristic offloading strategy is devised to determine compatible computing nodes to offload the computations considering the availability of resources and communicational time from the respective IoT devices. Finally, extensive simulations are carried out with conflicting scheduling parameters appraising the efficacy of the proposed strategy over existing algorithms. The percentages of improvements of the proposed algorithm over the compared algorithms are 35% and 28% for average waiting. time and average service latency, respectively.

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions.

Nowadays, Federated Learning (FL) over Internet of Medical Things (IoMT) devices has become a current research hotspot. As a new architecture, FL can well protect the data privacy of IoMT devices, but the security of neural network model transmission can not be guaranteed. On the other hand, the sizes of current popular neural network models are usually relatively extensive, and how to deploy them on the IoMT devices has become a challenge. One promising approach to these problems is to reduce the network scale by quantizing the parameters of the neural networks, which can greatly improve the security of data transmission and reduce the transmission cost. In the previous literature, the fixed-point quantizer with stochastic rounding has been shown to have better performance than other quantization methods. However, how to design such quantizer to achieve the minimum square quantization error is still unknown. In addition, how to apply this quantizer in the FL framework also needs investigation. To address these questions, in this paper, we propose FedMSQE - Federated Learning with Minimum Square Quantization Error, that achieves the smallest quantization error for each individual client in the FL setting. Through numerical experiments in both single-node and FL scenarios, we prove that our proposed algorithm can achieve higher accuracy and lower quantization error than other quantization methods.

Traffic Sign Recognition (TSR) is an essential component of Intelligent Transportation Systems (ITS) and intelligent vehicles. TSR systems based on deep learning have grown in popularity in recent years. However, since these models belong to the closed-world-oriented learning paradigm, they are only capable of accurately identifying traffic signs that are easy to collect and cannot adapt to the real world. Furthermore, the sample utilization of these methods is insufficient, the resource consumption of model training may become unbearable as the data scale grows. To address this problem, we propose a novel “knowledge + data” co- driven solution (i.e., Joint Semantic Representation algorithm, JSR) for TSR. JSR creates a hybrid feature representation by extracting general and principal visual features from traffic sign images. It also realizes the model’s reasoning ability to zero-shot TSR based on prior knowledge of traffic sign design standards. The effectiveness of JSR is demonstrated by experiments on four benchmark datasets and two self-built TSR datasets.

Accurate traffic flow forecasting is crucial to improving traffic safety and alleviating road congestion for intelligent transportation network management. Recently, spatial-temporal graph-based deep learning methods have achieving significant performance improvements in traffic flow forecasting. However, they only consider spatial-temporal correlation of traffic network but ignore a mass of semantic correlation. In addition, they need to centralize data for training models, leading to privacy leakage concern. To tackle these problems, we introduce a federated learning-based intelligent traffic flow forecasting model that integrates our proposed spatial-temporal graph-based deep learning model into the devised Multilevel Federated Learning framework(MFL), named MFVSTGNN. This MFL is used to allow data collaboration among different data owners to train an efficient model without sharing their private data, while achieving the trade-off between communication overhead and computation performance. The proposed spatial-temporal graph-based deep learning model is composed of two phases. The first phase utilizes Variational Graph Autoencoder (VGAE) to dynamically generate adjacency matrix that contains both the spatial and semantic dependencies, contributing to preserving valuable information for improving prediction accuracy, and the second phase employs general spatial-temporal graph neural network to conduct prediction. We evaluate the performance of MFVSTGNN with two large-scale traffic datasets from California and Los Angeles County. The experimental results demonstrate the superior performance of MFVSTGNN in reducing communication overhead, and improving prediction accuracy, validating the effectiveness of our proposed model.

In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.

The novel coronavirus disease 2019 (COVID-19) has been a severe health issue affecting the respiratory system and spreads very fast from one human to other overall countries. For controlling such disease, limited diagnostics techniques are utilized to identify COVID-19 patients, which are not effective. The above complex circumstances need to detect suspected COVID-19 patients based on routine techniques like chest X-Rays or CT scan analysis immediately through computerized diagnosis systems such as mass detection, segmentation, and classification. In this paper, regional deep learning approaches are used to detect infected areas by the lungs' coronavirus. For mass segmentation of the infected region, a deep Convolutional Neural Network (CNN) is used to identify the specific infected area and classify it into COVID-19 or Non-COVID-19 patients with a full-resolution convolutional network (FrCN). The proposed model is experimented with based on detection, segmentation, and classification using a trained and tested COVID-19 patient dataset. The evaluation results are generated using a fourfold cross-validation test with several technical terms such as Sensitivity, Specificity, Jaccard (Jac.), Dice (F1-score), Matthews correlation coefficient (MCC), Overall accuracy, etc. The comparative performance of classification accuracy is evaluated on both with and without mass segmentation validated test dataset.

