Human Activity Recognition (HAR) stands as a crucial technology, with applications ranging from healthcare monitoring to sports analytics. However, the traditional approach to HAR is often time-consuming and susceptible to human errors due to the high complexities involved in processing diverse sensor data. Recognizing the imperative for efficiency and accuracy in HAR systems, we propose the development of an Automated Decision-maker (ADM) system. This system serves to automate HAR pipelines, addressing the challenges posed by the huge sensor data. By harnessing the power of automation, ADM significantly streamlines the HAR process, reducing the time required for hyperparameter tuning and minimizing the risk of human errors. The results obtained from our proposed ADM system demonstrate notable improvements in HAR performance, showcasing achieved accuracy of 96.436% for UCI-HAR & 99.783% for PAMAP2 datasets. Moreover, ADM can be described as an innovative approach that contributes to the optimization of HAR systems while also establishing a foundation for building robust and reliable systems in complex environments.

Smart healthcare aims to revolutionize medical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultaneously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized training, the VQNN can achieve higher accuracy than that without personalized training.

Nanorobots have been used in smart health to collect time series data such as electrocardiograms and electroencephalograms. Real-time classification of dynamic time series signals in nanorobots is a challenging task. Nanorobots in the nanoscale range require a classification algorithm with low computational complexity. First, the classification algorithm should be able to dynamically analyze time series signals and update itself to process the concept drift (CD). Second, the classification algorithm should have the ability to handle catastrophic forgetting (CF) and classify historical data. Most importantly, the classification algorithm should be energy-efficient to use less computing power and memory to classify signals in real-time on a smart nanorobot. To solve these challenges, we design an algorithm that can Prevent Concept Drift in Online continual Learning for time series classification (PCDOL). The prototype suppression item in PCDOL can reduce the impact caused by CD. It also solves the CF problem through the replay feature. The computation per second and the memory consumed by PCDOL are only 3.572 M and 1 KB, respectively. The experimental results show that PCDOL is better than several state-of-the-art methods for dealing with CD and CF in energy-efficient nanorobots.

ABSTRACT This study introduces an innovative optimization algorithm called Lionfish Search (LFS) technique, which is inspired by the visual predator Lionfish, in which it is specifically imitating their hunting tactics. The suggested algorithm considers several parameters that influence the hunting behaviour of lionfish, such as visual acuity, mobility, striking success, and prey swallowing potential. Furthermore, this study examines the influence of the physiological traits of the lionfish and their relationship with environmental factors. The novel search algorithm has shown enhanced performance and efficiency, particularly in scenarios where the integration of visual cues and intricate hunting strategies is vital. The suggested LFS method was evaluated using 20 well‐known single‐modal and multi‐modal mathematical functions to analyse its different characteristics. The LFS method has shown remarkable efficacy in both exploration and exploitation, effectively reducing the likelihood of being trapped in local optima. Additionally, it has a rapid convergence capacity, particularly in the realm of large‐scale global optimization. Comparisons were made between the LFS algorithm, and 10 other prominent algorithms mentioned in the literature. The proposed LFS metaheuristic algorithm outperformed the others on almost all of the examined functions, demonstrating a statistically significant advantage. Moreover, the positive results found in three practical optimization situations demonstrate the effectiveness of the LFS in accomplishing problem‐solving tasks that have limited and unknown search areas.

ABSTRACT The application of artificial intelligence (AI) in healthcare has been witnessing an increasing interest. Particularly, federated learning (FL) has become favourable due to its potential for enhancing model quality whilst maintaining data privacy and security. However, the effectiveness of present FL methodologies could underperform under non‐IID conditions, characterised by divergent data distributions across clients. The globally constructed FL model may suffer potent issues by allowing the least‐performing models to equal participation. Thus, we propose a new accuracy‐based FL approach (FedAcc) which only takes into account the clients' validation accuracy to consider their participation during global aggregation, also called Smart Healthcare Amplified (SHA). However, with limited supervised data it is challenging to increase the model performance thus concept of transfer learning (TL) is used. TL enables the global model to integrate knowledge from precomputed systems, resulting in an efficient model. However, the complexity of the global system is amplified by these TL models, leading to challenges related to vanishing gradients, particularly when dealing with a substantial number of layers. To mitigate this, we present a Transfer Learning Domain Adaptation Model (TLDAM). TLDAM employs a two‐layered sequentially trained TL model, which contains approximately 50% fewer layers compared to traditional TL models. TLDAM is trained on multiple datasets such as MNIST and CIFAR10, to enhance its knowledge and make it domain‐adaptive. Moreover, experimental results conducted on the UCI‐HAR dataset reveal the supremacy of our proposed framework with an accuracy of 94.2990%, F‐score of 94.2820%, precision of 94.3058%, and recall of 94.2993% over traditional FL techniques and state‐of‐the‐art techniques.

Intelligent transportation systems (ITS) use information communication and technologies to provide road safety, traffic control, traffic congestion, accident avoidance, etc. Traffic accidents cause huge disruption of vehicle movements, road blockages, traffic jams, etc., known as secondary traffic incidents. These incidents in turn lead to huge CO2 emissions and fuel consumption, which directly impact the environment, reduce vehicle mileage, and unnecessary fuel waste. To reduce or avoid secondary traffic incidents, in this paper, we propose an Edge-AI-IoT integrated energy efficient system to detect, analyze, and predict the primary and secondary incidents. The proposed system uses the existing sensor technology like accelerometer, tilt, etc., to detect the accident and severity levels using Edge-AI-IoT, locally it analyzes and predicts the secondary incidents. The proposed system has been exhaustively simulated using real-time scenarios in OMENT++, Veins, and SUMO, and it was tested using the NVIDIA Jetson AGX Xavier edge device integrated with the ThingSpeak cloud platform. The proposed system is tested with performance parameters such as clearance time, accident detection, density of vehicles, speed of vehicles, CO2 emissions, and fuel consumption at different times of day. The simulation and real-time tested results show its real-time deployment.

