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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Reethika, A. Lavanya, S. Sheetal, Attar Mahay Vanitha, C.N. Priyadharsini, S. Jenolin rex, M. Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Mahendra College of Engineering Salem India

ISBN: (纸本)9798331541217

Today's networking and telecommunications industries greatly benefit from the growth of highly sturdy and energy-effective Internet of Things through communication sensors. Device efficiency may be increased while sensor energy consumption is reduced by calibrating sensors to use less power. Reinforcement learning (RL), which has garnered significant interest from researchers, is being widely applied to achieve intelligent automation in several academic disciplines today. Resource optimization has not received much attention in research, despite the Internet of Things using a large variety of sensors. By investigating a revolutionary approach to power conservation with the use of reinforcement learning, the creative study aims to produce a novel generation of Internet of Things with standardized power sources to optimize source consumption. A closed-grid, numerous power source controlling system for sensor consumption has been revealed. Our recommended strategy, which uses Deep Q learning, can help Internet of Things sensors optimize their operation. This study focused only on energy-effective sensor and simulation results show that the planned approach works well. © 2024 IEEE.

关键词： Telecommunication industry

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Data Analytics in Farming: Crop Yield Projection in Telangana State Employing Machine Learning Methods 5th

Data Analytics in Farming: Crop Yield Projection in Telangan...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Himaanshu, V. Suryanarayana, G. Prakash, L.N.C.K. Vignesh, Vamshi Srikar, B. Sai Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Telangana state is in the southern part of India where the Godavari and Krishna rivers flow providing important water resources for agriculture. Agriculture is the primary occupation of the major population in the state, and it is also one of the essential sectors that majorly contributes to the state’s economy. Accurate estimation of crop yield production is essential for sustainable agricultural development, food security, and poverty reduction. Therefore, various supervised machine learning procedures have been used to forecast the crop yield production in Telangana State. In this work, various algorithms are applied to the Telangana crop yield dataset. The results generated by these algorithms have been analysed and compared. With the help of crop yield analysis, farmers know their yield before their farming practice so that they can reveal and resolve their crop yield challenges. This article suggests a machine learning algorithm for the farmers to detect and solve their crop yield problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Trust Based Cooperative Secure Routing Protocols for Internet of Things (IoT) 2

Trust Based Cooperative Secure Routing Protocols for Interne...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mekala, Sagar Ramji, Bhukya CVR College of Engineering Dept. of Computer Science and Engineering Hyderabad India Kakatiya Institute of Technology and Science Dept. of Computer Science and Engineering Hanmakonda India

ISBN: (纸本)9798350366570

The Internet of Things has rapidly emerged and continues to create services, software, sensors-embedded devices, and protocols. IoT allows physical objects to communicate, exchange information, and make decisions while performing critical jobs. The Internet of Things is enhanced by wireless sensor networks, which serve as a continuous layer. To gain adoption and spread, Internet of things-based sensor networks must overcome serious general and specialized risks and technological hurdles. The main drawbacks of wireless sensor networks are insufficient resources. Internet of Things (IOT)-based Wireless Sensor Networks have issues in energy consumption, longevity, throughput, routing, and security. The goal of this effort was to enhance network longevity, throughput, packet latency/loss, and better encounters with hostile nodes. Consider wireless energy harvesting in the proposed three-layer cluster-based wireless sensor network routing protocol to enhance network lifespan. The proposed approach is a Trust based Cooperative Secure Routing Protocol (TCSRP) technique with security system to obtain malicious activities of objects and to slant them into blacklist. Finally, experiments were done to test the methodology. It outperforms most traditional routing protocols like AODV, DSR, and DSDV. © 2024 IEEE.

关键词： Network layers

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IoT-Based Classification of COVID-19 Cases with Cardiovascular Disease Using Deep Convolutional Decision Trees 14th

IoT-Based Classification of COVID-19 Cases with Cardiovascul...

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14th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Amudha, R. Kavitha, M.S. Karthik, S. Biju, Balakrishnan Department of Information Technology Hindusthan College of Engineering and Technology TamilNadu Coimbatore641032 India Department of Computer Science and Engineering SNS College of Technology TamilNadu Coimbatore641035 India Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur Chennai600069 India

ISBN: (纸本)9783031811678

The COVID-19 has underscored the need for advanced healthcare solutions. This research addresses the intersection of COVID-19 and cardiovascular disease (CVD) through the lens of Internet of Things (IoT) and deep learning. Patients with pre- existing cardiovascular conditions face elevated risks when infected with COVID-19. Current diagnostic methods often lack the precision required to identify specific health risks in this vulnerable population. This research aims to bridge this gap by developing a sophisticated model that combines IoT data and deep learning techniques for robust classification of COVID-19 cases with concurrent cardiovascular disease. While existing studies explore either COVID-19 classification or the relationship with cardiovascular conditions separately, there is a noticeable research gap in the integration of IoT and deep convolutional decision trees for a comprehensive analysis. This study fills this void by proposing a novel approach that harnesses the potential of both technologies to improve diagnostic accuracy. With the aim of enhancing classification accuracy, we propose an IoT-based framework that leverages deep convolutional decision trees. Our methodology involves the collection of diverse IoT data streams, including vital signs and patient activity, to create a comprehensive dataset. Deep convolutional decision trees are then employed to extract intricate patterns and relationships from the data. The model is trained on a well-curated dataset, optimizing its ability to accurately classify COVID-19 cases in individuals with pre-existing cardiovascular conditions. The results demonstrate a significant improvement in classification accuracy compared to traditional methods. The model exhibits enhanced sensitivity and specificity, showcasing its potential for early and precise identification of COVID-19 cases in individuals with cardiovascular disease. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engin

