This research presents a vehicle ID‐based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID‐based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back‐off numbers chosen by cars engaging in the back‐off procedure. This leads to identifying a car ID‐based randomized back‐off code, which reduces the likelihood of a collision due to the identical back‐off number. A traffic density based‐congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one‐fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID‐based back‐off values for high‐standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID‐based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back‐off figures.

Digital innovation has emerged as a transformative force driving the growth and development of economies worldwide, particularly in developing nations. By leveraging technology, these countries can overcome structural challenges, improve productivity, and achieve sustainable economic progress. In an era defined by rapid technological advancements, digital tools and platforms are playing a pivotal role in fostering economic inclusion, enhancing public services, and creating opportunities for individuals and businesses alike. Developing economies often face hurdles such as limited infrastructure, inefficient systems, and high levels of poverty. Digital innovation offers solutions to these issues by introducing cost-effective, scalable technologies that address local challenges while fostering global competitiveness. Mobile connectivity, for instance, has revolutionized access to financial services in regions where traditional banking systems are underdeveloped.

A mobile ad hoc network (MANET) is a self-configuring infrastructure less network of mobile devices communicating with each other through wireless links. Multicasting in MANET is going to play a key role in future wireless network. For resource limited environment like MANET, it is necessary to minimise energy utilisation of a network and also providing effective communication. Several energy efficient multicast routing protocols have been proposed in recent years for possible deployment of MANETs in government, military and business applications. In this paper, we present a thorough survey on performance analysis of various energy aware multicast routing protocols in mobile ad hoc network. A general overview has been given on multicast routing protocols, describing how they work, their advantages, limitations and their future enhancements. Finally, comparisons are made among various multicast routing protocols considering parameters like energy aware, topology, location aware, robustness, QoS aware, initialisation, control overhead and periodic control messaging, scalability etc.

Phishing attacks pose a major threat to cybersecurity by deceiving users into revealing sensitive information. This study proposes an optimized phishing detection framework that leverages both syntactic URL features and temporal behavior through a novel ensemble model combining Gradient Boosting and Recurrent Neural Networks (RNN). The Gradient Boosting component captures static patterns and feature importance from structured URL attributes, while the RNN processes sequential character-level URL data to learn underlying patterns of malicious domains. The ensemble approach enhances detection accuracy, reduces false positives, and adapts effectively to evolving phishing tactics. Experimental results on benchmark datasets demonstrate superior performance compared to individual models, confirming the potential of hybrid ensemble techniques for real-time phishing URL detection.

Usually, omnidirectional radiation pattern antenna is used in the mobile ad hoc network (MANET) which causes neighbor node interference, consumes more power, and supports only limited range of transmission. To overcome these problems, smart antennas are used. A lot of medium access control (MAC) protocols are proposed using smart antennas. Existing works addressed various problems such as hidden terminal problem, hidden beam problem, deafness of nodes, and head of line blocking problem. However, certain factors including determination of weight vector and conveying it to the neighbor nodes for distortion-free transmission are not considered. In this study, nullifying MAC (NULLMAC) framework is proposed using an adaptive antenna array (AAA) for improving network performance in MANET. NULLMAC framework uses channel information for achieving high throughput and spatial reuse through integrated physical and MAC layer. Before the transfer of data packets, the receiver initially determines its weight vector and conveys it to the transmitter through control packets. Then, the transmitter computes its weight to nullify the dynamic receivers present in the neighborhood region to find the desired receiver. Beamformer weights are determined through channel coefficients between a transmitter-receiver pair to establish distortion-free transmission. Extensive simulations are performed using OPNET integrated with MATLAB. NULLMAC framework achieves 27.22% more throughput and 40.46% increase in signal-to-noise ratio.

Physiological signal-based emotion recognition has emerged as a significant field of study with applications in interactive system interfaces, affective computing and healthcare. The DREAMER dataset to explore the effectiveness of EEGNet, a compact convolutional neural network designed for EEG-based interfaces between brain and computers in emotion classification. EEGNet can attain superior emotion recognition accuracy by learning directly from raw EEG data, thus outperforming traditional feature-based methods. The experimental results demonstrate that EEGNet attains accuracy for valence classification and arousal classification, significantly surpassing conventional machine learning approaches. This paper demonstrates the usefulness of EEGNet for creating EEG-based emotion identification systems as a versatile and effective solution for real-world application. The possible future concepts under this research include multimodal data integration and subject independent classification, which enhance deep learning in physiological signal processing.