Special issue on International conference on computing and communication networks (ICCCN2022

Internet users perceive a multilingual web but are unfamiliar with it due to communication in their regional language called Cross-Lingual Information Retrieval (CLIR). In CLIR, a translation technique is used to translate the user queries into the target document’s language. Conventional translation techniques are based on either a manual dictionary or a parallel corpus, whereas the trending Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) techniques are trained on a parallel corpus. NMT is not so mature for Hindi-English translation, according to the literature, and SMT performs better than the NMT. SMT provides a static translation due to the limited vocabularies in the available parallel corpus. It may not provide the translations for missing or unseen words, whereas the web provides a dynamic interface where multiple users are updating information at the same time. The web may provide the translations for missing or unseen words, and therefore the web is effectively used for technically developed languages like English, German, Spanish, Russian, and Chinese. In this article, different web resources such as Wikipedia, Hindi WordNet and Indo WordNet, ConceptNet, and online dictionary based translation techniques are proposed and applied to Hindi-English CLIR. Wikipedia-based translation approach incorporates three modules—exactly matched, partially matched, and disambiguation—to address the issues of wrong inter-wiki links, partially matched terms, and ambiguous articles. Hindi WordNet and Indo WorNet attribute “English synset” and ConceptNet attributes “Related term” & “Synonymy” are used for obtaining translations. Further, WordNet path similarity is used to disambiguate translations. Various online dictionaries are available that return multiple relevant and irrelevant translations. The proposed approaches are compared to the SMT where the Wikipedia-based approach achieves approximately similar mean average precision to SMT.

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94.7% and 93.6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite the high accuracy of supervised EEG classification models, they are constrained by labor-intensive annotations and poor generalization. Self-supervised models address these issues but encounter difficulties in matching the accuracy of supervised learning. Three challenges persist: 1) capturing temporal dependencies in EEG; 2) adapting loss functions to describe feature similarities in self-supervised models; and 3) addressing the prevalent issue of data imbalance in EEG. This study introduces the DreamCatcher Network (DCNet), a self-supervised EEG classification framework with a two-stage training strategy. The first stage extracts robust representations through contrastive learning, and the second stage transfers the representation encoder to a supervised EEG classification task. DCNet utilizes time-series contrastive learning to autonomously construct representations that comprehensively capture temporal correlations. A novel loss function, SelfDreamCatcherLoss, is proposed to evaluate the similarities between these representations and enhance the performance of DCNet. Additionally, two data augmentation methods are integrated to alleviate class imbalances. Extensive experiments show the superiority of DCNet over the current state-of-the-art models, achieving high accuracy on both the Sleep-EDF and HAR datasets. It holds substantial promise for revolutionizing sleep disorder detection and expediting the development of advanced healthcare systems driven by cognitive computing.

Abstract With recent advancements in the internet of things (IoT), wearables, and sensing technologies, the quality of healthcare services gets improved and it caused a shift from conventional clinical‐based healthcare to real‐time monitoring. The sensors are commonly integrated into several medical gadgets to save the bio‐signals produced by the physiological activities of the human body. At the same time, a biomedical electrocardiogram (ECG) signal is employed as a familiar way to examine and diagnose cardiovascular diseases (CVDs), which is rapid and non‐invasive. Since the increasing number of patients degrades the classification performance due to high differences in the ECG signal patterns among several patients, computer‐assisted automated diagnostic tools are essential for ECG signal classification. With this motivation, this paper introduces a new IoT and deep learning (DL) enabled healthcare disease diagnosis (IoTDL‐HDD) model using biomedical ECG signals. The proposed IoTDL‐HDD model aims to detect the presence of CVDs by the use of DL models in biomedical ECG signals. In addition, the proposed IoTDL‐HDD model utilizes a BiLSTM feature extraction technique to extract useful feature vectors from the ECG signals. For improving the efficiency of the BiLSTM technique, the artificial flora optimization (AFO) algorithm is employed as a hyperparameter optimizer. Besides, a fuzzy deep neural network (FDNN) classifier is employed for assigning proper class labels to the ECG signals. The performance of the IoTDL‐HDD model is examined on biomedical ECG signals and the outcomes are inspected in distinct features. The resultant experimental outcomes pointed out the supremacy of the IoTDL‐HDD model with the maximum accuracy of 93.452%.

Abstract Because of increased applications of Internet of Things (IoT) environment in real‐time environment, confidential data gathered by the IoT devices are being communicated to the cloud environment to train the machine learning (ML) models in understanding the patterns that exist in the data. At the same time, the sensitive nature of the IoT data attracts malicious users into hacking efforts. An intrusion detection system (IDS) can be applied to ensure security in the IoT environment. In order to improve security, the ML models can be executed at the data source instead of centralized cloud server. Federated learning (FL) is a recent progression of ML model which enables the ML models to move into the data source rather than moving the data to centralized cloud and thereby resolves cybersecurity problems in the IoT environment. In this view, this study introduces an FL based IDS using bird swarm algorithm based feature selection with classification (FLIDS‐BSAFSC) model in IoT environment. The presented FLIDS‐BSAFSC model undergoes training on multiple aspects of IoT dataset in a decentralized format to classify, detect, and defend against attacks. The proposed FLIDS‐BSAFSC model initially applies min–max normalization technique to pre‐process the IoT data. Besides, BSA based feature selection (BSA‐FS) technique is designed to elect feature subsets. Finally, social group optimization algorithm with kernel extreme learning machine model is employed for identifying various kinds of classes. In the view of FL where the IoT dataset is not distributed to the server carries out profile aggregation competently with the advantage of peer learning. The experimental validation of the FLIDS‐BSAFSC model is tested using benchmark datasets and the results are inspected under several aspects. The experimental values highlighted the better performance of the FLIDS‐BSAFSC model over recent approaches.

