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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Venkataramanan, N. Jayalakshmi, V. Anitha, S. Ganesan, R. Jenifer, V. Drbccc Hindu College Department of Computer Applications Pattabiram Chennai600072 India Department of Computer Science and Applications Ramapuram600089 India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai600119 India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Department of Computer Science and Applications Ramapuram Campus 600089 India

ISBN: (纸本)9798331509675

This research study reviews the significance of digital forensic analysis in modern criminal investigations, highlighting the way it complements conventional methods of inquiry and the difficulties it encounters in the information era. To solve crimes effectively, investigators must combine digital knowledge with traditional forensic techniques. This will enable them to use evidence from many different types of digital and physical sources. The specific intricacies of digital crime scenes need specialized methods for the proper storage and analysis of digital evidence to identify criminal intent and patterns of conduct. To propose Modern Techniques for Digital Evidence Management [MT-DEM] based Digital Forensics Service. Multidisciplinary teams and cutting-edge technology are becoming necessities in digital forensic investigation as the field adjusts to new threats like data encryption and massive amounts of data. The admissibility of evidence in the legal world depends on digital forensic procedures being transparent and adhering to set criteria. Digital forensic analysis has become an indispensable tool for contemporary investigators with the rise of digital evidence. © 2025 IEEE.

关键词： Forensic science

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Hyperparameter Optimization of Software Quality Model Using Improvised Genetic Algorithm and EEM 1

Hyperparameter Optimization of Software Quality Model Using ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Singh, Rakhi Sharma, Vishnu Kaushik, Neeraj Malhotra, Ritu Rauthan, Hemlata Delhi Institute of Higher Education Department of Computer Application Greater Noida India ITS Engineering College Department Of Computer Science and Engineering Greater Noida India Galgotias College of Engineering and Tehnology Department of Computer Applications Greater Noida India GNIOT Institute of Professional Studies Department of Computer Applications Greater Noida India Delhi Institute of Higher Education Department of Computer Applications Greater Noida India

ISBN: (纸本)9798350356816

Software quality analysis has become one of the topmost concerns in the modern world and requires the massive attention of developers and testers during software development. Software quality analysis is a multidimensional notion and one attribute may not describe the software product's overall quality. Software developer's objective is to build a software product that attains the highest quality which depends on multifarious attributes namely functional suitability, maintainability, etc. However, analysis of software product quality is challenging in both the development and maintenance phases. This research is aimed at hyperparameter optimization of the software quality model using an improvised genetic algorithm and an enhanced ensemble model (EEM) approach. In this research, there has been shown the optimal possibility of software quality model hyperparameter optimization using the modified architecture of genetic algorithm along with EEM. To validate the performance of this suggested approach, PROMISE Repository datasets have been utilized. As a result, this proposed approach achieves all performance metrics very optimal. The evaluated values of accuracy, precision, recall, and F1- score are 99.45%, 98.83%, 97.28%, and 99.37%, respectively. In the future, this approach may be integrated with modified architectures of software quality models for automated software product quality analysis. © 2024 IEEE.

关键词： Software quality

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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A Hybrid Deep Learning Model with Consensus-Based Feature Selection for DDoS Attacks Detection in SDN 4

A Hybrid Deep Learning Model with Consensus-Based Feature Se...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kachavimath, Amit V. Narayan, D.G. Department of Master of Computer Applications KLE Technological University Hubballi580031 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India

Software-Defined Networking (SDN) increases network flexibility by decoupling network control from hardware. However, this also makes networks more vulnerable to Distributed Denial of Service (DDoS) attacks, that can severely disrupt operations. Existing detection methods typically focus on specific DDoS attacks, highlighting the necessity for more comprehensive detection strategies. Our proposed methodology presents a resilient technique for detecting DDoS attacks by employing a hybrid deep learning approach. Utilizing the InSDN dataset tailored for SDN environments, we employ an advanced feature selection process based on a consensus approach to identify the best eight features, enhancing detection accuracy and efficiency. Cross entropy is utilized at the control plane to detect anomalous activity by computing the entropy between source IP (Internet Protocol) and destination IP data to identify DDoS attacks. Our proposed model integrates Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) networks, achieving a detection accuracy of 99.9%. The CNN provides efficient spatial feature extraction, while LSTM captures temporal dependencies, enhancing the model's capability to detect complex attack patterns. We also implement a comprehensive evaluation framework, including metrics such as model loss, model accuracy, and confusion matrix. © 2025 The Authors. Published by Elsevier B.V.

