ABSTRACT Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content‐Based Image Retrieval (CBIR) research. Even in Google searches, most of the time the query results are provided with inaccurate results or duplicate results due to the minor variations between apparel. Another major challenge is that the extracted feature vector dimensions are too high and difficult to handle. In this paper, a method named Multifeature Representation with Maximum Correlation‐based Feature Fusion, and Matching (MFR‐MCF2M) is proposed for apparel retrieval. This method consists of three modules: (1) Multifeature Representation Module (MFR‐M), (2) Maximum Correlation‐based Feature Fusion Module (MCF2‐M) and (3) Multifeature Matching Module (MFM‐M). In the MFR module, the shape, texture and deep features of apparel images are extracted using a Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and a pretrained deep CNN model, respectively. Also, the dimensionality of extracted features is reduced using the proposed Feature Subselection (FSS) method. The MCF module is implemented to measure the maximum correlation between reduced feature vectors. Finally, MCF2 is performed using Euclidean distance and a generated Feature Correlation Vector (FCV) to improve the retrieval accuracy and as the benchmark to assess the efficacy of the proposed method. In addition, a new large‐scale dataset named Apparel Images Gallery (AIG), which consists of 130,000 images, has been provided to the community. The performance of the proposed MFR‐MCF2M method is evaluated on three datasets, including two publicly available datasets and the proposed AIG dataset. The retrieval results are obtained after passing through the threshold function of both the Euclidean distance and the computed FCV. The proposed method achieved an accuracy of 78.3% on the clothing dataset, 94.8% on the CR dataset and 89.1% on the proposed AIG dataset. Consequently, the MFR‐MCF2M outperformed state‐of‐the‐art (SOTA) apparel retrieval methods.

Abstract Traditional healthcare systems have suffered from different data communication, security, data processing, and compliance issues. The traditional systems are also not well equipped to handle the new technologies like Artificial Intelligence (AI) by enabling more accurate diagnostics, personalized treatment plans, and improved patient outcomes. The existing data communication and security protocols and compliance are also not fully implemented to tackle the system's challenges. This article proposes a Tri‐Tier architecture by using data communication, AI data generative, and regulation and compliance tiers. The data communication tier is based on advanced sensing and monitoring technologies like cloud and edge‐based systems integrated with security detection mechanisms. The edge and cloud layer provides the all functions of the perception layer like smart sensing, visual sensing, and monitoring services, and can control the device's perception and behaviour. The second tier provides the AI data generative functionalities to handle real‐time synthetic medical images for predictive analytics to enhance patient care. This tier also automates routine tasks, such as administrative work and data analysis, which can free up healthcare professionals to focus on more complex tasks. The last regulation and compliance tier is responsible for handling the standards and compliance for healthcare systems. Experiments are conducted to test the data communication and security level of the proposed architecture. The results showed the suitability of existing solutions and synchronization with the proposed architecture.

Intelligent data analysis rapidly transforms healthcare care by improving patient care and predicting health outcomes through machine learning (ML) techniques. These advanced analytical methods allow intelligent healthcare systems to process large amounts of health data, improving diagnosis, treatment, and patient monitoring. The success of these systems is highly dependent on the quality and balance of the data they analyze. Class imbalance, a situation where certain classes dominate the dataset, can significantly affect the accuracy and effectiveness of ML models. In healthcare, it is not only crucial, but urgent, to accurately represent all conditions, including rare diseases, to ensure proper diagnosis and treatment. For this analysis, data was gathered from six reputable academic databases: ScienceDirect, IEEE Xplore, Scopus, Web of Science, Google Scholar, and PubMed. This review offers a comprehensive overview of current approaches to handling class imbalance, including data preprocessing methods like oversampling, undersampling, hybrid techniques, and ensemble learning strategies such as bagging, boosting, and AdaBoost. It also addresses the limitations of these methods and the ongoing challenges in effectively managing class imbalance in healthcare data. Furthermore, the review explores innovative and promising strategies that have shown success in overcoming class imbalance, with a particular emphasis on fairness, diversity, and ethical considerations, offering a hopeful outlook for the future of healthcare data analysis. The discussion highlights how class imbalance can impact the accuracy and reliability of intelligent healthcare systems, underscoring its significance in improving patient care, healthcare delivery, and the broader medical community.

