Online marketplaces offer vast product assortments, presenting consumers with the contradiction of choice, where abundant options delay the discovery of ideal items. While recommendation systems dynamically assist users, they struggle to address changing preferences and challenges such as data sparsity and the cold-start problem. This research proposes an innovative hybrid framework merging granular pattern discovery (GPD) with collaborative filtering (CF) to boost recommendation accuracy. The GPD stage applies techniques including decision trees, sequence mining, data transformations, clustering, and text analytics to heterogeneous user activity logs, extracting detailed behavioral patterns. These granular patterns enrich user profiles, enabling CF to more effectively match users by reducing data sparsity and enhancing prediction accuracy for new users or products with limited historical data. A graph-based algorithm matches consumers with associated preferences through similarity computations between profile vectors, seamlessly integrating neighborhood-based collaboration for personalized product rating predictions. Top-N recommendations are generated by ranking items based on predicted user–item affinity. Extensive testing demonstrates significant improvements over existing methods in key accuracy and ranking metrics, achieving a precision of 0.89 (compared with baseline methods of 0.68 and 0.62), a recall of 0.85, and an F1-score of 0.87. These improvements directly enhance user experience by increasing recommendation relevance and item discoverability, crucial for engagement and conversion in practical e-commerce scenarios. The proposed fusion approach significantly improves adaptability and accuracy, setting the stage for next-generation intelligent recommendation engines responsive to evolving user interests.

Abstract The rapid evolution of electronic media in recent decades has exponentially amplified the propagation of fake news, resulting in widespread confusion and misunderstanding among the masses, especially concerning critical topics like the COVID‐19 pandemic. Consequently, detecting fake news on social media has emerged as a prominent area of research, attracting significant attention. This article introduces a novel cascaded group multi‐head attention (CGMHA) model for COVID‐19 fake news detection. Our research collected Twitter datasets with accurate and fake tweets in Urdu. The novel CGMHA model and depth‐wise convolution capture local and global contextual information by employing multiple attention heads in a cascaded fashion, enabling a comprehensive understanding of fake news. While achieving state‐of‐the‐art performance, we also highlight challenges such as language variations and misinformation nuances in the detection process, contributing to a more comprehensive understanding of the complexities involved in combatting fake news. Our proposed model surpasses the performance of state‐of‐the‐art models in classifying fake news and achieves accuracy, F1 score, precision, and recall of 0.98, 0.96, 0.95, and 0.95, respectively.

The Internet of Vehicles (IoV) has revolutionized transportation by enabling seamless communication among vehicles and infrastructure. In emergency scenarios, the immediate dissemination of alert messages is vital for ensuring the safety of passengers, drivers, and pedestrians. This paper proposes an innovative cluster-based approach for efficient emergency message dissemination within the IoV network. Leveraging Software-Defined Networking (SDN) and Fog Computing, the proposed scheme seeks to alleviate transmission latencies and network congestion during the propagation of emergency messages in IoV networks. Empirical results unequivocally validate the superior efficacy of the proposed approach over existing methods.

According to research, the vast majority of road accidents (90%) are the result of human error, with only a small percentage (2%) being caused by malfunctions in the vehicle. Smart vehicles have gained significant attention as potential solutions to address such issues. In the future of transportation, travel comfort and road safety will be ensured while also offering several value-added services. The automotive industry has undergone a significant transformation through the use of emerging technologies and wireless communication channels, resulting in vehicles becoming more interconnected, intelligent, and safe. However, these technologies and communication systems are susceptible to numerous security attacks. The objective of this paper is to present a comprehensive overview of the smart vehicle’s architecture, encompassing emerging technologies and security challenges and solutions associated with smart vehicles. There has been a significant surge in the utilization of machine learning techniques in smart vehicles. We categorically discuss common security measures, including machine learning and deep learning based solutions that have been mentioned in the literature and implemented against security threats on smart vehicles. This paper has also been titled a tutorial due to its layout, which begins with covering preliminary knowledge, terminologies, and encompassing technologies required to comprehend smart vehicles. Following this, the paper addresses the overall challenges associated with smart vehicles and then focuses on security issues. In terms of solutions, the paper discusses overall solutions to security issues in smart vehicles before delving into a specific solution based on machine learning and deep learning.

