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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Nagender, Y. Ravichand, M. Kocherlakota, Nirupama Mary, S. Shyni Carmel Bharathi, S. Kavitha Kalaivaani, P.C.D. School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Vijayawada India Department of Linguistics MIT World Peace University Maharashtra Pune India Assistant Professor in Business Analytics Loyola Institute of Business Administration Loyola College Campus Chennai600 034 India Department of Computer Technology Kongu Engineering College Perundurai India Department of CSE Kongu Engineering College Perundurai638060 India

ISBN: (纸本)9789819727155

Recent years have seen tremendous progress in Natural Language Processing (NLP), particularly in word and document illustration. Conventional methods that relied on contextual embeddings have given way to creative alternatives. In NLP, pre-trained language models have shown remarkable effectiveness, resulting in a fundamental shift away from supervised learning and towards pre-training accompanied by fine-tuning. Enhancing pre-trained models has attracted a lot of research attention in the NLP community. The taxonomy of pre-trained models is introduced, along with a thorough assessment of current advancements and representative work in the field of natural language processing. We initially provide a quick overview of pre-trained models, then go on to distinctive architectures and techniques. Next, we present and examine the implications and difficulties associated with pre-trained models and their subsequent uses. We wrap up quickly and discuss potential future research options in this area. These developments provide more accurate and effective ways to organise and extract data from large textual collections. In conclusion, this study addresses the intrinsic drawbacks of contextual embeddings and emphasises the critical role that word and document illustrations play in advancing NLP. These cutting-edge methods offer a viable avenue to improve natural language comprehension and utilisation as NLP continues to develop, with extensive applications in domains including information retrieval, machine translation, and sentiment analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Embeddings

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A Novel Approach of Topology Control in Underwater Sensor Networks 2

A Novel Approach of Topology Control in Underwater Sensor Ne...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Sutagundar, A.V. Halakarnimath, B.S. Bhajantri, Lokesh B. Nalini, N. Basaveshwar Engineering College Electronics & Communication Engineering Karnataka Bagalkot India S. G. Balekundri Institute of Technology Computer Science & Engineering Karnataka Belagavi India Basaveshwar Engineering College Department of Information Science and Engineering Karnataka Bagalkot India Nitte Meenakshi Institute of Technology Professor & Dean Student's Welfare Department of Computer Science & Engineering Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The wide spectrum of marine applications has drawn the research community to discuss the several issues of UASNs. Acoustic communication is the most relevant channel for communicating information among sensor nodes that utilize more energy to avoid network failure and render continuous connectivity. The Energy-efficient Topology Control (ETC) is proposed to discuss this issue for UASNs to overcome the coverage and connectivity by improving their lifetime. The proposed approach is divided into the topology construction and topology maintenance phase. In the construction phase, initially, each node recognizes the neighbor nodes and records the range and orientation in an internal database. After that, each node drops some nodes by applying specific conditions and forms the candidate neighbor nodes. Finally, the minimum number of sensor nodes is added to the Topology Member Set (TMS) based on the nodes' orientation, range, energy levels, and topology of the network are created. The topology's maintenance is initiated by the sink node periodically, or any sensor node can send a request message to the sink node at particular conditions. The proposed scheme's performance shows the necessity for minimal sensor nodes for seamless connection and improves coverage area. © 2022 IEEE.

关键词： Sensor nodes

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ML) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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Empirical Analysis of Software Success Rate Forecasting During Requirement engineering Processes

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computers, Materials & Continua 2023年第1期74卷 783-799页

作者： Muhammad Hasnain Imran Ghani Seung Ryul Jeong Muhammad Fermi Pasha Sardar Usman Anjum Abbas Lahore Leads University Lahore42000Pakistan Computer and Information Sciences Virginia Military InstituteLexingtonVA24450USA Graduate School of Business IT Kookmin UniversitySeoul136Korea School of Information Technology Monash UniversityBandar Sunway47500Malaysia Department of Computer Science and Software Engineering Grand Asian University Sialkot51040Pakistan Government Sarwar Shaheed Associate College Gujjar KhanRawalpindi46000Pakistan

Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development *** order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success *** discussed a potential relationship between forecasting of software success or failure and the development *** noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development *** analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success *** results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’*** has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software *** empirical study highlights a need for other software practitioners to avoid such bias while working on software *** investigation can be performed to identify the other human factors that may impact software systems’success.