Power Line Communication (PLC) is categorized into wired and wireless technologies to distribute the power and transmit the data at different frequency ranges. System administration is one of the significant area in these networks to manage communication processes. Security is one of the significant concern which make networks slow and unavailable, false and altered instructions exist, malfunctioning, and abnormal behavior of systems observed. Intrusion Detection System (IDS) is one of the solution to handle security attacks and protect the systems from unauthorized access. However, the existing IDS systems have limited capabilities to handle the new attacks. This paper proposes a Machine Learning (ML) algorithm for IDS system used in PLC networks to improve the overall system performance and detect the vulnerabilities of the system. The proposed system can detect the latest assaults and protect the systems from unauthorized and malicious activities. The proposed IDS system is assessed by using a virtual environment using the latest dataset and compared with existing traditional systems. The experiment results indicated the better performance of the proposed system to handle the new assaults and protect the systems.

Summary Text classification is one of the most important tasks to extract information from the Internet and identifying the best text representation settings. With the increase of data volume on the world wide web, the significance of text classification increases. This situation requires huge human efforts to understand and classify the digital data available on the Internet. Text classification is classifying the number of text files into different classes. The data or text available on the Internet is in an unstructured form which increases the difficulty to understand and classify it for useful purposes. This paper proposes a context‐aware text classification system to improve text quality. We use a content‐aware recommendation system to extract the data from well‐known news databases. Text preprocessing techniques like tokenization, stemming, and stop words removal are studied in detail. Furthermore, unigram, bigram, and trigram attributes are also being tested. Attribute selection methods are also examined and their impact on the text classification results. To carry out a detailed investigation, 11 versions are created of each dataset to save the time in experimentation process and applied the different preprocessing techniques to understand the impact of each technique on classification results. The proposed system is compared with the existing approach to check the accuracy where the proposed system achieved better performance.

In the pharmaceutical industry, problems like counterfeit drugs, including vaccines, and their supply chain management problems like transparency, immutability, and traceability exist. In the case of vaccines, it becomes more difficult to standardize and detect fake vaccines because the public has less awareness and knowledge about vaccines. Moreover, the increase in online pharmacies gives more opportunities for counterfeiting vaccines to enter the authentic supply chain management system. We present transparent, immutable and secure vaccine supply chain (TISVSchain), a framework based on blockchain to handle the issues of counterfeited vaccines and vaccine supply chain problems like transparency, immutability, and traceability. Our proposed framework can run both on the private and public blockchain. We have implemented the framework on public blockchain by using remix ide and the smart contracts designed by solidity language run on very low gas cost. We also carried out several experiments by changing the number of nodes and their block time to evaluate the performance of our framework in terms of transaction per second (TPS), gas cost, and propagation delay. Our proposed framework improves the security by using offline unique account addresses in blockchain-based frameworks and improves the overall efficiency of the framework by keeping the gas cost low, finding a way to decrease the number of lost blocks to keep low propagation delay, and keeping high TPS value. TISVSchain shows us promising results to improve vaccine supply chain management’s overall performance, security, and efficiency.

Intelligent Transportation Systems (ITS) have gained popularity due to smart services and applications to facilitate the users on the roads. The increasing growth of users in these networks created new and complex data processing, storage, security, and privacy concerns. These networks are using centralized edge, fog, or cloud architecture for data management. User privacy is compromised in these networks due to the increasing demands and service provider’s services. To ensure the data privacy, the centralized architectures are used without privacy regulations. In this paper, we present a Blockchain-based Privacy-Preserving Authentication (BPPAU) model for ITS networks to ensures users privacy and security. The proposed model provides data storage, data accessing, and processing management by using a blockchain smartcontract system, access control policy and on demand based functions. The proposed model is tested in a simulation environment to check its performance in terms of transaction cost with data size, transaction per second analysis with block time, and computational time analysis with several transactions.

