Abstract Software-defined networks (SDN), owing to their centralized control architecture, provide high flexibility in network management, configuration, and monitoring; however, this architecture also introduces critical challenges related to scalability, performance bottlenecks, and quality of service (QoS) degradation under heavy and dynamic traffic conditions, particularly in large-scale and beyond 5G (B5G) networks with stringent real-time latency requirements. In such environments, the controller placement problem (CPP) becomes an inherently NP-hard multi-objective optimization task, where conventional sequential and heuristic methods struggle to explore the massive solution space within practical time constraints, thereby motivating the need for computationally scalable frameworks that can exploit parallel processing and high-performance computing (HPC) capabilities. To address these challenges, this paper proposes DeepWK-MSTC, an advanced multi-objective controller placement framework that integrates weighted Kmeans-based clustering with a deep learning-driven optimization mechanism. The proposed method leverages the inherent parallelism of Deep Monte Carlo Tree Search (Deep-MCTS) to enable concurrent rollouts and accelerated decision-making, while jointly optimizing three key objectives: minimizing average delay ratio (ADR), improving energy efficiency (EE), and balancing controller load under dynamic traffic patterns. By incorporating network topology characteristics and real-time traffic dynamics, DeepWK-MSTC efficiently avoids local optima and ensures stable optimization behavior. The effectiveness of the proposed framework is evaluated on six real-world network topologies from the Internet Topology Zoo, namely Aarnet, Chinanet, Deutsche Telekom, Colt, Cogent, and Tata, and compared against state-of-the-art baselines including ALO and ELA-RCP. Experimental results demonstrate that DeepWK-MSTC achieves an average reduction of 50.2% in ADR, an average energy saving of 26.45%, and a 24% decrease in maximum controller load, with an additional 11.5% relative ADR reduction compared specifically to ELA-RCP. Overall, by explicitly exploiting parallel optimization and HPC-oriented design principles, DeepWK-MSTC enhances resource utilization and ensures scalable, stable, and real-time-capable controller placement for large-scale SDN environments.

ABSTRACT Mobile crowdsensing (MCS) has emerged as a promising paradigm leveraging the widespread availability of mobile devices for large‐scale data collection. Ensuring high quality of service (QoS) in MCS is paramount for its effectiveness and reliability. This survey reviews the application of metaheuristic optimization algorithms to enhance QoS in MCS systems, with a focus on adaptive and hybrid optimization techniques for real‐time applications. We discuss key QoS metrics, such as accuracy, latency, and reliability, and outline the challenges inherent in maintaining these metrics, including scalability, adaptability to dynamic environments, and energy efficiency. The survey provides a comprehensive overview of various metaheuristic algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA), evaluating their applicability and potential in MCS contexts. Through a systematic review of the literature, we highlight recent advancements and practical implementations of these algorithms, presenting comparative insights and case studies to illustrate their effectiveness in addressing QoS challenges.

Abstract Human activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field.

Abstract One of the time-consuming and expensive phases in software development is software testing, which is used to improve the quality of software systems. Therefore, Software test automation is a helpful technique that can alleviate testing time. Several techniques based on evolutionary and heuristic algorithms have been put forth to produce maximum coverage test sets. The primary shortcomings of earlier methods are inconsistent outcomes, insufficient branch coverage, and low fault-detection rates. Increasing branch coverage rate, defect detection rate, success rate, and stability are the primary goals of this research. A time- and cost-effective method has been suggested in this research to produce test data automatically by utilizing machine learning and horse herd optimization algorithms. In the first stage of the proposed method, the suggested machine learning classification model identifies the non-error-propagating instructions of the input program using machine learning algorithms. In the second stage, a test generator was suggested to cover only the program's fault-propagating instructions. The main characteristics of produced test data are avoiding the coverage of non-error-propagating instructions, maximizing the coverage of error-propagating instructions, maximizing success rate, and the fault discovery capability. Several experiments have been performed using nine standard benchmark programs. In the first stage, the suggested instruction classifier provides 90% accuracy and 82% precision. In the second stage, according to the results, the produced test data by the suggested method cover 99.93% of the error-prone instructions. The average success percentage with this method was 98.93%. The suggested method identifies roughly 89.40% of the injected faults by mutation testing tools.

