Abstract Generally, the existing Intrusion Detection Systems (IDS) solutions suffer from low detection accuracy for some attack types compared with the overall detection accuracy of attacks. The data imbalance technically affects the ratio of detection accuracy of low frequent attacks class (e.g. zero‐day attack) compared to attacks with more instances. Therefore, IDS‐based machine learning algorithms potentially suffer from high false‐positive rates. To overcome the limitation of existing solutions, a hyper‐clustering model is proposed for dynamic intrusion detection based on the Density‐Based Spatial Clustering of Applications with Noise (DBSCAN) and cosine similarity. The proposed solution develops the standard DBSCAN by adding a new evolving process based on distance measures between the clusters to overcome the imbalance dataset. Moreover, a new classifier is proposed based on cosine similarity to predict the labelling of abnormal behaviour. The experimental results show that the proposed model outperformed the original DBCAN and the related works. The mean silhouette of the proposed DBSCAN achieves a high score of 0.87 compared to other solutions. Furthermore, the proposed DBSCAN reduces the mean square error from 0.66 to 0.13 and achieves 86.82%, 79.10% and 90.03% in general accuracy on KDDTest+, KDDTest‐21 NSL‐KDD and UNSW‐NB15 benchmark datasets, respectively.

Increases in population and prosperity are linked to a worldwide rise in garbage. The “classification” and “recycling” of solid waste is a crucial tactic for dealing with the waste problem. This paper presents a new two-layer intelligent decision system for waste sorting based on fused features of Deep Learning (DL) models as well as a selection of an optimal deep Waste-Sorting Model (WSM) based on Multi-Criteria Decision Making (MCDM). A dataset comprising 1451 samples of images of waste, distributed across four classes – cardboard (403), glass (501), metal (410), and general trash (137), was used for sorting. This study proposes a Multi-Fused Decision Matrix (MFDM) based on identified fusion score level rules, evaluation criteria, and deep fused waste-sorting models. Five fusion rules used in the sorting process and the evaluation perspectives into the MFDM are sum, weighted sum, product, maximum, and minimum rules. Additionally, each of entropy and Visekriterijumska Optimizacija i Kompromisno Resenje in Serbian (VIKOR) methods was used for weighting selected criteria as well as ranking deep WSMs. The highest accuracy rate of 98% was scored by ResNet50-GoogleNet- Inception based on the minimum rule. However, under the same rule, an insufficient accuracy rate of sorting was presented by ResNet50-GoogleNet-Xception. Since Qi = 0 for Inception-Xception, the final output based on MCDM methods indicates that the fused Inception-Xception model outperforms the other fused deep WSMs, which achieved the lowest values of Qi. Thus, Inception-Xception was chosen as the best deep waste-sorting model based on images of waste, multiple evaluation criteria, and different fusion perspectives. The mean and standard deviation metrics were both used to validate the selection findings objectively. The suggested approach can aid urban decision-makers in prioritizing and choosing an Artificial Intelligence (AI)-optimized optimal sorting model.

Industrial cyber–physical systems (ICPS) are emerging platforms for various industrial applications. For instance, remote healthcare monitoring, real-time healthcare data generation, and many other applications have been integrated into the ICPS platform. These healthcare applications encompass workflow tasks, such as processing within hospitals, laboratory tests, and insurance companies for patient payments, which necessitate a sequential flow. The external wireless, fog, and cloud services within ICPS face security issues that impact end-users’ healthcare applications. Blockchain technology offers an optimal solution for ICPS-enabled applications. However, blockchain technology for the ICPS platform is still vulnerable to cyberattacks, while microservices are essential for executing applications. This paper introduces the novel “Pattern-Proof Malware Validation” (PoPMV) algorithm designed for blockchain in ICPS. It exploits a deep learning model (LSTM) with reinforcement learning techniques to receive feedback and rewards in real-time. The primary objective is to mitigate security vulnerabilities, enhance processing speed, identify both familiar and unfamiliar attacks, and optimize the functionality of ICPS. Simulations demonstrate the superiority of the proposed approach compared to current blockchain frameworks, showcasing dynamic allocation of microservices and improved security with comprehensive attack detection by 30%.