Road traffic crashes are one of the prime issues in the world. Every year 1.35 million people die, 20–50 million fatal injuries, and many incur a disability due to road traffic crashes. 50% of the total death, injuries, and disabilities are among vulnerable road users (VRUs), such as motorcyclists, cyclists, and pedestrians. Among these VRUs, highly vulnerable is pedestrians. In this article, we are dealing with providing safety and alert system to pedestrians and vehicles to reduce and/or avoid the causes of road traffic crashes in metropolitan areas. In this article, we develop a cooperative communication framework between pedestrians and nearby vehicles as well as traffic light systems using mobile agent systems and apps. The proposed cooperative communication framework controls the mobility of vehicles as well as pedestrians to avoid accidents. We have developed an application that will notify the pedestrian if there is any automobile within the range of 200 m of the pedestrian. If there are any vehicles in this range, using direct Wi-Fi, connect the vehicles and pedestrians to notify them about their presence of them. The proposed system is implemented and tested in real time as well as simulated in the SUMO, Veins, OMNeT++, and MiXiM simulator. The proposed system’s results (real time, simulation, and comparison) show real-time deployability, accuracy, and reliability.

In smart cities, managing the energy in the Industrial Internet of Things (IIoT) IoT networks is a challenging issue. In a cluster-based IIoT, cluster heads (CHs) near the sink quickly deplete their energy as they are responsible for relaying a large amount of data. It results in isolating the sink from the network and causes an energy hole or hot spot problem. Many solutions exist for this problem, including sink mobility, unequal clustering, use of gateways, etc. Each of these solutions has its own merits and demerits. It is well known that combining clustering and energy-aware routing can significantly extend the IIoT’s lifetime. In this work, we propose an advanced energy management and harvesting system for enhancement of network lifetime of IIoT in smart cities by addressing the hot spot problems. The system is based on energy-aware clustering and routing algorithms that employ energy-harvesting nodes deployed along with the normal sensor nodes. We simulate the proposed system extensively for various cases of network topology. A couple of existing algorithms are analyzed and compared with simulated results. It is shown that the network lifetime is increased significantly compared to the existing ones.

The exponential growth of the Internet of Everything (IoE), in recent times, has revealed many underlying security vulnerabilities of the nodes forming IoE networks. The extension of conventional security protocol to these devices has been greatly complicated by the prevalence of restricted computational hardware and limited battery life. Modern learning-based algorithms have shown the potential to secure the IoE networks without undue duress on the nodes’ limited capabilities. In this article, a machine learning-based architecture has been proposed to identify malicious and benign nodes in an IoE network operating with big data. A novel approach for the cooperation of XGBoost and deep learning models along with a genetic particle swarm optimization (GPSO) algorithm to discover the optimal architectures of individual machine learning models has been proposed. Through simulations, it is shown that GPSO-based learning algorithms provide reliable, robust, and scalable solutions. The proposed model significantly outperforms other security protocols in the classification of malicious and benign nodes forming an IoE network.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

Fingerprint recognition has emerged as one of the most reliable biometric authentication methods , owing to its uniqueness and permanence. However, the security and confidentiality of the user’s data are key considerations in modern biometric systems. In this study, we describe an intelligent computational technique for automatically validating fingerprints for identification and verification purposes. The feature vector is created by fusing Gabor filtering features with deep learning techniques like the faster region-based convolutional neural network (Faster R-CNN). This study uses linear and decision-based median filtering (DBMF) techniques to minimize visual impulse noise. Faster-R-CNN with DBMF was applied to the feature vectors to reduce overfitting problems while improving classification precision and reliability. For fingerprint matching, the Euclidean distance between the associated Harris-SURF feature vectors of two feature points is used to measure feature-matching similarity between two fingerprint images . Furthermore, for fine-tuned matching an iterative technique known as RANSAC (Random Sample Consensus) is used. The experimental results collected from the public-domain fingerprint databases FVC-2002 DB1 and FVC-2000 DB1 show that the proposed design is viable and performs well with an accuracy of 99.43%, MSE value of 43.321%, and an execution time of 3.102 ms which was more exact than existing models.

In recent years, neural networks have achieved impressive performance on dialogue response generation. However, most of these models still suffer from some shortcomings, such as yielding uninformative responses and lacking explainable ability. This article proposes a Two-stage Dialogue Response Generation model (TSRG), which specifies a method to generate diverse and informative responses based on an interpretable procedure between stages. TSRG involves a two-stage framework that generates a candidate response first and then instantiates it as the final response. The positional information and a resident token are injected into the candidate response to stabilize the multi-stage framework, alleviating the shortcomings in the multi-stage framework. Additionally, TSRG allows adjusting and interpreting the interaction pattern between the two generation stages, making the generation response somewhat explainable and controllable. We evaluate the proposed model on three dialogue datasets that contain millions of single-turn message-response pairs between web users. The results show that, compared with the previous multi-stage dialogue generation models, TSRG can produce more diverse and informative responses and maintain fluency and relevance.

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

Abstract Most of the tragedies on any bridge structure have been the cause of high‐density crowd behavior as a response to trampling as well as the crushing scenario. Therefore, it is most important to monitor such unforeseen situations by sensing the load imposed on the bridge structures. This scenario may arise where crowd movement is huge on these types of bridges. Similarly, the fiber Bragg grating (FBG) is a promising technology for structural health monitoring applications. In this work, an Internet of Things based FBG optical sensing scheme is proposed to monitor real‐time strain distribution throughout the bridge structures and localization of load imposed on the structure from a central control room. A suspension bridge model is designed by referring to a real bridge scenario and these FBG sensors are deployed to validate the proposed machine learning models. In this article, the performances of two machine learning strategies are discussed for the accurate estimation of load and its position by acquiring high sensitive FBG sensors signals at a very high data rate. The algorithms include K‐nearest neighbor (KNN) and random forest (RF); which are applied on each sensing data source, and then validated using a prototype suspension bridge model integrated with three FBG sensors (1532 nm, 1538 nm, and1541 nm) on a single optical fiber cable.