关键词： COVID-19

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Innovative Skin Melanoma Diagnosis using Deep Learning and African Gorilla Troops Optimizer 1

Innovative Skin Melanoma Diagnosis using Deep Learning and A...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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Sentiment Analysis of Amazon Alexa Product Reviews: A Comprehensive Comparative Study of Learning Algorithms 5th

Sentiment Analysis of Amazon Alexa Product Reviews: A Compre...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rao, Gouravelli Akshith Prakash, L.N.C.K. Suryanarayana, G. Joshua, Pathi Varun Reddy, Katta Nithin Kumar Karnati, Ramesh Computer Science and Engineering Vardhaman College of Engineering Hyderabad India CVR College of Engineering Hyderabad India Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (纸本)9789819780426

Sentiment analysis is part of text mining, which means extracting data from comments on specific products and people’s opinions or sentiments. It involves considering opinions, analysis, and suggestions to customers about Amazon Alexa products by classifying a review as positive, negative, or neutral. This work is to explore different learning algorithms to solve sentiment analysis on Amazon products. The proposed work is a comparative analysis of existing learning algorithms that can help businesses in real-time decision-making by providing insights into customer preferences, needs, and choices which in turn results in the growth of the company and enhancement of customer satisfaction. The existing works provided the accuracy of classification to a level of 80%. Algorithms used in previous projects are SVM, Naïve Bayes, Random Forest, and other learning algorithms. In this work, logistic regression, Gradient boosting, Multinominal NB, and Gaussian NB are used along with previously implemented algorithms for sentiment analysis. Deep learning algorithms such as Long Short-Term Memory (LSTM) and ANN with dropout and without dropout layers are also implemented and compared with machine learning algorithms. Word2Vec is employed with LSTM to increase the accuracy of the representation. Based on the evaluation of their metrics, accuracies are compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Logistic regression

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Energy-Aware Scheduling for Tasks with Target-Time in Blockchain based Data Centres

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computer Systems science & engineering 2022年第2期40卷 405-419页

作者： I.Devi G.R.Karpagam Department of Computer Science and Engineering PSG College of TechnologyCoimbatore641004India

Cloud computing infrastructures have intended to provide computing services to end-users through the internet in a pay-per-use *** extensive deployment of the Cloud and continuous increment in the capacity and utilization of data centers(DC)leads to massive power *** intensifying scale of DCs has made energy consumption a critical *** paper emphasizes the task scheduling algorithm by formulating the system model to minimize the makespan and energy consumption incurred in a data ***,an energy-aware task scheduling in the Blockchain-based data center was proposed to offer an optimal solution that minimizes makespan and energy *** estab-lished model was analyzed with a target-time responsive precedence scheduling *** observations were analyzed and compared with the traditional scheduling *** outcomes exhibited that the developed solution incurs better performance with a response to resource utilization and decreasing energy *** investigation revealed that the applied strategy considerably enhanced the effectiveness of the designed schedule.

关键词： Cloud computing taskscheduling blockchain energy-aware virtual machine

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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Machine Learning Vigilance: Safeguarding Networks Through Cyber Attack Detection 12th

Machine Learning Vigilance: Safeguarding Networks Through Cy...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Sangeam, Ruchitha Ala, Rajitha Punyamurthy, Bheemeshwar Kanagala, Sai Sriharsha Kesari Swain, Nirmal Swetha, Rame Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderbad India

ISBN: (纸本)9789819601424

Embracing the latest technological strides holds immense promise for individuals, organizations, and governments. However, divergent perspectives exist on these advancements, particularly regarding safeguarding critical information, securing stored data platforms, and ensuring data accessibility. A pivotal challenge in the contemporary landscape is the escalating threat of digital terror ism, causing disruptions for individuals and institutions. This form of cybercrime, orchestrated by diverse entities like criminal organizations, skilled individuals, and digital activists, now poses a significant risk to public and national security. Intrusion Detection Systems (IDS) have been developed to address this pressing issue. The study leverages the recently introduced UNSWNB dataset to evaluate the efficacy of the chosen approach. The SVM algorithm demonstrated a commendable accuracy of 83.29. The company demonstrated its ability to effectively counter cyber threats by showcasing its potential. Moreover, they utilized a convolutional neural network (CNN) to achieve this feature outperformed, and achieved an accuracy level of 99.52 with your cybersecurity project. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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