关键词： Feature Selection

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Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware Strategies in Wireless Sensor Networks

Enhancing Data Aggregation Efficiency: Dynamic Energy-Aware ...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Priya, Naincy Sinha, Priyanka Singh, Pranita Jain, Arpit Kumar, Munish GLA University Department of Computer Engineering & Applications U.P. Mathura India Muzaffarpur Institute of Technology Department of Computer Science & Engineering Bihar Muzaffarpur India Government Engineering College Khagaria Department of Computer Science & Engineering India Roorkee Institute of Technology Department of Computer Science and Engineering Roorkee India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram AP India

ISBN: (纸本)9798350394412

In today's surplus world, wi-fi sensor networks are essential for many systems. This network encapsulates sensor nodes powered by irreplaceable batteries, preserving a fixed topology for displaying specific locations. Their efficiency makes it crucial for large data packages. Wireless sensor networks continuously analyze physical factors such as temperature, pressure, ambient soft, sunlight and humidity, dominating the process Energy consumption monitoring is a key task in this area. To address this framework, this paper proposes a different approach: a modified controlled, privacy-controlled approach to record collection. The method aims to reduce redundant sensitivity, thus increasing system lifetime by reducing the frequency of data transfer between nodes and large sinks A comparative study with existing methods shows how the proposed method is effective. Performance findings show that the proposed bundling method (MEBPP) provides high packet transmission values while consuming less power and extending network lifetime. © 2024 IEEE.

关键词： Data aggregation

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Heart Disease Prediction Using Tree Structured Parzen Estimator Optimization 5

Heart Disease Prediction Using Tree Structured Parzen Estima...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Hemalatha, S. Kavitha, T. Sivabalaselvamani, D. Karthika, R.T. Hariprasath, N. Desikan, R. Kongu Engineering College Department of Computer Applications Erode India School of Information Science Presidency University Bengaluru India

ISBN: (纸本)9798331530013

Heart diseases involve diseases of the heart and blood vessels like coronary artery disease, arrhythmias and congenital heart diseases. Electrocardiogram or ECG is a test that assesses the activity, rhythm and electrical conduction of the heart and is paramount in diagnosing heart disorders. It should, however, be noted that even though the ECG is important in heart conditions diagnosis, it cannot by itself be a heart disease predictor, but still crucial in determining the health of the heart. TPE is a type of Bayesian optimization algorithm that is predominantly applied in hyperparameter tuning of predictive models. TPE can efficiently search the hyperparameter space as it creates a probabilistic model of the objective function and at every iteration pushes the most optimal hyperparameters and therefore increases the entire model's performance. This approach acts to augment the effectiveness of the models, particularly in complex prediction tasks. Convolutional Neural Networks (CNNs) are multi-layer perceptron neural networks used for images recognition, hence composed of filter layers, pooling layers, and fully connected layers. Their advantages include the ability to capture spatial features making them powerful in image classification and object detection tasks. The combination of TPE optimization with CNNs results in improved performance because hyperparameter tuning is automated and an optimal setting which maximizes the accuracy all that is required, with values as high as 87.3% accuracy in heart disease prediction. © 2024 IEEE.

关键词： Multilayer neural networks

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Optimizing Retinal Fundus Images for the Automated Diabetic Retinopathy Diagnosis 2

Optimizing Retinal Fundus Images for the Automated Diabetic ...