Deep learning has a great impact on advanced real-world problem solving since it can deal with complex and big amount of data. One of the recent successful applications of deep learning is biomedical imaging and there is a remarkable research effort using medical image data (obtained via MR, tomography, X-Ray, pathology, microscopy, breast CAD, etc.) to perform especially diagnosis oriented studies considering vital diseases such as human brain disorders diseases (i.e., Alzheimer's, Parkinson, sleep disorders) or cancer (i.e., breast cancer, lung cancer, skin cancer). The literature often reports effective results, and thus the use of deep learning for biomedical imaging is a research hot topic. Deep learning is essentially a collection of advanced neural networks such as convolutional neural networks (CNN), deep belief networks (DBN), or auto-encoder neural networks. CNN is the most famous among them but all of these deep learning techniques can be successfully applied in biomedical imaging studies. In some cases, it has been also possible to combine them in hybrid-modelled solutions for improved results. Here, the key questions for understanding the performance of such deep neural networks could be (1) How effective can these neural networks detect a disease, via biomedical imaging? (2) How fast and early can they perform a diagnosis? (3) How can they accomplish the same performance for different types of diseases? (4) How can they contribute to the current and future of medicine?, by moving over the biomedical imaging? This special issue focuses on recent advances, challenges, and future perspectives about deep neural networks applied in biomedical studies in different domains of knowledge. From around 90 submitted articles to this particular section, six papers were selected based on the reviews. Each paper was reviewed by at least two reviewers and went through at least two rounds of reviews. The brief contributions of these papers are discussed below. In the first paper of this special issue, the authors (Shah et al., 2022) have used deep-convolutional generative adversarial networks algorithm to address which generates synthetic images for all the classes (Normal, Pneumonia and COVID-19). To validate whether the generated images are accurate, the k-mean clustering technique with three clusters (Normal, Pneumonia and COVID-19) have been used. The selected X-ray images classified in the correct clusters for training. In this way, a synthetic dataset with three classes has been formed. The generated dataset was then fed to The EfficientNetB4 for training and the experiments achieved promising results of 95% in terms of area under the curve (AUC). The authors (Yadav et al., 2022) propose an enhanced DL CNN model with the Leaky ReLU activation function. DermNet NZ's facial acne images dataset is used for the experiments. Three different techniques- K-Means, texture analysis and HSV model-based segmentation, are applied for image segmentation to extract the acne region from skin images. After applying all the above image segmentation methods five times for each method, output images from K-Means and HSV (5 + 5 images) are collected and combined with the dataset. Using that dataset, one SVM model using Scikit-learn and two CNN models- one with the ReLU activation function and another with the LeakyReLU activation function, is trained. Out of these three models, the proposed CNN (LeakyReLU) model achieved a 97.54% accuracy. In this paper by Loh et al. (2022), the short-time Fourier transform (STFT) is first applied to the EEG signals to obtain spectrogram images of MDD patients and healthy subjects. These spectrogram images are then fed to the CNN model for automated detection of MDD patients and healthy subjects. The EEG signals used in this study were obtained from public database with 34 MDD patients and 30 healthy subjects. The highest classification accuracy, precision, sensitivity, specificity and F1-score of 99.58%, 99.40%, 99.70%, 99.48% and 99.55%, respectively, were obtained with hold-out validation. The proposed MDD detection model is highly accurate and needs to be validated with more diverse MDD database before it can be used in clinical settings. In this research, the authors (Mallikarjuna et al., 2022) support deep neural network (DNN) analysis in healthcare and COVID-19 pandemic and gives the smart contract procedure, to identify the feature extracted data (FED) from the existing data. At the same time, the innovation will be useful to analyse future diseases. The proposed method also analyse the existing diseases which had been reported and it is extremely useful to guide physicians in providing appropriate treatment and save lives. To achieve this, the massive data is integrated using Python scripting language under various libraries to perform a wide range of medical and healthcare functions to infer knowledge that assists in the diagnosis of major diseases such as heart disease, blood cancer, gastric and COVID-19. The next paper by Mansour et al. (2022) presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM-CRC. The presented AIFM-CRC model primarily undergoes Gaussian filtering based noise removal and contrast enhancement as a pre-processing stage. In addition, a fusion based feature extraction process takes place where the SIFT based handcrafted features and Inception v4 based deep features are fused together. Besides, whale optimization algorithm tuned deep support vector machine model is employed as a classification technique to determine the existence of CRC. In order to highlight the proficient results analysis of the AIFM-CRC model, a comprehensive simulation analysis takes place. The resultant experimental values pointed out the betterment of the AIFM-CRC model by accomplishing a maximum accuracy of 96.18%. The final article by Yuan et al. (2022) explores the adoption value of deep learning combined with computed tomography (CT) imaging omics in the prediction of metastatic lymph nodes of nasopharyngeal carcinoma (NPC). An end-to-end neural network architecture was designed based on the fully convolutional neural network (FCNN), which was applied to the CT image analysis of 52 patients with lymphatic metastasis and 36 patients without lymphatic metastasis. Patient's lymph node volume (V), the largest cross-sectional shortest diameter (d-value) and other macro characteristics were recorded. The microscopic features of its CT imaging omics were extracted. The results showed that the lymph node volume (4.37 ± 0.67) and the shortest diameter of the largest cross section (12.35 ± 2.31) of patients with lymph node metastasis were greatly larger than those without lymph node metastasis (1.84 ± 0.65, 7.98 ± 2.04) (p < 0.05). To conclude, this special issue publishes six papers out of a total of around 90 submitted papers. The guest editors hope that the research contributions and findings in this special issue would benefit the readers in enhancing their knowledge and encouraging them to work on various aspects of deep neural networks for biomedical data and imaging. We want to express our sincere thanks to the Editor-in-Chief and Special Issues & Reviews Editor for allowing us to organise this particular issue. The editorial office staffs are excellent, and thanks for their support. We are also thankful to all the authors who made this special issue possible, and to the reviewers for their thoughtful contributions.

