Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.

Clustering evaluation techniques are important to check the clustering algorithm quality. High cluster similarity help to reduce the distance between a node to node within the cluster, also good separation was more important to avoid overlapping clusters. The network performance will increase and the signal will be high. Many researchers proposed different validation indexes such as Davies–Bouldin, Dunn, and Silhouette indexes. These cluster validation indexes focus on the internal or external cluster similarity, and some of them deal with both cases. The employing of graph-based distance to non-spherical clusters and selection of reference points will not be effective all the time because the average distance between reference points and all nodes will be changed dynamically such as in the VANET application. To solve this problem a dynamic sample node should be selected or the similarity of all nodes should be checked. This paper proposes a new Minimum intra-distance and Maximum inter-distance Index (M2I) to improve these indexes. The proposed index checks the internal similarity and the external distance among all nodes from cluster to cluster to ensure that high separation will occur. M2I checks the similarity from node to node within the cluster and cluster to cluster. The proposed index will be an improvement of all high-rank indexes. The proposed index was applied in different scenarios (VANET and real datasets scenarios) and compared with other indexes. The index result shows that the proposed M2I outperforms the others. The M2I accuracy is 100% in the VANET scenario and 89% in the real datasets scenario.

In this study, an automatic solar defect detection and classification system using deep learning was proposed. This study focuses on solar faults in photovoltaic systems identified through Electroluminescence (EL) images by employing a deep learning framework that utilizes both traditional Convolutional Neural Networks (CNNs) and a pre-trained VGG16 and VGG19 network for feature extraction. This approach was designed to enhance the accuracy and efficiency of solar defect classification. The framework is structured into three main phases: image preprocessing, feature extraction using CNNs, Histogram of Oriented Gradients (HOG) and Artificial Neural Networks (ANN), and classification through a Deep Neural Network (DNN). During preprocessing, images are scaled down to uniform dimensions to ensure consistent learning. They adopted two classification strategies: binary classification (defective or non-defective) and multiclass classification; the class names are 0%, 33%, 67%, and 100% (here, % represents the percentage of defectiveness), which represents the defect likelihood. To refine the model’s performance, a data augmentation technique has been utilized on the dataset. The effectiveness of the model was evaluated using various metrics, including the precision, recall, F1-score, and accuracy for two and four classes and obtained on, supported by confusion matrices. VGG-19 model outperformed other models and achieved precision, recall, F1-score and accuracy of 90% each for two classes respectively and similarly 94% for four classes. This study compares two classification methods to assess the ability of the deep learning framework to detect and classify solar defect images automatically.

The dedicated vehicular ad-hoc network (VANET) is a communication model as vehicles can communicate with other vehicles directly or via fixed nodes called Roadside Units (RSU). It has become necessary to find supporting protocols for RSU to increase their efficiency, and thus increase the efficiency of the network. Since these nodes are distributed on the roads, it is important to find appropriate ways to distribute them to increase data transfer and reduce their energy consumption. In this paper, a zigzag distribution method is proposed and a mathematical model of Right Side–Left Side (RS-LS) is used to reduce the energy consumption of RSU and compare it with the main chain protocol. Two different cases were taken, Case-1-is for low density with 20 vehicles and Case-2-is for high density with 40 vehicles. The proposed method succeeded in saving energy and reduce the consumption in both cases, by 60% and 44%, respectively.

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.