The Internet of Vehicles (IoV) lets cars talk to one another in smart ways by sharing data in real time between vehicles, infrastructure, and edge nodes. But as more and more parts become linked, the danger of advanced attacks rises. At the same time, traditional Intrusion Detection Systems (IDS) typically cannot keep up with IoV's changing, low-latency, and resource-limited needs. Deep learning has become a viable alternative, but it needs a lot of labeled data and architectures that are hard to compute, which makes it less useful in real-world vehicle situations. To solve this problem, we suggest Vehicular Intrusion Detection by Temporal-aware Attention (VITA), a lightweight, self-supervised, and label-free IDS framework made just for IoV. VITA uses an Adversarial Autoencoder (AAE) with Efficient Channel Attention (ECA) to pick out important spatial characteristics and residual Temporal Convolutional Networks (TCNs) to simulate long-range temporal relationships in a way that is efficient way. A latent-space smoothing technique is added to stabilize adversarial learning, and a log-cosh reconstruction loss is added to make the system more resistant to noisy vehicle telemetry. VITA works in real time and does not need a lot of processing power, so it can be used on edge devices in vehicles. Our proposed VITA framework shows superior performance across several critical criteria, including an average 30% reduction in latency compared to state-of-the-art models. VITA achieves an average 8% improvement in detection accuracy, effectively identifying a broad spectrum of vehicular attacks, such as replay, Global Positioning System (GPS) spoofing, and Denial-of-Service (DoS) attacks. Furthermore, VITA outperforms existing systems with an average 5x reduction in false positive rate, and shows robust performance under varying noise levels, with only a modest 4.7% drop in accuracy under high jitter conditions. Additionally, it excels in computational efficiency, requiring 50% less memory and processing power on average, making it highly suitable for real-time, edge-based IoV deployments.

With the increasing complexity and interconnectivity of IoV, protecting in-vehicle networks from cyberattacks and controlling mobile dynamics have become increasingly difficult. Currently, most techniques rely on large amounts of labeled data, which are difficult to obtain and expensive to generate. Furthermore, they are not focused on real-time Intrusion Detection Systems (IDSs) and adaptive decision making. To address these issues, this paper proposes an Adaptive Decision Framework for IoV-IDS (ADFII). To ease a policy update in the ADFII, we use Deep Reinforcement Learning (DRL) as it can generalize the policy to different kinds of vehicles by updating them according to specific variables, and we use Variational Autoencoders (VAEs), which ease the detection of hidden features and generalizes anomalies and new attack patterns. This combination of cores ensures that ADFII is both stable and flexible, helping in the efficient detection of intrusions in IoV environments that are subject to property changes, making the decision-making process more secure and reliable. To learn to detect incursions, navigate, and locate, ADFII only has to learn the right rules for the environment. In tests, ADFII has 7.1%, 3.7%, 4.1%, and 5% improvement, respectively, for better decision-making, reward, faster time, and finding an attack than baseline algorithms.

The Internet of Vehicles (IoV) enables cooperative driving and real-time Vehicle-to-Everything (V2X) communication but remains vulnerable to behavioral and structural anomalies due to its dynamic, decentralized nature. Existing deep learning methods either overlook topological inconsistencies or ignore communication feature fidelity, while random-walk sampling introduces contextual noise. In this paper, we propose Network Observation for Vehicular Anomalies (NOVA), a self-supervised graph-based framework that detects both behavioral and structural anomalies in IoV networks without labeled data. NOVA models vehicular communications as attributed graphs and employs intimacy-guided subgraph sampling to extract meaningful neighborhoods. A Graph Convolutional Network (GCN)–based generative module reconstructs node attributes to reveal behavioral deviations, while a contrastive module validates structural coherence through embedding comparisons of real and perturbed contexts. Their hybrid anomaly score enables accurate, scalable, and real-time detection of compromised nodes. Performance results show that NOVA achieves state-of-the-art performance (98.7% accuracy, 98.1% F1), real-time throughput (~4.7k events/s at 5k msg/s), and strong robustness (AUROC 0.99, AUPRC 0.98, FAR 0.05) with near-linear scalability (≤40 ms latency for 50k vehicles). By integrating generative and contrastive self-supervised learning with context-aware sampling, NOVA significantly enhances IoV security, reliability, and adaptability.

Timely detection of cancer is essential for enhancing patient outcomes. Artificial Intelligence (AI), especially Deep Learning (DL), demonstrates significant potential in cancer diagnostics; however, its opaque nature presents notable concerns. Explainable AI (XAI) mitigates these issues by improving transparency and interpretability. This study provides a systematic review of recent applications of XAI in cancer detection, categorizing the techniques according to cancer type, including breast, skin, lung, colorectal, brain, and others. It emphasizes interpretability methods, dataset utilization, simulation environments, and security considerations. The results indicate that Convolutional Neural Networks (CNNs) account for 31 % of model usage, SHAP is the predominant interpretability framework at 44.4 %, and Python is the leading programming language at 32.1 %. Only 7.4 % of studies address security issues. This study identifies significant challenges and gaps, guiding future research in trustworthy and interpretable AI within oncology.

ABSTRACT Routing protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On‐Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real‐life situations. It is interesting to see how the results of the CTPN–based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T‐tests indicate that the modeling results are highly reliable.