Developments in computer communications and networks have led to exciting new areas of research and application, including the internet of things, vehicular networks, collaborative big data analysis, to name just a few. Moreover, the design and implementation of energy efficient future generation communication and networking technologies have fostered the development of new mobile, pervasive, and large-scale computing technologies. The International Conference on Computing and Communications Networks serves as a forum for exchanging the latest findings and experiences ranging from theoretical research to practical system development in all aspects of computing and networking. In its 2021 edition (ICCCN2021), many of those contributions had a significant artificial intelligence component, which is the focus of this Special Issue, which brings together selected papers from ICCCN2021. Of around 25 submitted articles, only 10 papers were selected based on their reviews. Each paper was reviewed by at least two reviewers and went through at least two rounds of reviews. The contributions of these papers are summarized briefly below. Ibrahim et al., 2022 propose a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over- and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. Gupta et al., 2022 present a new AI technique based on optimal deep convolutional neural network (AI-ODCNN) for retinal fundus image classification. Primarily, the proposed model uses the Gaussian Blur based noise removal and contrast enhancement technique (CLAHE) based contrast enhancement technique to pre-process the retinal fundus image. In addition, morphology and contour-based image segmentation is performed. Moreover, the deep CNN with RMSProp Optimizer is employed for retinal fundus image classification. A wide range of simulations was performed on the automated retinal image analysis and structured analysis of the retina and the outcomes are examined with respect to various measures. The simulation outcomes ensured the better performance of the proposed approach related to other recent algorithms with maximum accuracy of 96.47%. Zhang et al., 2022 present a genetic algorithm-based on-orbit self-repair implementation for SRAM FPGAs to address the disadvantages of the genetic algorithm-based fault repair method in the aerospace industry. The proposed method has been verified by various applications in XC7VX330T, which demonstrates its engineering practicability. Mishra & Kohli, 2022 present the Anas platyrhynchos optimizer with deep learning-enabled block-based motion estimation (APODL-BBME) model. The proposed model estimates motion using block-based concepts and DL approaches. To accomplish this, the training and testing frames are separated into non-overlapping block categories, and the blocks are filtered via the bilateral filtering (BF) approach. In addition, a histogram of gradients (HOG) and densely connected network (DenseNet) model are employed to extract features, which are then fed into the bidirectional long short-term memory (BiLSTM) model to classify the input features. Finally, the APO algorithm is applied to optimally tune the hyperparameters of the BiLSTM model, which helps to improve the overall motion estimation efficacy, showing the novelty of the work. To demonstrate the enhanced performance of the APODL-BBME model, a comprehensive analysis is carried out, and when we compare the results, the APODL-BBME model outperforms recent motion estimation approaches. Bedi et al., 2022 propose an unsupervised approach for extractive summarization, based on semantic similarity and keyword-phrase extraction. Merging Concept Map and the RAKE method, a generic summary is computed based on threshold values. Both single-document and multi-document summarization can be accomplished with the approach. To evaluate the proposed unsupervised approach, various biomedical transcripts of neuro-science, general medicine, gastroenterology, orthopaedic and radiology domain are used. MT Sample Dataset is used to collect 1040 different transcripts. Using the proposed approach, an average ROUGE score of 0.77 for single-document summarization; however, for generic summary, an average ROUGE is 0.72. The proposed technique is validated for the previous corpus of BioMed articles, and results are better with state-of-the-art techniques. Marzouk et al., 2022 introduce a Quasi-Oppositional Wild Horse Optimization-based Multi-Agent Path Finding (QOWHO-MAPF) scheme for real-time IoT systems. The aim of the proposed QOWHO-MAPF scheme is to determine the optimal set of paths to reach the destination in real-time IoT networks. QOWHO algorithm is created by integrating the concepts of Quasi-Oppositional Based Learning (QOBL) and conventional WHO algorithm. In addition, the proposed QOWHO-MAPF model derives a fitness function that involves two input parameters such as residual energy and distance-to-destination. The proposed QOWHO-MAPF model was experimentally analysed and the results were inspected under several aspects. The simulation results established that QOWHO-MAPF model is a superior model compared with other state-of-the-art models. Vinoth & Prabhavathy, 2022 propose an automated sarcasm detection and classification tool using hyperparameter tuned deep learning (ASDC-HPTDL) model for social media. The proposed ASDC-HPTDL technique primarily involves pre-processing stage to transform the data into useful format. In the next stage of pre-processing, the pre-processed data are converted into the feature vector by Glove Embedding's technique. Then attention bidirectional gated recurrent unit (ABiGRU) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the ABiGRU technique, a hyperparameter tuning process using improved artificial flora algorithm (IAFO) is employed, showing the novelty of the work. The proposed model is validated using the benchmark dataset and the results are examined in terms of precision, recall, accuracy, and F1-score. Nyangaresi et al., 2022 deploy an ANN for target cell selection in a 5G network, and authentication of communicating entities takes place before being admitted in the new cell. It has a low packet loss ratio and latency variations. This scheme is resilient against conventional 5G attack vectors at relatively low costs. Finding drug-target interactions (DTI) is crucial in making new drugs. As new drugs are discovered, there is a significant focus on repurposing existing drugs and incorporating approved drugs. Most computer models for predicting drug-target interactions emphasize binary classification, but the goal is to determine if two drug targets interact. Sharma & Deswal, 2022 compare drug and protein-encoding techniques to predict DT binding affinities. The validation results on the standard dataset show that the proposed model can help with drug discovery by predicting how well DT binds. Finally, Maray et al., 2022; Marzouk et al., 2022 introduce Intelligent Metaheuristics-based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML-MD) on IoT-enabled MTS. Primarily, IMFSOML-MD technique involves the design of Quantum Invasive Weed Optimization Algorithm-based Feature Selection technique to optimally choose a subset of features. Moreover, an Optimal Wavelet Neural Network (OWNN) model is employed to perform the classification process. The initial parameters of the WNN model are optimally tuned with the help of Colliding Bodies Optimization algorithm, thereby improving the detection performance. The proposed IMFSOML-MD technique was experimentally validated using publicly available CICMalDroid2020 dataset. The results from extensive comparative analysis demonstrated the superiority of the proposed IMFSOML-MD technique over other compared methods in terms of detection performance with maximum accuracy of 98.96%. We want to express our sincere thanks to the editor-in-chief for allowing us to organize this particular issue. The editorial office staffs are excellent, and we thank them for their support. We are also thankful to all the authors who made this special issue possible, and to the reviewers for their thoughtful contributions. Deepak Gupta received a B. Tech. Degree in 2006 from the Guru Gobind Singh Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from Delhi Technological University, India, and PhD degree in 2017 from Dr. APJ Abdul Kalam Technical University (AKTU), Lucknow, India. He completed his Post-Doc from the National Institute of Telecommunications (Inatel), Brazil, in 2018. He has co-authored more than 207 journal articles, including 168 SCI papers and 45 conference articles. He has authored/edited 60 books, published by IEEE-Wiley, Elsevier, Springer, Wiley, CRC Press, DeGruyter, and Katsons. He has filled four Indian patents. He is the convener of the ICICC, ICDAM, ICCCN, ICIIP & DoSCI Springer conferences series. He is Associate Editor of Computer & Electrical Engineering, Expert Systems, Alexandria Engineering Journal, Intelligent Decision Technologies. He is the recipient of the 2021 IEEE System Council Best Paper Award. He has been featured in the list of top 2% scientist/researcher databases worldwide. In India, Rank 1 as a researcher in the field of healthcare applications (as per Google Scholar citation) and Ranked #78 in India among Top Scientists 2022 by Research.com. He is also working towards promoting Startups and also serving as a Startup Consultant. He is also a series editor of "Elsevier Biomedical Engineering" at Academic Press, Elsevier, "Intelligent Biomedical Data Analysis" at De Gruyter, Germany, and "Explainable AI (XAI) for Engineering Applications" at CRC Press. He is appointed as Consulting Editor at Elsevier. Accomplished productive collaborative research with grants of approximately $144000 from various international funding agencies, and he is Co-PI in an International Indo-Russian Joint project of Rs. 1.31 CR from the Department of Science and Technology.

