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Advanced Electromagnetics 2025年第1期14卷 39-51页

作者： Mbida, Joseph Mari Mebara Eyebe, Guy Ayissi Singh, Ghanshyam Mbinack, Clément Yende, Mohamed Njianga Department of Physics Faculty of Sciences University of Yaounde I Yaounde Cameroon Electrical and Computing Engineering Department Université du Québec à Trois-Rivieres Canada Department of Electronics and Communication Engineering Feroze Gandhi Institute of Engineering and Technology Raebareli India

This paper presents a simple and efficient approach for an antenna design and construction based on an I-shaped slot-loaded triple wideband microstrip monopole antenna with novel radio frequency (RF) power/energy harvesting capability. The proposed antenna takes advantage of circular ring shape properties, loaded with an I-shaped slot with degraded ground structure (DGS) to improve the input performances of the conventional planar circular microstrip patch antenna (C-patch). Input impedance and magnetic field radiation pattern are calculated using the electromagnetic theory of transmission line in traveling waves and resonant cavity TE11 field mode, respectively. The proposed antenna is configured on thin lossy substrate material RO4003C of volume 64.800×57.000×1.524 (in mm3), relative dielectric permittivity Εr = 3.55, and numerically investigated using CST MWS. The prototype of the proposed antenna has been fabricated and tested. The experimental results show that the fabricated antenna exhibits an impressive 142% (2.98-11.11 GHz), 21.3% (11.54-14.42 GHz), and 19.6% (15.78-19.44 GHz) fractional bandwidth (FBW) for the resonant frequency of 5.73, 13.55, and 18.69 GHz respectively and peak gain of 4.2 dBi at the frequency of 5.8 GHz. Experimentally, the proposed antenna can be used in multiple applications like 2.4 GHz (2.401-2.495 GHz) Wi-Fi band/5 GHz (5.170-5.875 GHz) WLAN band/(3.1-10.6 GHz) US UWB at the view of the achieved lower band, FR3 6G operation regarding the obtained middle/intermediate band, and the future wireless and millimeter-wave communication systems according to the obtained upper band. © 2025, Advanced Electromagnetics. All rights reserved.

关键词： Directional patterns (antenna)

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SecuDroid : Android Malware Detection using ML classifier on Static Features 2

SecuDroid : Android Malware Detection using ML classifier on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vanusha, D. Singh, Shashank Jha, Abhijeet Kumar Delsi Robinsha, S. School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365269

The Android operating system's widespread use has unfortunately made it a target for malware attacks, underlining the necessity for robust detection strategies. Existing literature has explored the potential of machine learning in combating Android malware, often employing a mix of static and dynamic analyses. However, a major portion of these malwares are zero-day vulnerabilities. In this context, our study makes a significant contribution by focusing on using static features extracted from Android application package (APK) files to differentiate between benign and malicious applications. Specifically, all training and testing in this study were performed on the Drebin-215 dataset, a comprehensive collection known for its relevance in Android security research. Improving malware detection using static features allows for detection of malwares before they are run on devices. We analyzed five ML classification algorithms, beginning with a Decision Tree, followed by SVM (Support Vector Machine) with an RBF kernel, then moving to Logistic Regression and K-Nearest Neighbor, and finally, we introduced our proposed model, the SecuDroid Neural Network - employing a methodology that employs precise hyper-parameter tuning, and evaluation through stratified 4-fold cross-validation. The evaluation on an unseen test dataset, based on accuracy, F1-score, and AUC metrics, revealed the SecuDroid Neural Network surpassed its counterparts by reaching an accuracy of 0.993, an F1-score of 0.991, and an AUC of 0.999. Our findings not only offer a comparative analysis of different machine learning models for malware detection but also introduce an efficient approach, thereby enriching the arsenal against Android malware threats, with all empirical validations grounded in the Drebin-215 dataset. © 2024 IEEE.

关键词： Android malware

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A Hierarchical Hybrid Deep-Net Framework for Water Demand Forecasting Using OPHO Optimization and Deep Learning Techniques

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Iranian Journal of Science and technology - Transactions of Civil engineering 2025年 1-17页

作者： Jeyavelu, Subha Mohan, Kowsigan Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu 603203 India

Intelligent water supply relies heavily on water demand forecasting. Due to nonlinear changes in the water consumption process, traditional prediction models have significant limitations regarding precision and consistency. Water wastage is monitored and the water distribution system is wirelessly monitored via the Internet of Things (IoT) technology. This study introduces a Hierarchical Hybrid Deep-Net Framework for water demand forecasting, combining advanced deep learning architectures and the Osprey-Pelican Hybrid Optimization (OPHO) algorithm to enhance prediction accuracy and efficiency. The framework integrates RNN-LSTM to capture temporal patterns, Caps Net to understand hierarchical relationships, and MobileNetV2 for efficient data processing. The methodology begins with comprehensive data preprocessing, including data cleaning to handle inconsistencies, Min–Max normalization for feature scaling, and feature extraction through statistical measures such as mean, median, standard deviation, and skewness and correlation-based features like Spearman rank correlation. High-level abstract features are extracted using Inception V3, a pre-trained deep learning model. Feature selection is performed using the OPHO algorithm, which combines the precision of Osprey optimization and the local search capabilities of Pelican optimization to identify the most informative features while reducing dimensionality. The framework is validated on a real-world dataset sourced from United Utilities, a water and wastewater management firm in the North West of England, obtained from Kaggle. The proposed model is evaluated for both 70% and 80% of training data for the metrics MAE of 0.0300, 0.0100, MAPE of 0.0119, 0.0223. Also, the MSE, NMSE, RMSE, and IoA have superior performance when compared to the other existing methods. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Hierarchical hybrid deep net Inception V3 Min–max normalization Osprey-Pelican hybrid optimization Water demand forecasting