关键词： Cognitive bias misinterpretation of requirements miscommunication software success and failure prediction decision making

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An Efficient and Lightweight Intrusion Detection System for Mobile Ad Hoc Networks

An Efficient and Lightweight Intrusion Detection System for ...

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International Conference on Recent Advances in Design, Materials and Manufacturing, ICRADMM 2020

作者： Vali, Y. Sharmasth Prakash, N. Shakkeera, L. Research Scholar Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Associate Professor Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Associate Professor School of Computing Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789811640827

The distinctive aspects of Mobile Ad Hoc Networks (MANETs), including dynamic network topology, susceptible wireless medium, and limited battery power are highly vulnerable to various types of invasion. The traditional Intrusion Detection System (IDS) prevents the network from intruders using observation-based techniques and security algorithms. However, the limited battery power and network overhead is still a major issue to design an IDS. This paper proposes a Distributed and Cooperative Lightweight Intrusion Detection (DCLID) system to discern and eradicate the intruders from the network. The proposed system includes local detection engine, local analyzer, and trust management system. A local detection engine continuously monitors the network activity and stores the monitoring data in a table. A local analyzer examines the monitoring data periodically to evaluate a threshold based on network traffic and network connectivity. An extra field is added to a routing protocol to store the observed data for threshold measurement. The DCLID system is a lightweight system, it only turns on when the attack happens in the network and the network lifetime is high owing to lightweight designing. The threshold value measurement is used to trigger the IDS for detecting the intruders based on trust estimation. It estimates the trustworthiness of a node using Dempster–Shafer (DS) evidence theory. The DS theory provides a mathematical way to merge the observed data from different observers for deciding trustworthiness of an intruder. The simulation results demonstrate the efficacy of the DCLID system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Dempster–Shafer theory First keyword Lightweight intrusion detection system MANET Network layer attacks Second keyword Third keyword Trust management

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Prediction of agricultural waste compost maturity using fast regions with convolutional neural network(R-CNN)

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Materials Today: Proceedings 2023年

作者： Sangeetha, J. Govindarajan, Priya Senior Assistant Professor Department of Computer Science & Engineering Srinivasa Ramanujan Centre SASTRA Deemed University Kumbakonam India. Assistant Professor Department of Computer Science AMRITA VISHWA VIDYAPEETHAM Deemed University Mysuru India.

In agriculture production, the enormous volume of wastage production is a major concern. Therefore, composting a massive amount of thrash will transform from agricultural wastage to stabilized, disinfected, and pollution-less yields, allowing important nutrients to be retained and the fertility of soil to be enhanced. Carbon to Nitrogen proportion (C:N) or estimating seed germination index (GI) is the most well-known technique for deciding the compost development of agricultural throwaway, they are both tedious and hard to carry out. Thus, various powerful feature extraction calculations were utilized in the literature. These broadly utilized texture feature extraction strategies could obtain only single-level characteristics, however, the Faster R - CNN model can separate multilevel image attributes since it has a bunch of cascaded layers of convolution and activation functions that could make sense of color and texture features of composts at different levels. Thus, Fast Regions with Convolutional Neural Network(R-CNN) was acquainted in order to rapidly evaluate compost maturity by examining pictures of different composting phases. © 2023 Elsevier Ltd. All rights reserved.