In many sectors, including healthcare services, Internet of Things (IoT) systems are growing rapidly, providing promising technological, economical, and social potential. Healthcare services can be improved with IoT capabilities, including remote patient monitoring, diagnosis of medical issues in real-time, and more, all of which improves both the quality and the satisfaction of human users. The Internet of Medical Things (IoMT) is gaining momentum as wearable devices, and their numerous health monitoring applications increase popularity. The IoMT plays a significant role in reducing death rates by detecting diseases early. Prediction of heart disease is an essential challenge in clinical dataset analysis. The proposed research aim is to employ machine learning (ML) classification algorithms to predict heart disease. The IoMT-based cloud-fog diagnostics for heart disease have been proposed. Fog layer is used to quickly analyze patient data using ML classification techniques. The performance of the healthcare model is evaluated with different simulations and achieves 97.32% accuracy, 97.58% recall, 97.16% precision, 97.37%$F1$-measure, 96.87% specificity, and 97.22%$G$-mean, which has significant improvement as compared with previous models.

As the technology era progresses, many opportunities are brought to the healthcare industry. With the support of technology and Internet of Things platforms, e-healthcare is now more common than ever. However, the sensitive nature of healthcare records makes them vulnerable to many attacks. Therefore, a privacy-enabled framework for cloud-based e-healthcare systems is proposed to achieve privacy-preserved and secured communication in e-healthcare. The analysis of the proposed protocol is presented in this article to demonstrate that it is secure against all well-known security attacks and provides patient anonymity, doctor anonymity, and patient and doctor unlinkability while ensuring data confidentiality. Additionally, the security simulations are performed using the Automated Validation of Internet Security Protocols and Applications tool. We also performed the proposed framework's performance analysis and compared it with existing frameworks. The analysis result indicates that the proposed framework achieves encouraging performance over other frameworks while ensuring security.

The Industrial Internet of Things (IIoT) is the essential component of Industry 4.0. Blockchain is a promising technology for secure data sharing and trustable cooperation between IIoT devices. However, the ever-growing transaction records make it difficult for the storage-limited IIoT devices to join the blockchain network. In this article, an adaptive compression scheme is proposed to decrease the storage volume on each node. In the scheme, the block body is compressed by representing the included transactions as their remainders stored in the distributed nodes. The original transaction could be recovered based on the Chinese remainder theorem. In particular, each node adapts its compression ratio according to its storage resource. The nodes storing more data have advantages in transaction recovery, introducing an incentive mechanism for efficient storage utilization. The theoretical analysis and simulation results show that the proposed scheme can achieve a high compression ratio with good service availability. The proposed scheme dramatically lowers the threshold for IIoT devices to join the blockchain network, which is important for the large-scale application of blockchain in Industry 4.0.

5G edge computing enabled Internet of Medical Things (IoMT) is an efficient technology to provide decentralized medical services while device-to-device (D2D) communication is a promising paradigm for future 5G networks. To assure secure and reliable communication in 5G edge computing and D2D enabled IoMT systems, this article presents an intelligent trust cloud management method. First, an active training mechanism is proposed to construct the standard trust clouds. Second, individual trust clouds of the IoMT devices can be established through fuzzy trust inferring and recommending. Third, a trust classification scheme is proposed to determine whether an IoMT device is malicious. Finally, a trust cloud update mechanism is presented to make the proposed trust management method adaptive and intelligent under an open wireless medium. Simulation results demonstrate that the proposed method can effectively address the trust uncertainty issue and improve the detection accuracy of malicious devices.

The papers in this special section focus on artificial intelligence Internet of Things for medical things (AIoMT), with particular emphasis on medical sensors for remote patient monitoring and body area interfacing. Examines issues involving design and implementation, practice use, measurements, and patient monitoring.

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.

This current research paper measures the performance of Indian private sector banks through various multi-criteria decision-making (MCDM) techniques. To measure the performance of the banks the data about various criteria such as profit after tax, borrowings, advances, adjusted EPS, enterprise value, and NPAs from the Annual reports of the banks were extracted. The MCDM techniques, SDV (standard deviation) CRITIC (CRiteria Importance Through Intercriteria Correlation), ARAS (Additive Ratio Assessment), MOORA (Multi-objective Optimization on the basis of Ratio Analysis) are applied to analyze the data and measure the performance of the banks. In MCDM techniques, different methods provide different weights of the criteria, and also different ranks are obtained by different methods. Sensitivity analysis was carried out by measuring the criteria weights by SDV and CRITIC and the alternatives are ranked using two MCDM techniques, ARAS and MOORA. The results of the study show that among the private banks, HDFC created a benchmark and leading while Yes bank has shown poor performance on the basis of annual reports of 2020.

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning‐based intrusion detection paradigm for IIoT with hybrid rule‐based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule‐based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well‐known network datasets, NSL‐KDD and UNSW‐NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL‐KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW‐NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.

Chronic wound (CW) diagnosis is more demanding to monitor the healing process of the wound. However, the availability of specialist medical help in remote/rural areas in developing countries, like India, is a challenge. Further, visiting specially hospitals in city is both expensive and time consuming. This paper discusses the comprehensive CW diagnostic approach using three important modules, namely, wounds data acquisition (WDA), tele-wound technology network (TWTN), and wound screening and diagnostic (WSD) respectively. We have proposed a CW characterization and diagnosis under telemedicine framework to classify the tissue depending on percentage of wound and based on color variation at regular time intervals. The Bayesian classifier based wound characterization (BWC) method is proposed to identify the percentage of tissue with high accuracy. It has been observed that the BWC method provides overall accuracy of 87.11%.