In software engineering community, defect prediction is one the active domain. For the software’s success, it is essential to reduce the software engineering and data-mining gap. Software defects prediction forecasts the source code errors before the testing phase. Methods for predicting software defects, such as clustering, statistical methods, mixed algorithms, metrics based on neural networks, black box testing, white box testing and machine learning are frequently used to explore the effect area in software. The main contribution of this research is the use of feature selection for the first time to increase the accuracy of machine learning classifiers in defects pre-diction. The objective of this study is to improve the defects prediction accuracy in five data sets of NASA namely; CM1, JM1, KC2, KC1, and PC1. These NASA data sets are open to public. In this research, the feature selection technique is use with machine-learning techniques; Random Forest, Logistic Regression, Multilayer Perceptron, Bayesian Net, Rule ZeroR, J48, Lazy IBK, Support Vector Machine, Neural Networks, and Decision Stump to achieve high defect prediction accuracy as compared to without feature selection (WOFS). The research workbench, a machine-learning tool called WEKA (Waikato Environment for Knowledge Analysis), is used to refine da-ta, preprocess data, and apply the mentioned classifiers. To assess statistical analyses, a mini tab statistical tool is used. The results of this study reveals that accuracy of defects prediction with feature selection (WFS) is improve in contrast with the accuracy of WOFS.

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a dendrogram for clustering, which solved the problem. However, there is still room for a single algorithm to analyze the data’s characteristics. The proposed novel approach model RFMT analyzed Pakistan’s largest e-commerce dataset by introducing k-means, Gaussian, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) beside agglomerative algorithms for segmentation. The cluster is determined through different cluster factor analysis methods, i.e., elbow, dendrogram, silhouette, Calinsky–Harabasz, Davies–Bouldin, and Dunn index. They finally elected a stable and distinctive cluster using the state-of-the-art majority voting (mode version) technique, which resulted in three different clusters. Besides all the segmentation, i.e., product categories, year-wise, fiscal year-wise, and month-wise, the approach also includes the transaction status and seasons-wise segmentation. This segmentation will help the retailer improve customer relationships, implement good strategies, and improve targeted marketing.

With an increase in the number of processing cores or systems, the high-performance edge-computing system’s power consumption along with its computational speed will increase, essentially. However, this comes at the expense of high-energy utilization. One notable solution to reduce the energy consumption of these systems is to execute these systems at the slowest feasible speed so that the job’s deadline times are met. Unfortunately, this method is at the expense of more response time and performance loss. To resolve this issue, in this paper, we propose a scheduling approach that associates the genetic algorithm (GA) with the first feasible speed (FiFeS) technique i.e. GA-FiFeS algorithm. This does not jeopardize real-time tasks’ deadlines. The GA-FiFeS algorithm proposes an energy-efficient schedule while still ensuring high response times. The results of the proposed approach, using plausible assumptions and experimental parameters, are compared with currently in-practice approaches, i.e. FiFeS and LeFeS (least feasible speed) approaches. Using numerical simulations and plausible assumptions, our investigation suggests that the proposed GA-FiFeS technique outperforms the FiFeS technique in terms of energy consumption (~18.56%) and response times (~2.78%). Furthermore, the GA-FiFeS has comparable outcomes with the LeFeS method while taking the expected time of execution as an assessment feature for analysis.

Ever since its emergence, the concept internet of things (IoT) has been applied in many fields. In the area of medical sciences, a new concept “Internet of Medical Things” (IoMT) has been explored. IoMT establishes a connection between humans & machines and serves both of them. It has been expected that, by 2025, services of IoMT would reach the entire world. IoMT has covered a wide scope pertaining to health but unfortunately been facing many security challenges. Healthcare systems consist of sensitive and significant data, which is unorganized and noisy and needs additional power to be calculated for effective analysis & workable results. This data is worked upon for the purpose of making critical decisions. Therefore, it has become the main target of Cyber Criminals. The need of robust security and privacy (S&P) is gradually increasing as more and more devices are getting connected to the IoMT. The S&P of the IoMT has now become a great challenge, considering the utmost significance and vulnerability of the data in the healthcare industry. Lack of sufficient S&P in IoMT devices keeps the patients’ privacy at high stake. This research is intended to propose a Security Model to cope with these Security threats, attacks, issues and challenges. The proposed model has been developed by thoroughly investigating all the major security models through a detailed systematic literature review. The SLR has been conducted to explore all the security threats, security attacks, security issues and security challenges. Extensive meta-analysis has been performed for each of the defined category in order to prioritize these risks. After analyzing these risks, a comprehensive security model has been proposed. The interface has been developed in Python which is well structured, user friendly and easy to implement. The developed module not only identify and prioritize the risks but also automatically control different level of threats. The developed system also contain user intimation modules in case of any threat. This research is based on a very flexible and comprehensive model, which would be highly beneficial to future researchers who desire to work on existing models for the improvement as well as to those who wish to create new security models for IoMT.

The Internet and Big Data expansion have motivated the requirement for more generous stockpiling to hold and share information. Against the current era of information, guaranteeing protection and security to individuals sending data to each other is of utmost importance. The only file type that is instantly and widely used is the image. Therefore, to secure transmission, it is necessary to develop a mechanism to safeguard user data transmission. Considering this thought, it is necessary to analyze the best file type of image for essential criteria of image steganography, such as Payload, Robustness, Imperceptibility, etc., to challenge the weakness of the current algorithms. The widely used image formats are PNG, TIFF, JPEG, BMP, and GIF, which is the cause of existing methods. However, in this case, the critical softness is the credibility of the steganography, which plays a vital role in these format images to ensure the end users communicate. In this paper, a single algorithm provides several advantages for various types of images used as cover objects. However, after the critical and comparative analysis of different perspectives and some assessment metrics, the experimental results prove the importance, significance, and promising limits for these image formats by accomplishing a 4.4450% normal higher score for PSNR correlation than the next best existing methodology. Besides, in PSNR with a variable measure of code implanted in similar pictures of similar aspects, the proposed approach accomplished a 6.33% better score. Encrypting similar code sizes in pictures of various dimensions brought about a 4.23% better score. Embedding the same message size into the same dimension of different images resulted in a 3.222% better score.

Frequent itemset mining (FIM) is a fundamental task in data mining with applications ranging from market basket analysis and recommendation systems to fraud detection and bioinformatics. However, mining frequent itemsets from massive datasets remains computationally challenging due to high execution time and memory consumption. Traditional algorithms such as Apriori suffer from exponential candidate generation and repeated database scans, FP‐Growth incurs high memory overhead from tree construction, and Equivalence Class Transformation (ECLAT) faces expensive tid‐list intersections. To address these limitations, we propose RPriori, a novel recursive divide‐and‐conquer algorithm that operates on the item‐space instead of the transaction‐space. RPriori recursively partitions the set of items and terminates early when a subset is found frequent, thereby eliminating candidate generation and avoiding global data structures. It performs localized support counting and merges only verified frequent subsets, resulting in O (NMlogM) time complexity and O (M) space complexity. Extensive experiments on six benchmark datasets, namely, Bakery, Chess, Mushroom, Retail, Accident, and T10I4D100K, demonstrate that RPriori consistently outperforms Apriori, FP‐Growth, ECLAT, and TL‐DIC. At low support thresholds, RPriori achieves up to 91% reduction in runtime and 93% reduction in memory usage, confirming its superior scalability and efficiency for both sparse and dense datasets.