Summary Structure Query Language Injection Attack is among the top 10‐security threats that can be used on the web application to cause severe damage or gain unauthorized data access to the application server. Many reports have indicated an average of 64% of global websites are at risk of being attack by SQL injection, and many of the top companies have experienced thousands of attacks attempts through SQL injection. The current trend shows the increasing number of attacks factor as a result of the daily deployment of these applications without security detection and prevention mechanism is placed. To overcome this challenge, researches in academia and industry presented a proposal that automates SQL injection vulnerabilities assessment on the tested application. Current studies show the need to enhance techniques of these proposals to reduce the false alarms. In this study, we propose a component‐based technique to minimize the incidence of inaccurate results, as well as enable the ease of improving the proposed solution. The study uses three costumed applications as tested to evaluate the accuracy of the proposed solution. Each of these testbed consists of several vulnerabilities where the experimental evaluation performs to test the proposed tool. An empirical evaluation is carried out on three vulnerable custom websites to evaluate the effectiveness of the proposed study. The experiment results indicated significant results in terms of high accuracy. On the other hand, the proposed solution also has better capabilities to analyze page response based on four different techniques. Moreover, the proposed solution is the only solution that performs stored procedure attacks SQL and bypass login authentication even if the returned records are limited restriction is applied.

Summar Software Defined Networks (SDN) is now the leading framework for the existing network infrastructure. Increasing Internet traffic leads to attract SDN infrastructure in large networks like enterprise or data centers by using logical centralize control concept. This abstraction, flexibility, and agility enable the network managers to view the global picture of the network and flow the traffic in an efficient way to avoid congestion and traffic delay issues. However, besides the benefits, the internal mechanism of SDN has some serious challenges, which leads flow table overflow, congestions, controller and switch overloading, link failure and latency issues. This research focus on the delays produced during communication between control plane and data plane due to the parameters like rule formation, mismatches, buffer/queue constraints, flow entries, controller resource utilization or duplicate flow packets, and unordered packets. These delays become more critical in large networks especially in‐case of reactive modes. Furthermore, the frequent rule composition and installation causes extra burden at the controller, this control communication needs prompt reply to forward the traffic in a stipulated time. This article presents the Efficient Resource Management Scheme (ERMS), which efficiently handle the inter‐communication delay and minimize the network overheads. The experiment results depict the better performance of ERMS during the communication between controller and switch by efficient packet handling and flow rules management while minimizes the overheads on controller. The proposed solution enhances the performance of SDN networks by improving the quality of services parameters.

Abstract According to the World Health Organization, cardiovascular diseases contribute to 17.7 million deaths per year and are rising with a growing ageing population. In order to handle these challenges, the evolved countries are now evolving workable solutions based on new communication technologies such as ambient assisted living. In these solutions, the most well-known solutions are wearable devices for patient monitoring, telemedicine and mHealth systems. This systematic literature review presents the detailed literature on ambient assisted living solutions and helps to understand how ambient assisted living helps and motivates patients with cardiovascular diseases for self-management to reduce associated morbidity and mortalities. Preferred reporting items for systematic reviews and meta-analyses technique are used to answer the research questions. The paper is divided into four main themes, including self-monitoring wearable systems, ambient assisted living in aged populations, clinician management systems and deep learning-based systems for cardiovascular diagnosis. For each theme, a detailed investigation shows (1) how these new technologies are nowadays integrated into diagnostic systems and (2) how new technologies like IoT sensors, cloud models, machine and deep learning strategies can be used to improve the medical services. This study helps to identify the strengths and weaknesses of novel ambient assisted living environments for medical applications. Besides, this review assists in reducing the dependence on caregivers and the healthcare systems.