With the swift growth of mobile devices in a wireless Internet of Things (IoT) network, mobility management in IP networks has attracted significant research interest due to certain issues with the frequent motion of mobile nodes (MNs) participating in the handover process. Hierarchical mobile IPv6 (HMIPv6) is one of the protocols proposed to accommodate recurrent mobility of MNs to decrease the signaling amount and improve packet loss issues, by localizing the mobility. HMIPv6 treats micro and macro mobility separately by introducing a new level in mobile IPv6 (MIPv6) with the addition of a node called mobility anchor point (MAP) which significantly improved handover performance specifically in micro-mobility. However, macro mobility yet needed to be improved as it is managed in a way as MIPv6 does. The registration of MN with the new MAP domain is a lengthy process, which leads to longer handover latency with high signaling cost to start proper communication again. Here, we proposed an advance-binding update HMIPv6 (A-BU), a group-based scheme to lessen interdomain handoff latency of IoT MNs. The efficiency of the proposed technique is validated with numerical analysis. The results compared with state-of-the-art mobility protocols illustrate a visible reduction in handover cost, signaling cost and provide seamless packet delivery during group-based interdomain mobility.

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.

In recent years researchers have shown significant interest in vehicular networks to augment road safety by providing real-time messaging services among vehicles. This work aims to provide a detailed analysis of emergency message dissemination techniques for the Internet of Vehicles (IoV). We explored position-based data dissemination techniques for emergency message dissemination, which is considered the best routing method because it does not rely on predestination entries of the route. Position-based schemes encounter some challenges, such as delay and accurate positioning. Existing survey papers of IoV focused on architecture, technologies, and layers. However, this article examines a brief comparison of subtypes of position-based emergency message routing, beacon-oriented and beacon-less techniques. In the end, we presented the basic challenges of emergency message dissemination; moreover, future directions are highlighted to promote the development of new protocols for emergency message dissemination to enhance the efficiency of IoV in a better way.

Real-time social multimedia level threat monitoring is becoming harder, due to higher and rapidly increasing data induction. Data induction through electric smart devices is greater compared to information processing capacity. Nowadays, data becomes humongous even coming from the single source. Therefore, when data emanates from all heterogeneous sources distributed over the globe makes data magnitude harder to process up to a needed scale. Big data and Deep learning have become standard in providing well-known solutions built-up using algorithms and techniques in resolving data matching issues. Now, with the involvement of sensors and automation in generating data obscures everything, predicting results to overcome a current era of ever enhancing demands and getting real-time visualization brings the need of feature like human behavior mode extraction to overcome any future threats. Big data analytics can bring the opportunity of predicting any misfortune even before they happen. Map reduce feature of big data supports massive data oriented process execution using distributed processing. Real-time human feature identification and detection can occur through sensors and internet sources. A behavioral prediction can further classify the information collected for introducing enhanced security extents. Real-time sensor devices are producing 24/7-hour data for further processing recording each event. IoT-based sensors can support in behavioral analysis model of a human. Real-time human behavioral monitoring based on image processing and IoT using big data analytics.

The act of source code plagiarism is an academic offense that discourages the learning habits of students. Online support is available through which students can hire professional developers to code their regular programming tasks. These facilities make it easier for students to practice plagiarism. First, raw source codes are cleaned from noisy data to extract meaningful codes as the actual logic is more important to the programmers. Second, pre-processing techniques based on tokenization are used to convert filtered codes into meaningful tokens. It breaks the codes into small instances with the number of occurrences known as the frequency. Thirdly, the local and global weighting scheme method is applied to estimate the significance of each feature in an individual or a group of documents. It helps us greatly to zoom in on the importance of each feature of how effective it is for the next phase. Fourth, the single value decomposition method is used to reduce the dimensions of these features by maintaining the actual semantics of the source codes. This technique is used to remove overloaded noise information and collect only those features that are more effective for plagiarism detection. Fifth, the latent semantic analysis (LSA) technique is used to mine the actual semantics of the source codes in the form of latent variables. After that, the LSA features are used as input to cosine similarity to compute the plagiarism among different source codes. To validate the proposed approach, we used the topic modeling approach to group the relevant features into different topics.