Abstract World Health Organization recognized COVID‐19 as a pandemic on March 11, 2020. A total of 213 countries and territories around the world have reported a total of 27,948,441 confirmed cases as on September 9, 2020. This article adopted two non‐linear growth models (Gompertz, Verhulst) and exponential model (SIR) to analyse the coronavirus pandemic across the world. All the models have been used for active COVID‐19 patients predictions based on the data collected from John Hopkins University repository in the time period of January 30, 2020 to June 4, 2020. Outbreak of COVID‐19 disease has been analysed for India, Pakistan, Myanmar (Burma), Brazil, Italy and Germany till June 4, 2020 and predictions have been made for the number of positive cases for the next 28 days. Verhulst model fitting effect is better than Gompertz and SIR model with R‐score 0.9973. The proposed model perform better as compare to other three existing models with R‐score 0.9981.These above models can be adapted to forecast in long term intervals, based on the predictions for a short interval as of June 5, 2020 and June 30, 2020, active COVID‐19 patients for India, Pakistan, Italy, Germany, Brazil and Myanmar predicted as (236,170, 88,998, 234,066, 184,922, 645,057 and 235) and (486,357, 218,864, 240,545, 193,727, 1,211,567 and 309).

A smart city refers to a city equipped with the basic infrastructure to provide a good quality of life and a clean and sustainable environment to its citizens using smart technology-based solutions. It is a smart way to provide the best services to its residents and develop the infrastructure. The smart city focuses on controlling available resources safely, sustainably, and efficiently to improve the economy and societal outcomes. People, systems, and things in the cities generate data. However, their heterogeneity makes it difficult to publish, organize, discover, interpret, combine, analyse, and consume them. The next generation of these technologies, including the 6G and intelligent internet of things (6G/IIoT), have been recently proposed aiming to provide endless networking capabilities to the city users. General estimates revealed that the number of smart IoT devices would approach over 50 billion by 2022. With the proliferation of smart IoT devices, smart applications are expected to lead to further innovation in 6G/IIoT-oriented cities. This special issue aims to stimulate discussion on the IoT and Big Data analytics for future smart cities with smart applications including smart health, smart governance, smart homes, and smart buildings, smart mobility and transportation, smart factories, and smart data-driven decision-making. In this special issue, each paper was reviewed by three or more experts during the assessment process. After evaluating the overall scores, six papers were selected for inclusion in this special issue. The selected papers present in-depth studies of practical issues and challenging problems in Big Data Analytics with IoT-oriented Infrastructures for Future Smart Cities. This paper (Haque et al., 2022) focuses on the overview and conceptual development of the smart city. Initially, the work discusses the smart city idea and fundamentals explored in various pieces of literature. Further various smart city applications along with notable implementations are put forth to understand the quality of living standards. This article (Zhang & Liu, 2022) conducts APP page management through technical analysis of mobile devices and user experience-oriented design. The framework design of the browser and the construction of the interface MVC design mode, and then through the questionnaire survey method to the user experience needs and expectations of the medical APP to design the main key of the page function. In this paper (Galyan et al., 2022), simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range-free methods. The proposed technique is useful for mapping several instances like fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities, and so forth. In this work (Jain et al., 2022), the authors have applied logistic regression, decision tree, support vector machine, linear discriminant analysis, quadratic discriminant analysis, naïve Bayes, random forest, and k-nearest neighbour algorithms to predict the stability of the grid. The authors have used the smart grid stability data set freely available on Kaggle to train and test the models. This work (Mandloi & Arya, 2022) proposed a Machine Programming based approach for the deployment of 5 G-enabled UmBSs. The centroid-based clustering algorithms such as; K-means, K-medoid, and FCM were proposed to find the centroid of the cluster. Then, the UmBSs were deployed at the centroid location of each cluster. This paper (Dixit et al., 2022) presents a systematic outlook of AI techniques in anomaly detection of AEVs. A solution taxonomy is proposed based on research gaps in existing surveys, and the evaluation metrics for AI-based anomaly detection are discussed. The open challenges and issues in AI deployments are discussed and a case study is presented on anomaly classification through a weighted ensemble technique.

Abstract The delay in transportation of necessary items is due to traffic congestion throughout the world. This is a serious phenomenon which results in waste of time and fuel. The detection of road conditions and dissemination of traffic information efficiently and effectively is a big challenge to authorities. Recently, the technologies of vehicular ad hoc networks (VANETs) have been utilized and become an important part of the intelligent transportation system (ITS). For this existing problem, vehicle‐to‐vehicle (V2V) communication provides a means for cooperation and route management in transport networks. This paper proposed a novel congestion detection system based on the combination of k‐means clustering and analytical hierarchy process. In the simulation of urban mobility (SUMO) simulator, a transport network is created and parameters of vehicles facing congestion are taken to extract the key parameter by using the k‐means clustering technique and mathematical mean algorithm. This parameter is utilized in analytical hierarchy process to detect the highest priorities parameter and based on that the congestion is detected in particular lane. The result can be a better technique for congestion detection as it requires low installation cost and can be incorporate in vehicles for congestion avoidance which will alternatively improve the traffic flow.