Abstract Biometric technologies are fast becoming a requirement in security systems today, providing solutions where traditional means alone would not be adequate. This paper proposes FootprintNet, a Siamese network that utilizes pre-trained convolutional neural networks, specifically EfficientNet, MobileNet, and ShuffleNet, to improve the robustness and accuracy of footprint recognition. By learning the ability to identify fine distinctions between images of footprints, FootprintNet offers great biometric identification potential. Detailed analysis of the Biometric 220 × 6 Human Footprint dataset shows a true positive rate over 99% under various thresholds and a precision rate of 100% during training. Most importantly, this system is also applicable to newborn and infant identification, making it especially significant in medical settings, including hospitals and birthing clinics. Furthermore, the model sizes range from 7.8 MB (ShuffleNet) to 24.5 MB (EfficientNet), which makes FootprintNet deployable on low-computational-power devices—a highly desirable trait for mobile or high-security applications.

Abstract The lack of symptoms in the early stages of liver disease may cause wrong diagnosis of the disease by many doctors and endanger the health of patients. Therefore, earlier and more accurate diagnosis of liver problems is necessary for proper treatment and prevention of serious damage to this vital organ. We attempted to develop an intelligent system to detect liver failure using data mining and artificial neural networks (ANN), this approach considers all factors impacting patient identification and enhances the probability of success in diagnosing liver failure. We employ multilayer perceptron neural networks for diagnosing liver failure via a liver patient dataset (ILDP). The proposed approach using the backpropagation algorithm, improves the diagnosis rate, and predicts liver failure intelligently. The simulation and data analysis outputs revealed that the proposed method has 99.5% accuracy, 99.65% sensitivity, and 99.57% specificity, making it more accurate than Previous related methods.

Summary Recently, with the rapid development of the Internet of Things (IoT) and its real‐time applications, the network is required to guarantee the differential quality of service (QoS) requirements of the various data flows (telemedicine, video, and live streaming) of various IoT services. Software‐defined network (SDN) as emerging network technology separates the control logic from data planes of networks and is a promising technique to guarantee the QoS requirements of different services. In SDN, the controller has global visibility of the whole network devices such as switches and routers. Hence, controllers can dynamically optimize QoS requirements and resources of the network. On the other hand, providing a QoS‐aware routing scheme is an important challenge. Hence, in this paper by capitalizing on the fuzzy analytic hierarchy process (AHP), a new QoS‐aware routing method is proposed in SDNs. The proposed scheme considers the combination of different QoS criteria such as bandwidth, route length, reliability, and bit error rate for selecting an appropriate route between sender and receiver nodes. The simulation results indicated that the proposed method improves throughput and end‐to‐end delay. Comparison with other routing algorithms shows that the proposed method performs better load balancing under different network topologies.

Summary In a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.

IPv6 routing protocol for low‐power and lossy networks (RPL) has been developed as a routing agent in low‐power and lossy networks (LLN), where nodes’ resource constraint nature is challenging. This protocol operates at the network layer and can create routing and optimally distribute routing information between nodes. RPL is a low‐power, high‐throughput IPv6 routing protocol that uses distance vectors. Each sensor‐to‐wire network router has a collection of fixed parents and a preferred parent on the path to the Destination‐oriented directed acyclic graph (DODAG) graph’s root in steady‐state. Each router part of the graph sends DODAG information object (DIO) control messages and specifies its rank within the graph, indicating its position within the network relative to the root. When a node receives a DIO message, it determines its network rank, which must be higher than all its parents’ rank, and then continues sending DIO messages using the trickle timer. As a result, DODAG begins at the root and eventually extends to encompass the whole network. This paper is the first review to study intrusion detection systems in the RPL protocol based on machine learning (ML) techniques to the best of our knowledge. The complexity of the new attack models identified for RPL and the efficiency of ML in intelligent and collaborative threats detection, and the issues of deploying ML in challenging LLN environments underscore the importance of research in this area. The analysis is done using research sources of “Google Scholar,” “Crossref,” “Scopus,” and “Web of Science” resources. The evaluations are assessed for studies from 2016 to 2021. The results are illustrated with tables and figures.