Abstract Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. Many current solutions suffer from data fragmentation, lack of reliable provenance, and diverse protocol regulations across multiple distributions and processes. Amongst other solutions, Blockchain has emerged as a leading technology, since it provides secure traceability and control, immutability, and trust creation among stakeholders in a low cost IT solution. Although Blockchain is making a significant impact in many areas, there are many impediments to its widespread adoption in supply chains. This article is the first survey of its kind, with detailed analysis of the challenges and future directions in Blockchain-enabled supply chains. We review the existing digitalization of the supply chain including the role of GS1 standards and technologies. Current use cases and startups in the field of Blockchain-enabled supply chains are reviewed and presented in tabulated form. Technical and non-technical challenges in the adoption of Blockchain for supply chain applications are critically analyzed, along with the suitability of various consensus algorithms for applications in the supply chain. The tools and technologies in the Blockchain ecosystem are depicted and analyzed. Some key areas as future research directions are also identified which must be addressed to realize mass adoption of Blockchain-based in supply chain traceability. Finally, we propose MOHBSChain, a novel framework for Blockchain-enabled supply chains.

Long distance high bandwidth networks are spanning several continents and many remote Healthcare centers are centralizing their data centers for economic reasons. For the best performance of their data centers, TCP (Transmission Control Protocol) performance and data security are the main critical issues in these network scenarios. TCP performance is directly related to its congestion control mechanism which is responsible for detecting and reacting to the overload traffic on the network. Data security is related to the security mechanism being used by sender and receiver nodes during communication. Linux users, which have rapidly increased in the last five years and most of the Healthcare data centers are being deployed on the Linux operating system, focus the researchers to work on Linux to enhance its performance and security accordingly. The Linux operating system uses TCP CUBIC as a congestion control mechanism with TCP during communication. TCP CUBIC became the default congestion control mechanism of Linux in 2006 after kernel 2.6.18. TCP CUBIC is fundamentally a loss based TCP congestion control mechanism and at each packet loss detection, it reduces its Congestion Window (cwnd) size 20 percent instead of 50 percent as in trademark congestion control mechanism Standard TCP. The aim of this paper is to design a new security mechanism that will work with TCP CUBIC to achieve the maximum possible performance and security over the network link. In this paper, Network Simulator 2 (NS-2) is used to compare the performance of TCP CUBIC with state-of-the-art mechanisms in long and short Round Trip Time (RTT), high bandwidth network scenarios. Results show that when new security mechanism is used with TCP CUBIC, overall better performance in the form of protocol fairness, TCP friendliness, goodput, and convergence time is achieved over the network link.

Information hiding has attracted the attention of researchers in recent years because digital contents are generated and share online through different communication channels. The verification of original authorship of digital contents is a crucial task. Digital watermarks are used to provide copyright protection and ownership verification solutions. However, digital text watermarking is a challenging job due to limited research and existing schemes like inter-word and paragraph spacing, line and word shift are used to hide information, which is not robust. If spaces are removed between words, lines, and paragraphs, the hidden information is destroyed. The special properties of Word document like variable, bookmarks, and range are appropriate for information hiding. The proposed scheme uses these special properties for watermarking without affecting the content. The watermark information is embedded in the special properties after encryption. Our proposed algorithm shows the results. After applying different attacks, the proposed scheme is robust 99.9%, excellent on imperceptibility 99.9%, and improve embedding capacity from Bytes to KiloBytes.

Since years, road collisions are a public issue which needs to be dealt. In this regard, the researchers have worked on developing autonomous vehicles. In this way, the number of collisions have been reduced to a great extent. But their is a need to develop the autonomous simulator which helps to pretest the autonomous vehicles in different road scenarios before launching them in the market. Hence, in this research work, we propose an autonomous simulator which helps to pre-train the autonomous vehicles in the real world. The effectiveness of the simulator has been proved through performing extensive experiments.

In the twenty-first century, globalisation made corporate boundaries invisible and difficult to manage. This new macroeconomic transformation caused by globalisation introduced new challenges for critical infrastructure management. By replacing manual tasks with automated decision making and sophisticated technology, no doubt we feel much more secure than half a century ago. As the technological advancement takes root, so does the maturity of security threats. It is common that today’s critical infrastructures are operated by non-computer experts, e.g. nurses in health care, soldiers in military or firefighters in emergency services. In such challenging applications, protecting against insider attacks is often neither feasible nor economically possible, but these threats can be managed using suitable risk management strategies. Security technologies, e.g. firewalls, help protect data assets and computer systems against unauthorised entry. However, one area which is often largely ignored is the human factor of system security. Through social engineering techniques, malicious attackers are able to breach organisational security via people interactions. This paper presents a security awareness training framework, which can be used to train operators of critical infrastructure, on various social engineering security threats such as spear phishing, baiting, pretexting, among others.