Summary The vision of Industry 4.0 is characterized by the amalgamation of cyber‐physical systems and industrial Internet of Things. Such a complex ecosystem urges for the requirement of novel security protocol and mechanisms for access control so as to allow the smart devices to authorize external entities and granting them access rights without depending on centralized authentication entities. The work proposed in this article aims to utilize a community‐based hierarchical approach to define the procedure for obtaining access rights in the Industry 4.0 ecosystem. The proposed scheme considers a hierarchy of authorizing devices that work in collaboration for providing access control of the smart end devices to the users. The adoption of hierarchical structure ensures that the access rights are eventually given to only those users that have passed multiple levels of successful authorization. The proposed scheme also combats any infringement of users identity since the authorizing entities involved in the proposed system work in close collaboration for user authentication. The proposed user authentication scheme has been validated using burrows‐abadi‐needham (BAN)‐logic and is proved to be secure against a variety of security attacks.

The fifth industrial revolution (Industry 5.0) integrates humans and machines to satisfy the increasing customization demands of the manufacturing complexity using an optimized robotized manufacturing process. Industry 5.0 make use of collaborative robots (cobots) for optimizing productivity and ensuring safety. At the same time, unmanned aerial vehicles (UAVs) are predicted to be the main part of industry 5.0 in the forthcoming days. Regardless of high mobility and energy-limited UAVs for wireless communication as significant advantages, different issues are also existing in the UAV networks, such as security, reliability, etc. Several research works have focused on resolving security issues in UAV communication to support safety-critical applications. With this motivation, this article presents an artificial intelligence-based UAV-borne secure communication with classification (AIUAV-SCC) framework for industry 5.0 environment. The proposed AIUAV-SCC model involves two major phases namely image steganography-based secure communication and deep learning (DL)-based classification. At the initial stage, a new image steganography technique with multilevel discrete wavelet transformation, quantum bacterial colony optimization based optimal pixel selection, and encryption processes take place. Next, in the second stage, the Bayesian optimization (BO)-based SqueezeNet model is applied for the classification of securely received UAV images where the parameters in the SqueezeNet method are optimally tuned by the utilize of the BO technique. To validate the performance of the presented model, extensive simulations are applied using the UC Merced dataset (UCM) aerial dataset and the outcomes are investigated under several dimensions. The outcomes make sure the goodness of the presented model on test UCM aerial dataset over the compared methods.

With the advancement of information and communication technologies (ICTs), there has been high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services, and provide many extensive benefits to the commuters and transportation authorities. In this article, we propose a novel edge-based AI-IoT integrated energy-efficient intelligent transport system for smart cities by using a distributed multi-agent system. An urban area is divided into multiple regions, and each region is sub-divided into a finite number of zones. At each zone an optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects a huge volume of data from the various sensors, devices, and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze, and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain smooth and steady movement, which results in reduction in GHG emissions and energy consumption, and finally improves the freight vehicles’ mileage by reducing traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

Summary The purpose of this paper is to compare the end results generated after usability assessment by two different ways. The assessment of top‐fifty academic websites is done by employing the tool and by involving end‐users. This research work is performed to assess latest heuristic guidelines for academic websites under three different categories i.e. {Social Media, Mobile and Security} by involving a hundred end‐users as well as by evaluating the websites using the tool. Association rule is implemented on the collected qualitative data to recognize common usability problem patterns pertaining to academic websites but from two uncommon perspectives. This study further compares these problematic patterns to provide more comprehensive analysis. Our findings investigate that most of the usability problems have been uncovered by the end‐users on academic websites that are not detected by tool. In other words, there are several key features that these tools are failed to evaluate. On the contrary, end‐users can help in evaluating these features and uncover remarkable usability problematic issues on any interactive system.

Abstract Plant leaves play an important role in the diagnosis of plant diseases. Losses from such diseases can have a significant economic as well as environmental impact. Thus, examination of leaves into a healthy or infected carries substantial importance. An improved artificial plant optimization (IAPO) algorithm using machine learning has been introduced that identifies the plant diseases and categorize the leaves into healthy and infected on a private dataset of 236 images. Features are extracted from the images using histogram of oriented gradients (descriptor). The concepts of artificial plant optimization are then applied to study the features of healthy leaves using IAPO. A machine learning algorithm has been created to make the model adaptive with varied datasets. The degree of infection is eventually computed, and the leaves with infection greater than a certain calculated threshold are classified as infected leaves. The results show that IAPO can be used for classification of infected and healthy leaves and this algorithm can be generalized to solve problems in other domains as well. The proposed IAPO is also compared with other classification algorithms including k‐nearest neighbours, support vector machine, random forest and convolution neural network that show accuracies of 78.24%, 83.48%, 87.83%, and 91.26%, respectively, whereas IAPO shows quite accurate results in classification of leaves with an accuracy of 97.45% on training set and 95.0% accuracy on test set.

The aim of this study is to propose a model based on machine learning (ML) and Internet of Things (IoT) to diagnose patients with COVID-19 in smart hospitals. In this sense, it was emphasized that by the representation for the role of ML models and IoT relevant technologies in smart hospital environment. The accuracy rate of diagnosis (classification) based on laboratory findings can be improved via light ML models. Three ML models, namely, naive Bayes (NB), Random Forest (RF), and support vector machine (SVM), were trained and tested on the basis of laboratory datasets. Three main methodological scenarios of COVID-19 diagnoses, such as diagnoses based on original and normalized datasets and those based on feature selection, were presented. Compared with benchmark studies, our proposed SVM model obtained the most substantial diagnosis performance (up to 95%). The proposed model based on ML and IoT can be served as a clinical decision support system. Furthermore, the outcomes could reduce the workload for doctors, tackle the issue of patient overcrowding, and reduce mortality rate during the COVID-19 pandemic.