Localisation of an active capsule endoscope inside the stomach has different challenges. One of them is the estimation of the capsule's roll angle. Another challenge is adjusting the distance between the capsule and the stomach to achieve high‐quality imaging in the region of interest. In this study, an optimised image‐guided localisation (O‐Localisation) method is proposed to estimate the roll angle and the scale factor between the consecutive frames. The distance between the capsule and walls of the stomach can be adjusted using the suggested fuzzy adjuster, which is developed based on the estimated scale factors and calibration parameters. This new method is only based on visual information extracted from wireless capsule endoscope video frames. The results show that this method can accurately estimate the rotation angles and scale factors with errors <0.2% for the angles up to 90° and 0.3% for the scales up to 5, respectively. The method is robust to the brightness changes up to 80% with a maximum error of 0.3%. The computational time is about 1 s and can be considered near real‐time for this application. Accordingly, the O‐Localisation method as a real‐time, robust and precise method for capsule localisation can provide a more efficient controllable and steerable capsule endoscopes.

Abstract The objective of this study is to develop an algorithm to detect and classify six types of electrocardiogram (ECG) signal beats including normal beats (N), atrial pre‐mature beats (A), right bundle branch block beats (R), left bundle branch block beats (L), paced beats (P), and pre‐mature ventricular contraction beats (PVC or V) using a neural network classifier. In order to prepare an appropriate input vector for the neural classifier several pre‐processing stages have been applied. Initially, a signal filtering method is used to remove the ECG signal baseline wandering. Continuous wavelet transform is then applied in order to extract features of the ECG signal. Next, principal component analysis is used to reduce the size of the data. A well‐known neural network architecture called the multi‐layered perceptron neural network is then utilized as the final classifier to classify each ECG beat as one of six groups of signals under study. Finally, the MIT‐BIH database is used to evaluate the proposed algorithm, resulting in 99.5% sensitivity, 99.66% positive predictive accuracy and 99.17% total accuracy.

Nowadays, among the microscopic traffic flow modeling approaches, the car-following models are increasingly used by transportation experts to utilize appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, in this paper, a modified neural network approach is proposed to simulate and predict the car-following behavior based on the instantaneous reaction delay of the driver-vehicle unit as the human effects. This reaction delay is calculated based on a proposed idea, and the model is developed based on this feature as an input. In this modeling, the inputs and outputs are chosen with respect to the reaction delay to train the neural network model. Using the field data, the performance of the model is calculated and compared with the responses of some existing neural network car-following models. Considering the difference between the responses of the actual plant and the predicted model as the error, comparison shows that the error in the proposed model is significantly smaller than that that in the other models.

Nowadays, simulation has become a cost-effective option for the evaluation of infrastructure improvements, on-road traffic management systems, and in vehicle driver support systems due to the fast evolution of computational modeling techniques. This paper presents a Locally Linear Neuro-Fuzzy (LLNF) model to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU). Local Linear Model Tree (LOLIMOT) learning algorithm is applied to train the model using real traffic data. This model was developed based on a new idea for estimating the instantaneous reaction of DVU, as an input of LLNF model. The model?s performance was evaluated based on real observed traffic data and also through comparisons with the results of LLNF models based on constant reaction delay. The results showed that LLNF model based on instantaneous reaction delay input outperformed the other car following models.

A driving simulator experiment is presented investigating different road departure prevention (RDP) setups. To induce the risk of road departure, thirty test drivers were asked to avoid a pylon-confined area (obstacle) while keeping the vehicle within the road limits. The RDP system intervened by applying a haptic-feedback (i.e., haptic shared control) and/or correcting the steering angle (i.e., drive-by-wire (DBW) input-mixing shared control) in the event that a vehicle road departure was likely to occur. The system that determines the correcting steering input is a RDP controller based on the driver's inputs. The results showed that DBW effectively helped drivers to stay within road limits and reduced workload. The haptic shared control had a significant influence on the measured steering torque, but limited effect on the steering wheel angle and the vehicle path. The DBW system resulted in drivers making counter-corrections demoting their performance. In conclusion, shared control for RDP is effective, although more research needs to be conducted regarding the human response in situations where the relationship between the steering wheel angle and the front wheels' steering angle is altered while driving.