The selection of antivirus masks is an important problem in the context of the ongoing COVID-19 pandemic. Multiple attribute decision-making (MADM) algorithmic approaches can be used to evaluate and compare different masks based on multiple criteria, such as effectiveness, comfort, and cost. An aggregation of interval-valued multi-fuzzy hypersoft sets provides a flexible framework for handling uncertainty and imprecision in the MADM process. This approach allows for the integration of multiple sources of information such as expert opinions and empirical data, and considers the different levels of uncertainty and ambiguity associated with each criterion. By using the matrix-manipulated aggregation of interval-valued multi-fuzzy hypersoft sets like the induced fuzzy matrix, α-level matrix, threshold matrix, and mid-threshold matrix, an algorithm is proposed for the optimal selection of material for manufacturing antivirus masks. The robustness of the algorithm is maintained by following simple computation-based stages that enable a wide range of multidisciplinary readers to understand the idea vividly. By using this algorithm, it is possible to improve the accuracy and reliability of the decision-making process and to better balance the trade-offs between the different criteria, i.e., the computed results of the proposed algorithm and the structural aspects of the proposed approach are both compared with some relevant existing structures. Computation-based and structural comparisons are presented to assess the adaptability and reliability of the study. The first one is meant to check reliability, while the second is meant to check flexibility. In both cases, however, the presented approach yields the required standard. By comparing the prospective structure to the relevant developed model, the implications of the proposed framework are explored.

This paper presents the augmented Internet of Things (AIoT) framework for cooperatively distributed deep blockchain-assisted vehicle networks. AIoT framework splits the vehicle application into various tasks while executing them on different computing nodes. The vehicle application has different constraints, such as security, time, and accuracy, which are considered during processing them on parallel computing nodes (e.g., fog and cloud). We propose a partitioned AIoT scheme, dividing vehicular tasks into local and remote tasks. The objective is to minimize delays and efficiently execute urgent tasks such as vehicle, pedestrian, and traffic signals on local vehicles. The existing blockchain technologies suffer from many security issues, such as anonymous node issues and malware attacks in blockchain blocks. This is why we present the combined deep convolutional neural network (DCNN)-assisted proof-of-trust miner (PoTM) scheme. It safely handles tasks in different blocks. The smart contract is a human-written piece of code in blockchain technologies so that malicious code can be integrated into blockchain blocks during the registration of vehicles among nodes. The main limitation of smart contracts is that they are not changeable and cannot be changed once executed for any block. To avoid this situation, we present an augmented adaptive Trust Management Credibility Score Scheme (TMCSS) scheme that registers the vehicles before starting any services at blockchain miners. These registration certificates are changeable once DCNN detects any malicious activity in the vehicle data. Simulation results show that the proposed schemes improved delays by 35%, reduced the failure ratio of transactions by 39%, and enhanced overall transactions with the minimum failure compared to existing blockchain technologies for road-cooperation services in networks.

Alzheimer's disease (AD) is a memory-related disease that occurs in the human brain where neurons become degenerative. It is an evolved form of dementia that deteriorates over time. Machine learning, an extended version of deep learning, has appeared as an optimistic strategy for AD detection. Regardless, the existing AD detection approaches have yet to acquire the expected accuracy, mainly due to unreasonable data for training and testing. In this paper, we present the Federated Deep Convolutional Neural Network Alzheimer Detection Schemes (FDCNN-AS), specifically designed for varying age groups. FDCNN-AS is an efficient framework that contains architecture, algorithm flow, and implementation. It manages AD data from various laboratories and processes it in additional clinics. Our method mixes training data models from different types of data such as positron emission tomography, summed tomography, magnetic resonance imaging, blood tests, and questionnaires about synaptic degeneration. Further, we look at some restrictions that have yet to be addressed in AD detection. These include seeing AD at different ages, extrapolating the severity of brain damage, comparing treatment and recovery rates, and finding benign and malignant ranges in AD data that has been collected. To ensure secure and privacy-preserving learning, we execute FDCNN-AS within a federated learning environment that concerns considerable laboratories and clinics. Within this setup, we operate the generic deep convolutional neural network. The experimental results indicate that FDCNN-AS performs optimally, reaching a remarkable 99% accuracy in detecting dementia Alzheimer's in the human brain.

The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in the presented V2I paradigms regarding security, cyber-attacks, and application processing among heterogeneous nodes. These cyber-attacks, Sybil attacks, and their attempts cause a lack of security and degrade the V2I performance in the presented paradigms. This paper presents a new secure blockchain framework that handles cyber-attacks, as mentioned earlier. This paper formulates this complex problem as a combinatorial problem, encompassing concave and convex problems. The convex function minimizes the given constraints, such as time and security risk, and the concave function improves performance and accuracy. Therefore, numerous constraints, such as time, energy, malware detection accuracy, and application deadlines, require optimization for the considered problem. Combining the jointly non-dominated sorting genetic algorithm (NSGA-II) and long short-term memory (LSTM) schemes is the best way to meet the problem’s limitations. In this study, the paper designed a malware dataset with known and unknown malware. The different kinds of malware lists (e.g., cyber-attacks) are considered in the form of known and unknown malware lists with the characteristics, size of code, where malware comes from, attack on which data, and current status of the workload after being attacked by the malware. Our main idea is to present blockchain, NSGA-II, and LSTM schemes that handle phishing, routing, Sybil, and 51% of cyber-attacks without compromising application performance. Simulation results show that the study reduces delay and energy, improves accuracy, and minimizes security risks for vehicular applications.