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Vehicle Detection in Challenging Scenes Using CenterNet Based Approach

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Computers, Materials & Continua 2023年第2期74卷 3647-3661页

作者： Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud Department of Computer Science University of Engineering and Technology Taxila47050Pakistan Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589Saudi Arabia Faculty of Computing and Information Technology Northern Border UniversityRafhaSaudi Arabia

Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road *** major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of *** research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed *** this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely *** DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded *** there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object *** in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by *** train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP *** of CenterNet can be justified through the huge improved *** performance of our model is evaluated on KITTI(Karlsruhe Institute of technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature *** also compare our model with other SOTA techniques.

关键词： CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI

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Anticipating Customer Churn in Telecommunication Using Machine Learning Algorithms for Customer Retention 3

Anticipating Customer Churn in Telecommunication Using Machi...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Rajarajan, K. Vishal Reddy, D. Priya, S. College Of Engineering And Technology Srm Institute Of Science And Technology Department Of Computing Technologies Kattankulathur India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318609

Customer attrition is a significant issue and a top priority for large corporations. This study addresses this pressing issue. The dataset is obtained from Kaggle which constitutes of training set and testing set, 80 percent and 20 percent of the entire dataset respectively for identifying customers who tend to unsubscribe in the telecommunications industry. Predicting customer churn with precision is a difficult endeavor, mostly due to the dependence on a singular prediction model in most existing projects. To address this, this study proposes a novel approach that compares multiple prediction models like logistic regression, KNN, random forest, SVV, Gaussian NB, Kernel SVM, Support Vector Machine, to estimate customer churn. With ROC AUC Mean score of 84% for Logistic Regression produced a better result. In the future, the study might be expanded to investigate the evolving behavioral patterns of churn consumers with the application of Artificial Intelligence approaches for predictive and trend analysis to preserve valuable client. © 2024 IEEE.

关键词： Logistic regression

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Artificial intelligence-inspired comprehensive framework for desertification prediction

International Journal of Cognitive Computing in Engineering

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International Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis from Medical Imaging

HealthScan AI-Deep Learning-Based Multi-Disease Diagnosis fr...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Ganesh, Prudhvi Sai Kiriti, Chakali Murthy Gari Keerthi Rama, P. Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India College of Engineering and Technology SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies TN Kattankulathur603203 India

ISBN: (纸本)9798350375237

The HealthScan AI is a comprehensive health diagnosis AI system which utilizes deep learning algorithms and the Keras library to determine if a person has certain diseases or not using their chest x-rays and other scans. It utilizes six distinct CNN models for the identification of COVID-19 and pneumonia using the chest X-rays, Glaucoma from internal eye scans. Our methodology involves rigorous training and evaluation processes, resulting in a highly accurate and efficient model. The integration of these models into a user-friendly interface, developed with Streamlit, allows seamless interaction. This interface empowers users to upload medical images, facilitating real-time diagnostic feedback on their health status. By providing a flexible and effective tool for the identification of a range of disorders, this novel technique advances the field of medicine and has the potential to completely transform healthcare procedures. © 2024 IEEE.

关键词： COVID-19

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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Computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Design of a Secured and Trusted E-Voting System Using Blockchain technology

Design of a Secured and Trusted E-Voting System Using Blockc...

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2024 IEEE SmartBlock4Africa, SmartBlock4Africa 2024

作者： Elegba Jeremiah, O. Agbelusi, O. Olutayo Victor, A. Akintola Kolawole, G. School of Computing The Federal University of Technology Department of Software Engineering Akure Nigeria School of Computing The Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9798350376838

The state of election in Nigeria is worrisome. Electoral malpractices have been a major challenge to the Nigerian government in recent times. Government has made frantic efforts to tackle these electoral challenges but the recent event in year 2023 presidential election is worrisome suggests that the solution is not near. Recently, the percentage of eligible voters who vote on election days is declining. Voters turnout went up from 52.3% in 1999 - the first general election since 1993 - to 69% in 2003. But, it has been on the decline nearly ever since - 57.5% in 2007, 53.7% in 2011, 43.7% in 2015 and 34.8% in 2019. In 2023, it is 28.63%. With the way things are going, if urgent action is not taken, this could signal the end of democracy in Nigeria. This research presents the design and implementation of a trusted and secured e-voting based on blockchain technology that will increase voters confidence and trust. The system utilizes *** for the frontend and Solidity on the Polygon blockchain for the backend, ensuring transparency, tamper-resistance, and heightened security. This innovative approach addresses trust and reliability concerns in traditional voting systems, offering a modernized and trustworthy electoral process. © 2024 IEEE.

关键词： Blockchain

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