Neuro Degenerative Disorders (NDDs) involve progressive nerve cell loss, impacting functions like sensation, movement, memory, and cognition, posing life-threatening risks. Despite extensive research, viable therapies remain elusive due to complex pathophysiology. Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), shows promise in NDD diagnosis and treatment by leveraging vast datasets for accurate predictions. However, because AI models are “black boxes,” explainable AI (XAI) had to be created to make sure that physicians and patients would trust and accept it. Early detection is critical to stop degeneration and make things better for patients. Many in-depth studies on XAI are designed explicitly for NDDs. Existing research does not constantly look at how to interpret NDDs, how to evaluate them, or how to keep them safe. This paper fills in these gaps by looking at and grouping XAI methods for different NDDs, to make them easier to understand and use in medical settings. In this paper, we look at the interpretability methods used in various NDD studies. The methods are split into five groups based on the conditions they are used to treat: Frontotemporal Dementia (FTD), Multiple Sclerosis (MS), Amyotrophic Lateral Sclerosis (ALS), and Alzheimer's Disease (AD). It organizes XAI methods into groups and talks about their pros, cons, and clinical importance. The study also finds some important research gaps. For example, it says that there are no good security frameworks and that XAI is hard to use in real-life healthcare settings. By giving helpful information and a plan for future research, this paper shows how XAI could change how NDDs are found, treated, and predicted. AI technologies will be used more in healthcare, and this will help us learn more about these challenging conditions.

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area.

Abstract Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of Excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

Real-world problems often consist of multiple conflicting objectives to be optimized simultaneously, featuring a set of Pareto-optimal solutions. Estimating the entire Pareto front can be computationally expensive, and is not always necessary, as decision makers (DMs) will likely be interested only in specific regions of the Pareto front. In the absence of knowledge about the DM preferences, the so-called knees in the Pareto front are considered to be particularly attractive. In this article, we propose using Thompson sampling in the Bayesian optimization framework to estimate the location of the knee regions in a data-efficient manner. Our experimental results show that the proposed methods accurately locate the knee regions after a very small number of evaluations, providing a computationally efficient approach to single- and multiknee detection in multiobjective optimization.

Summary Wireless Sensor Networks (WSNs) are critical for communication within a mile radius and industrial applications. These networks are very prone to failure due to their enormous number of nodes and their unique hardware and software restrictions. To make sure network performance, a lot of study needs to be done to improve failure tolerance and stability. This study looks at how to judge the availability and dependability of WSNs that have long‐term issues. The suggested method checks how well a network works in various failure cases by using fault trees and Markov chain analysis. Such methods help us find and study possible failure scenarios and how they might impact the network's dependability in a planned way. The results show that WSNs have major flaws and give useful suggestions for making the systems work better. The findings show that using these evaluation methods may greatly enhance the ability to handle faults, lower the risk of damage, and allow developers of WSNs to make smart choices.

Summary Cloud computing is quickly becoming a common commercial model for software delivery and services, enabling companies to save maintenance, infrastructure, and labor expenses. Also, Internet of Things (IoT) apps are designed to ease developers' and users' access to networks of smart services, devices, and data. Although cloud services give nearly infinite resources, their reach is constrained. Designing coherent and organized apps is made possible by integrating the cloud and IoT. Expanding facilities by combining services is a critical component of this technology. Various services may be presented in this environment based on the user's demands. Considering their Quality of Service (QoS) attributes, discovering the appropriate available atomic services to construct the needed composite service with their collaboration in an orchestration model is an NP‐hard issue. This article suggests a service composition method using Grey Wolf Optimization (GWO) and MapReduce framework to compose services with optimized QoS. The simulation outcomes illustrate cost, availability, response time, and energy‐saving improvements through the suggested approach. Comparing the suggested technique to three baseline algorithms, the average gain is a 40% improvement in energy savings, a 14% decrease in response time, an 11% increase in availability, and a 24% drop in cost.

Abstract Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network , recurrent neural network , generative adversarial network , multilayer perception , and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F -score, latency, adaptability, and scalability.

With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.

Because of recent technological developments, such as Internet of Things (IoT) devices, power consumption has become a major issue. Atomic silicon quantum dot (ASiQD) is one of the most impressive technologies for developing low-power processing circuits, which are critical for efficient transmission and power management in micro IoT devices. On the other hand, multipliers are essential computational circuits used in a wide range of digital circuits. Therefore, the multiplier design with a low occupied area and low energy consumption is the most critical expected goal in designing any micro IoT circuits. This article introduces a low-power atomic silicon-based multiplier circuit for effective power management in the micro IoT. Based on this design, a$4\times 4$-bit multiplier array with low power consumption and size is presented. The suggested circuit is also designed and validated using the SiQAD simulation tool. The proposed ASiQD-based circuit significantly reduces energy consumption and area consumed in the micro IoT compared to most recent designs.

The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoD without demonstrating safety and security for human life is exceedingly difficult. In addition, intrusion detection systems (IDSs) in IoD confront several obstacles because of the dynamic network architecture, particularly in balancing detection accuracy and efficiency. To increase the performance of the IoD network, we proposed a blockchain-based radial basis function neural networks (RBFNNs) model in this article. The proposed method can improve data integrity and storage for smart decision-making across different IoDs. We discussed the usage of blockchain to create decentralized predictive analytics and a model for effectively applying and sharing deep learning (DL) methods in a decentralized fashion. We also assessed the model using a variety of data sets to demonstrate the viability and efficacy of implementing the blockchain-based DL technique in IoD contexts. The findings showed that the suggested model is an excellent option for developing classifiers while adhering to the constraints placed by network intrusion detection. Furthermore, the proposed model can outperform the cutting-edge methods in terms of specificity, F1, recall, precision, and accuracy.