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2nd Jordanian International Biomedical engineering Conference, JIBEC 2024

作者： Awad, Samer Gharaibeh, Yazan Obeid, Abeer Awwad, Sari The Hashemite University Department of Biomedical Engineering Zarqa Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan

ISBN: (纸本)9798331539542

Diabetic retinopathy (DR) is a common eye condition arising from the complications associated with diabetes. The diagnosis of DR is traditionally carried out by experienced clinicians who screen fundus images to identify numerous small features. This process is challenging, time-consuming, and susceptible to errors. Various studies have successfully utilized different machine learning techniques for the automated diagnosis of DR. The performance of DR diagnosis can be influenced by the preprocessing of the images. In this study, the impact of unifying image resolution and field of view on the performance of machine learning-based automated DR diagnosis was evaluated. This approach ensured that the sizes of the various internal parts of the retina were effectively standardized across different images. Over 32,000 images from two different datasets obtained from *** were utilized. Two distinct classification experiments were conducted on the two datasets using an Inception convolutional neural network, both with and without the proposed method. The proposed method resulted in accuracies of 78.8% and 96.8% for dataset 1 and dataset 2, respectively, compared to accuracies of 78.1% and 95.9% without this method. © 2024 IEEE.

关键词： Adversarial machine learning

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Say No to the Poisonous Fungi: An Effective Strategy for Reducing 0-1 Cost in FungiCLEF2024 25

Say No to the Poisonous Fungi: An Effective Strategy for Red...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： Tan, Bao-Feng Li, Yang-Yang Wang, Peng Zhao, Lin Wei, Xiu-Shen School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Nanjing China

The FungiCLEF2024 competition endeavors to precisely identify fungi species leveraging both metadata and image analysis. Pivotal to the success of this competition are two crucial evaluation metrics: minimizing the error rate and the 0-1 cost loss resulting from misclassification. To reduce the identification error rate, we introduce a Dynamic MLP framework, drawing inspiration from [1]. This approach effectively integrates image and metadata embeddings through recursive blocks, utilizing matrix multiplication for deep fusion of information. To further address the issue of 0-1 cost, we devise a novel probability-based screening strategy, which initially consolidates poisonous fungi categories into a single class, then employs marginal expected loss and a threshold parameter α to optimize the recall rate for poisonous species. These approaches significantly reduce the error rate and 0-1 cost associated with misclassification and achieve a score of 0.5548 on the private leaderboard, securing the third-place ranking. The code is available at https://***/bftan1949/FungiCLEF2024. © 2024 Copyright for this paper by its authors.

关键词： 0-1 cost loss Fine-grained image recognition Fungi Species Identification Open-Set

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An Effort Toward Localization and Recognition of Elevation Values in a Topographic Sheet 7th

An Effort Toward Localization and Recognition of Elevation V...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Pradhan, Ashis Supriya, Sneha Pradhan, Mohan P. Pradhan, Ratika Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Gangtok India Department of Computer Applications Sikkim University Sikkim India

ISBN: (纸本)9789819764648

This study focuses on efficient elevation value localization and recognition in topographic sheets (TS) through morphological operations and YOLO-based deep learning. The aim is to enhance the digitization process, crucial for creating Digital Elevation Models (DEMs) widely used in various applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Miocene

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RECRIoT-MAC: A Reliable and Energy Efficient Resource Management for Internet of Things Based Cognitive Radio Multi- Channel MAC Protocol

RECRIoT-MAC: A Reliable and Energy Efficient Resource Manage...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Currently, the overcrowding of unlicensed spectrum bands is causing severe interference to IoT devices embedded with sensors. CR-IoT networks apply cognitive radio capabilities to the internet of things to solve the spectrum scarcity issue in conventional IoT networks. An CR radio detects available spectrum channels by dynamically modifying its programming and configuration. IoT, smart cities, and 5G all benefit from cognitive radio applications. A cognitive radio application is useful for IoT, smart cities, and 5G. Compared to benchmark Cognitive Radio MAC (CR- MAC) protocols, the proposed protocol is more reliable, intelligent, and smart. CR technology for IoT applications can be advanced by us in order to make IoT more affordable and applicable. It is demonstrated by comparing the proposed RECRIoT-MAC protocol with benchmark CR-MAC protocols that is more energy efficient and consumes less communication time. This paper examines RECRIoT-MAC's energy consumption and throughput. © 2024 IEEE.

关键词： Cognitive radio

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