Among constituents of communication architecture, routing is the most energy squeezing process. In this survey article, we are targeting an innovative aspect of analysis on routing in wireless sensor network (WSN) that has never been seen in the available literature before. This article can be a guiding light for new researchers to comprehend the WSN technology, energy aware routing, and the factors that affect the energy aware routing in WSN. This insight comprehension then makes the ways easy for them in designing such types of algorithms as well as evaluating the authenticity and extending the existing algorithms of this category, since algebraic and graphical modelling of these factors is also demonstrated. Various available techniques used by existing routing algorithms to handle these factors in making themselves energy aware are also given. Further, they are analyzed along with the suggested improvements for the researchers. At the end, we presented our previously published research work as an example and case study of discussed factors. A rich list of references is also cited for interested readers to explore the related given points.

Mobile Cloud Computing (MCC) is a developing technology that assists in improving the quality of the mobile services. Since the increase in mobile resources, the researchers have taken the initiative to take into contemplation resource sharing among heterogeneous mobile devices. Therefore, to design a system architecture for mobility models and resource sharing are key issues that require utmost efforts to be solved to achieve anticipated objectives. Therefore, keeping in view the desired goals, in this paper, we present a system architecture based on the hierarchical resource sharing mechanism for MCC. The proposed system architecture is divided into three domains, such as Global Cloud Server (GCS), Local ISP Server (LIS), and Gateway Server (GWS). Also, the novel paradigm for minimizing the delay in the network based on deploying Foglets at each proposed algorithm of clustering mechanism is also present. Moreover, the fuzzy rule-based scheme is proposed to eliminate the inappropriate foglets before deciding an optimal foglet for handover. A foglet selection scheme is developed based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision mechanism. Various parameters such as delay, jitter, Bit Error Rate (BER), packet loss, communication cost, response time, and network load are considered for selecting an optimal network. To check the feasibility and performance of the proposed system architecture, the mobility scenario is considered with a different speed of a mobile node ranging from very high to very low. The simulation results and an analytical model are compared with existing scheme for foglet selection by a mobile node. From the analysis and discussion, it is shown that the proposed system architecture helps in minimizing handover delay, packet loss, average queuing delay, and device lifetime. in a network.

Interoperability remains a significant burden to the developers of Internet of Things’ Systems. This is due to the fact that the IoT devices are highly heterogeneous in terms of underlying communication protocols, data formats, and technologies. Secondly due to lack of worldwide acceptable standards, interoperability tools remain limited. In this paper, we proposed an IoT based Semantic Interoperability Model (IoT-SIM) to provide Semantic Interoperability among heterogeneous IoT devices in healthcare domain. Physicians communicate their patients with heterogeneous IoT devices to monitor their current health status. Information between physician and patient is semantically annotated and communicated in a meaningful way. A lightweight model for semantic annotation of data using heterogeneous devices in IoT is proposed to provide annotations for data. Resource Description Framework (RDF) is a semantic web framework that is used to relate things using triples to make it semantically meaningful. RDF annotated patients’ data has made it semantically interoperable. SPARQL query is used to extract records from RDF graph. For simulation of system, we used Tableau, Gruff-6.2.0, and Mysql tools.

Putting constraint on temporal domain based system performance, sometimes turns right into wrong, update into outdate, current into past and lawful into illegal. Therefore perfection satisfaction not only at hardware level but also at software level design is indispensable to quench the thirst of such ideal real time communication. From software perspective, in the timeless sensitive communication context, RTOS's kernel design must be sensitive enough to compensate different delays. Active contribution from each communication layer protocol as well as cross layer protocol makes the overall performance healthier and valiant. This active contribution becomes much important when the resources are in stringent constraint as in wireless sensor network (WSN). A WSN is a network of energy, memory and processing constraint nodes. In this paper, we have proposed a protocol: REAR, to achieve the real-time routing with energy awareness feature in the ad hoc micro sensors network. It follows the customized proactive routing with in-network processing and localized behavior. Simulation results have intuited that REAR presents a better solution in energy consumption with the effect of improving the network life as well as increasing the performance efficiency in end-to-end delay.