关键词： Feature extraction

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Biometric-Based Access Control Systems with Robust Facial Recognition in IoT Environments 3

Biometric-Based Access Control Systems with Robust Facial Re...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Satish Babu, B.V. Manoranjini, J. Changala, Ravindra Aarif, Mohd Mary, S. Suma Christal Raj, I Infant Prasad v Potluri Siddhartha Institute of Technology Department of It Vijayawada India Chaitanya Bharathi Institute of Technology Department of Ai & Ml Hyderabad India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Associate Editor-Publishing Global Research Network Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India

ISBN: (纸本)9798350361186

The integration of biometric-based access control systems with robust facial recognition technology within Internet of Things (IoT) environments holds paramount significance in addressing contemporary security challenges. The novelty of the research endeavours to contribute to this critical area by investigating and implementing a novel approach, employing Convolutional Neural Networks (CNN) for facial classification within the biometric framework. The significance of this study lies in its potential to enhance security measures in IoT environments, ensuring reliable and efficient access control. Previous attempts at integrating facial recognition into IoT have encountered notable challenges, including suboptimal accuracy, vulnerability to adversarial attacks, and scalability issues. This research builds upon these prior issues by introducing CNN for facial classification, leveraging its capabilities in image processing and pattern recognition to achieve enhanced accuracy and resilience against potential security threats. The novelty of the study is underscored by the application of CNN within the biometric context of IoT, offering a cutting-edge solution tailored to the intricacies of interconnected devices. Experiments conducted on benchmark datasets showcase the promising capabilities of the proposed system, with face recognition accuracies reaching an impressive 98%. These results underscore the potential of this research to significantly advance the state-of-the-art in biometric-based access control, particularly within the context of IoT environments. The utilization of CNN introduces a pioneering element, contributing to the evolution of secure and efficient access control solutions in the era of IoT. © 2024 IEEE.

关键词： Biometrics

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Deep Reinforcement Learning for Energy Efficiency Optimization using Autonomous Waste Management in Smart Cities

Deep Reinforcement Learning for Energy Efficiency Optimizati...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： T. Kavitha Koushik Reddy Chaganti Sivananda Lahari Reddy Elicherla M. Ravi Kumar D. Chaithanya K Manikanta Assistant Professor Department of CSE Srinivasa Ramanujan Institute of Technology Research Scholar Dept. of Computer and Information Systems University of the Cumberlands Associate Professor Department of CSE Dayananda Sagar University Assistant Professor Department of Cyber Security & IoT Malla Reddy University Assistant Professor Department of CSE CVR College of Engineering Assistant Professor Department of CSE Gates Institute of Technology

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Smart cities face vital waste management challenges regarding efficiency and sustainability in their development period. The established waste collection approaches that depend on pre-set schedules together with unchanging routes create problems by possessing both inefficient resource usage and elevated fuel usage as well as delayed waste pickup times. The proposed framework builds a Deep Reinforcement Learning (DRL)-based system for dynamic waste collection route optimization using Autonomous Waste Collection Vehicles (AWCVs) with Internet of Things (IoT)-based smart bins. The system uses Deep Q-Networks (DQNs) to analyze traffic conditions data alongside vehicle battery level data with smart bin information from the system so vehicles carry out efficient waste collection operations using minimal energy. The newly proposed algorithm provides 30% distance reduction and 28% faster responses and 30% lower energy usage than baseline heuristic methods do. The conclusions show that AI-powered waste management strategies can create sustainable operations which increase both service performance and overall energy efficiency in cities. The research team plans to apply the proposed approach to actual waste collection systems while exploring methods of deploying multiple agents alongside renewable energy systems to enhance future urban waste management operations.

关键词： Waste management Schedules Renewable energy sources Smart cities Deep reinforcement learning Energy efficiency Real-time systems Vehicle dynamics Sustainable development Optimization

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management

Resilient Framework for Next-Generation Heterogeneous Wirele...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan S. Balamurugan G.Vinoth Rajkumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Associate professor Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Associate Professor Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments.

关键词： Wireless networks Heuristic algorithms Disasters Focusing Disaster management Network coding Routing Automobiles Next generation networking Resilience

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Rice Category Identification through Deep Transfer Learning Features and Machine Learning Classifiers: An Intelligent Approach

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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