As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusi...
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This work investigates discrepancies in parameters and performance between an induction machine design simulation and the produced unit. The squirrel cage motor is explored analytically and in finite element space bas...
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The study presents and demonstrates an efficiency enhancement of Buck converters by a controlled inductor that achieves BCM mode at fixed frequency. The paper includes analysis, simulation, and experimental verificati...
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With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2...
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With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is *** them,the device-to-device(D2D)communication is one of the considerable *** D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile *** flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless *** of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel *** order to solve this problem of co-channel interference,this paper proposes a graph coloring based *** main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user *** proposed algorithm also provides simpler power *** heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is *** results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.
In today’s era, an enormous quantity of information is generated everyday by the Internet and this information need a specific tool by which we can get a more useful and important part of that information. In this st...
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The harmonic and electromagnetic noise issues by switching operations are becoming more serious due to the enhancement of the fast-switching capability of power devices. A modular cascaded linear amplifier (MCLA) has ...
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Maritime Authorities face significant challenges in monitoring vast maritime domains and enforcing regulations within their Exclusive Economic Zones (EEZs). Synthetic Aperture Radar (SAR) imaging of Fers an effective ...
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Ocular illnesses present a considerable risk to worldwide public health, frequently resulting in visual impairment or blindness if not identified and addressed appropriately. This study focuses on the essential task o...
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Within digital forensics, drone forensics is a specialist profession that investigates and examines unmanned aerial vehicles (UAVs) for evidence. As drones become increasingly common, their digital fingerprints can be...
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In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and p...
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In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and predicting the likelihood of students dropping out. To uncover these influential factors, feature selection methods are employed, while various machine learning models are used to predict students at risk of underperforming. Filter-based feature selection methods are commonly used in educational data mining due to their efficiency and ability to rank important features affecting academic success. However, because of their independence from classifiers and relying on a fixed threshold or predefined feature count, filter-based methods can sometimes negatively affect model performance. To address this, the present study introduces an optimized chi-square-based feature selection technique that dynamically selects the optimal features for each learning algorithm, ensuring that model performance is not compromised. The effectiveness of five classifiers—k-Nearest Neighbour (k-NN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR)—has been evaluated using three configurations: no feature selection, traditional chi-square feature selection, and proposed optimized chi-square based feature selection. These evaluations were conducted on two distinct student datasets, one from secondary schools (DS1) and another from engineering institutions (DS2). The results demonstrated that the optimized chi-square method consistently improved prediction accuracy across all classifiers. Additionally, a bagging-based ensemble classifier, constructed using the best-performing individual classifier, further enhanced predictive performance. The highest accuracies achieved were 94.62% for DS1 and 96.36% for DS2, outperforming traditional feature selection and ensemble methods. This study presents a scalable, reliable, and stable approach to s
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