Internet of connected vehicles (IoCV) is one of the popular subclasses of vehicle ad hoc networks and an up-rising form based on new Internet, 5G, cloud, and edge computing features. The vehicle nodes in such networks can exchange the information with other nodes with the help of infrastructure or without prior infrastructure. For efficient data communication among the vehicle nodes, the routing protocols play a significant role and able to handle the different network characteristics including high mobility, dynamic topologies, and disconnected links. To address the existing routing protocol issues including data delay, disconnection, interference, scalability, and overhead, this paper presents the Intersection Gateway and Connectivity based Routing (IGCR) protocol for IoCV networks. The proposed protocol uses important traffic-aware routing metrics, including traffic density and direction of nodes towards the destination for route and next forwarder node selection. The experimental results indicated that the proposed protocol well-behaved and showing better performance compared to the state of the art routing protocols in terms of data delivery, data delay, and data throughput.

New integrated technologies have changed various existing fields and converted into new and advanced data communication systems including, smart agriculture, smart homes, smart health, and smart transportation systems. Internet of Things (IoT) has evolved a new theme to vehicular networks field known as the Internet of Vehicles (IoV). This paper presents a comprehensive review and detailed background and motivation to evolve the heterogeneous vehicular networks. Paper also proposed new integrated models and key technologies related to network maintenance, a six-layered architecture model based on protocol stack and network elements, network model based on cloud services, big data analytical model based on data acquisition and analytics, security model based on detection and prevention systems. Paper also envisioned existing challenges and future directions to design the new integrated models.

Summary The transformation of traditional networks is being done by incorporating billions of daily life devices to provide service centric facilities. With such transformation, the major traffic load will be shifted toward the backhaul networks, for which guaranteed bandwidth and low latency are the basic requirements. In order to meet varying and dynamic requirements of each service, the development of a traffic aware network is unavoidable. For achieving last mile connectivity, wireless mesh is considered among the best of the backhaul networks. Additionally, classical single radio mesh routers restrict the whole network on a single channel and hence the full potential of available multiple channels is not achieved. Mesh routers plugged with multiple radios allow parallel transmissions and increase the capacity of the whole network. To utilize network resources more efficiently, the key issue of channel assignment for wireless mesh networks is explored by incorporating the concept of time‐based traffic in a distributed environment. This paper discusses the problem of assigning a limited number of channels to a large number of radios while keeping in view the restrictions involved in maintaining a minimal level of interference and preservation of network topology. Bayesian estimation approach is used to gather knowledge from surroundings to determine the high‐interfered region and hence a distributed solution is proposed where mesh routers can find a more suitable alternative channel for respective region. The proposed algorithm is evaluated through traces on multiple flows, collected from simulations. Results show that the proposed algorithm performed better than existing ones in the presence of interference.

Geocasting in vehicular communication has witnessed significant attention due to the benefits of location oriented information dissemination in vehicular traffic environments. Various measures have been applied to enhance geocasting performance including dynamic relay area selection, junction nodes incorporation, caching integration, and geospatial distribution of nodes. However, the literature lacks toward geocasting under malicious relay vehicles leading to cybersecurity concern in vehicular traffic environments. In this context, this paper presents cybersecurity measures for geocasting in vehicular traffic environments focusing on security oriented vehicular connectivity. Specifically, a vehicular intrusion prevention technique is developed to measure the connectivity between the cache agent (CA) and cache user (CU) vehicles. The connectivity between static transport vehicles and CA/CU is measured via vehicular intrusion detection approach. The performance of the proposed vehicular cybersecurity measure is evaluated in realistic traffic environments. The comparative performance evaluation attests the benefits of security oriented geocasting in vehicular traffic environments.