The statistics highlights that physical rehabilitation are required nowadays by increased number of people that are affected by motor impairments caused by accidents or aging. Among the most common causes of disability in adults are strokes or cerebral palsy. To reduce the costs preserving the quality of services new solutions based on current technologies in the area of physiotherapy are emerging. The remote monitoring of physical training sessions could facilitate for physicians and physical therapists' information about training outcome that may be useful to personalize the exercises helping the patients to achieve better rehabilitation results in short period of time process. This research work aims to apply physical rehabilitation monitoring combining Virtual Reality serious games and Wearable Sensor Network to improve the patient engagement during physical rehabilitation and evaluate their evolution. Serious games based on different scenarios of Virtual Reality, allows a patient with motor difficulties to perform exercises in a highly interactive and non-intrusive way, using a set of wearable devices, contributing to their motivational process of rehabilitation. The system implementation, system validation and experimental results are included in the paper.

Public transport systems in a metropolitan area experiences several complex issues, like resource scarcity, resource allocation, congestion, resource reliability and so on, due to the dynamic arrivals of heterogeneous commuter and exceptional occurrence of unforeseen events. The progress of these issues may lead to economic losses, under-utilization of transport resources, and commuters’ queuing delay. In this paper, we propose a novel dynamic public transport vehicle allocation scheme based on Emergent Intelligence (EI) technique in a metropolitan area. In addition, we demonstrate the EI technique’s capability for solving public transport system problems. To do so, the EI technique maintains historical information, commuters’ arrival rates, resource avaialability, deficit resources and surplus resources of neighbor depots’s agent. In the proposed scheme, the EI technique is utilized to collect, analyze, share and optimally allocate transport resources effectively. The proposed EI technique provides reliable services (allocation and scheduling) by coordinating with a reliable neighborhood depot’s agent. We have build mathematical models for estimation of resources, utilization and reliability parameters. The proposed scheme is exhaustively tested by simulation and analyzed with varying commuters’ arrival rates, number of vehicles, number of requests, and different values of reliability parameters. The proposed scheme’s results (analytical, simulation and comparison) show the reliabiltiy, accuracy and real time deployability.

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

The COVID-19 pandemic has wreaked havoc all over the world. The rising number of cases have overburdened healthcare systems even in the most developed countries. To ease the burden on healthcare systems a quick and efficient testing technique is needed. Currently, the RT-PCR testing is done with time consuming and laborious an alternative is a detection from Chest X-Ray images. It has been discovered in published studies that Chest X-Rays of COVID-19 patients have specific malformations that can be used to identify a positive case. Inspired by the work done on “COVID-Net” by Linda Wang, Zhong Qiu Lin and Alexander Wong, a Deep Learning approach to detect coronavirus from Chest X-Ray images is used in this study. To surpass previous results the EfficientNet Convolutional Neural Network (CNN) model is proposed. This model not only achieves +2% accuracy, but it also attains higher sensitivity and Positive Predictive Values. The study uses the open source COVIDx dataset. It has approximately 14,000 X-Ray images. To the best of authors' knowledge, this dataset contains the largest number of COVID-19 positive cases. The study offers a Deep Learning approach contributing to create an efficient COVID-19 detector that can be used in the real world.

Summary The security of digital images is an essential and challenging task on shared communication Model. Generally, high secure working environment and data are also secured with an encryption and decryption method by using secret and public keys. In this paper, the innovative encryption technique for image security, ie, Multiple key‐based Homomorphic Encryption (MHE) technique is proposed. For increasing the security level of encryption and decryption processes, the optimal key is selected using Adaptive Whale Optimization (AWO) algorithm. Fitness function was considered for optimization as PSNR of plain and cipher images. The original image was transformed into blocks and then rearranged utilizing encryption process, this work achieved maximum security, much better than other encryption techniques. From the outcomes, one can achieve incredible quality of the proposed model, the maximum PSNR, and the minimum MSE contrasted with other encryption schemes.

The public transportation system (PTS) in a metropolitan area is a nonlinear, dynamic, and complex system. Managing and providing suitable public transportation services are difficult. In this article, we propose an Internet of Things-based intelligent PTS (IoT-IPTS) in a metropolitan area. An IoT is used to interconnect transportation entities, such as vehicles, commuters (mobile phones), routes (sensors), roadside units (RSUs), etc., in a metropolitan area. The IoT provides the seamless connectivity between different networking technologies whenever the commuters or vehicles move from one location to another location. Hence, IoT provides the suitable seamless public transportation services in the metropolitan area. In addition, we have used context information of transportation entities, such as routes condition, traffic density, number of routes available, traffic congestion, vehicles' movement, and their mobility, which are stored in the cloud. The stored context information in cloud along with the IoTs are used to find the relevant routes, alternative modes, departure times, and many more for providing public transportation services in a metropolitan area. The proposed IoT-IPTS makes use of static and mobile agents with the emergent intelligence technique (EIT) for collecting, analyzing, and sharing context information. The analyzed context information is used to form the policies to provide the best available public transportation services to the commuters in a metropolitan area. The software-defined network is used to enable the cloud computing and EI network to manage the public transportation services to the commuters.

In a neural network, the weights act as parameters to determine the output(s) from a set of inputs. The weights are used to find the activation values of nodes of a layer from the values of the previous layer. Finding the ideal set of these weights for training a Multi-layer Perceptron neural network such that it minimizes the classification error is a widely known optimization problem. The presented article proposes a Hybrid Wolf-Bat algorithm, a novel optimization algorithm, as a solution to solve the discussed problem. The proposed algorithm is a hybrid of two already existing nature-inspired algorithms, Grey Wolf Optimization algorithm and Bat algorithm. The novel introduced approach is tested on ten different datasets of the medical field, obtained from the UCI machine learning repository. The performance of the proposed algorithm is compared with the recently developed nature-inspired algorithms: Grey Wolf Optimization algorithm, Cuckoo Search, Bat Algorithm, and Whale Optimization Algorithm, along with the standard Back-propagation training method available in the literature. The obtained results demonstrate that the proposed method outperforms other bio-inspired algorithms in terms of both speed of convergence and accuracy.