The Internet of things (IoT) is a network of technologies that support a wide variety of healthcare workflow applications to facilitate users' obtaining real-time healthcare services. Many patients and doctors' hospitals use different healthcare services to monitor their healthcare and save their records on the servers. Healthcare sensors are widely linked to the outside world for different disease classifications and questions. These applications are extraordinarily dynamic and use mobile devices to roam several locales. However, healthcare apps confront two significant challenges: data privacy and the cost of application execution services. This work presents the mobility-aware security dynamic service composition (MSDSC) algorithmic framework for workflow healthcare based on serverless, serverless, and restricted Boltzmann machine mechanisms. The study suggests the stochastic deep neural network trains probabilistic models at each phase of the process, including service composition, task sequencing, security, and scheduling. The experimental setup and findings revealed that the developed system-based methods outperform traditional methods by 25% in terms of safety and 35% in application cost.

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Abstract Images captured through a visual sensory system are degraded in a foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools are needed to detect, pre‐process, and enhance foggy scenes. Machine learning (ML) has a significant role in image defogging domain for tackling adverse issues. Unfortunately, regardless of contributions that were made by ML, little attention has been attributed to this topic. This paper summarizes the role of ML methods and relevant aspects in the image defogging research area. Also, the basic terms and concepts are highlighted in image defogging topic. Feature extraction approaches with a summary of advantages and disadvantages are described. ML algorithms are also summarized that have been used for applications related to image defogging, that is, image denoising, image quality assessment, image segmentation, and foggy image classification. Open datasets are also discussed. Finally, the existing problems of the image defogging domain in general and, specifically related to ML which need to be further studied are discussed. To the best knowledge, this the first review paper which sheds a light on the role of ML and relevant aspects in the image defogging domain.

Sensor-aware distributed workflow applications are becoming increasingly popular underwater. The apps are marine operations that generate data and process it based on its characteristics. Mobile-fog-cloud paradigms, as well as computing such as sensor nodes, have emerged. As previously stated, the nodes can be combined into a single system to achieve several goals. Many factors are considered, including network contents, workload fluctuation, variable execution durations, deadlines, and bandwidth. As a result, scheduling mobile workflow systems with multiple parameters might be challenging. The study suggests a novel content-efficient decision-aware task scheduling (CATSA) method for defining and adapting to complicated environmental changes. The CATSA consists of several components that work together to perform various benchmarks in the system, including a decision planner, sequencing, and scheduling. As evidenced by test findings during evaluation, the suggested architecture outperforms current studies regarding workflow execution quality of services and improved the makespan 30% and deadline meeting 40% in the study.

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.

Parkinson's disease (PD) diagnostics includes numerous analyses related to the neurological, physical, and psychical status of the patient. Medical teams analyze multiple symptoms and patient history considering verified genetic influences. The proposed method investigates the voice symptoms of this disease. The voice files are processed, and the feature extraction is conducted. Several machine learning techniques are used to recognize Parkinson's and healthy patients. This study focuses on examining PD diagnosis through voice data features. A new multi-agent feature filter (MAFT) algorithm is proposed to select the best features from the voice dataset. The MAFT algorithm is designed to select a set of features to improve the overall performance of prediction models and prevent over-fitting possibly due to extreme reduction to the features. Moreover, this algorithm aims to reduce the complexity of the prediction, accelerate the training phase, and build a robust training model. Ten different machine learning methods are then integrated with the MAFT algorithm to form a powerful voice-based PD diagnosis model. Recorded test results of the PD prediction model using the actual and filtered features yielded 86.38% and 86.67% accuracies on average, respectively. With the aid of the MAFT feature selection, the test results are improved by 3.2% considering the hybrid model (HM) and 3.1% considering the Naïve Bayesian and random forest. Subsequently, an HM, which comprises a binary convolutional neural network and three feature selection algorithms (namely, genetic algorithm, Adam optimizer, and mini-batch gradient descent), is proposed to improve the classification accuracy of the PD. The results reveal that PD achieves an overall accuracy of 93.7%. The HM is integrated with the MAFT, and the combination realizes an overall accuracy of 96.9%. These results demonstrate that the combination of the MAFT algorithm and the HM model significantly enhances the PD diagnosis outcomes.