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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Buvanesvari, M. Shivaprakash, M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Thandalam Pachama602117 India Manakula Vinayagar Institute of Technology Pondicherry University Department of Computer Science and Engineering Puducherry605107 India

ISBN: (纸本)9798350377972

Wireless Sensor Network (WSN) is comprised of infinite Sensor Nodes (SNs), which are low-priced, low-power, and minute features. These nodes are data-centric and have the responsibility to gather important data in the pretentious area and transfer the data to the sink node (control base or center station) in a single- or multi-hop technique. To commence WSNs to sustain in a healthy and secure atmosphere, safety devices according to identity authentication and cryptography have been proposed to oppose outside threats on WSNs. Routing is predominant in WSNs for optimizing their efficiency, scalability, and longevity. Routing represents a process of describing the optimum routes for the data transmission from SNs to the sink. Dynamic routing guarantees reliable and timely data transmission however balances the network load and decreases energy intake. WSNs can gain improved routing using metaheuristics that outcomes in lengthy operational lifespans and better performance of the network. This study presents an Optimized Energy Efficient Routing Network using Lévy Flight with Seagull Optimization Algorithm (OEERN-LFSOA) technique in WSN. The presented OEERN-LFSOA technique mainly focusing on enhancing the energy efficiency and lifespan of WSNs using routing approaches. To achieve this, the presented OEERN-LFSOA method involves the integration of the Lévy Flight with Seagull Optimization Algorithm (LFSOA) model. Furthermore, the presented OEERN-LFSOA method obtains a fitness function for exploiting the lifetime and diminishing consumption of energy. A comprehensive set of simulations was directed to show the performance of the OEERN-LFSOA method. The experimental outcomes stated that the OEERN-LFSOA method achieves better performance than other recent techniques. © 2024 IEEE.

关键词： Sensor nodes

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Soil Data Analytics using Machine Learning Techniques - A Survey 3

Soil Data Analytics using Machine Learning Techniques - A Su...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Raghavendra Rao, R.V. Srinivasulu Reddy, U. Mohan Reddy, Ch Ram BMS College of Engineering Department of Computer Applications Bangalore India National Institute of Technology Department of Computer Applications Tiruchirappalli India

ISBN: (纸本)9798350338607

Food security is the most basic need with any individual all over the globe. With food security we can reduce malnutrition in children, who are the backbone of nation's development. To achieve this using machine learning techniques in agriculture domain is the new beginning. Machine learning techniques successfully implemented in innumerable domains, now entered in agriculture. Machine learning techniques establishes a novel way in data processing and data analytics, producing good results with larger data. In this work we carried out survey on machine learning technique's role in agriculture domain and challenges involved in it. We examined the models developed based on agricultural soil. In addition, we analyzed the associations between soil nutrients, which help in crop yield. These results specify that the accuracy of the model is more with ensemble methods. © 2023 IEEE.

关键词： Soils

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Modelling and Performance Analysis of Physical and Electrical Properties of BaZrS3 Chalcogenide Perovskite Solar Cells 24

Modelling and Performance Analysis of Physical and Electrica...

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24th IEEE International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2024

作者： Gahlawat, Devansh Kaur, Jaspinder Basu, Rikmantra Sharma, Ajay Kumar Rani, Uma National Institute of Technology Delhi Department of Electronics and Communication Engineering New Delhi India National Institute of Technology Delhi Department of Computer Science and Engineering New Delhi India Dr. Akhilesh Das Gupta Institute of Professional Studies Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350390537

This study explores the application and utility of BaZrS3-based perovskite solar cells (PSCs) for thin-film photovoltaic applications. The proposed device exhibited stable current up to 1.1 eV and maintained over 65% quantum efficiency across a broad spectrum. The Mott-Schottky analysis indicated a bandgap of approximately 1.7 eV. Nyquist and Bode plots confirmed robust impedance behaviour. The device displayed V oc=1.33 V, Jsc = 16.56 mA/cm2, FF = 83.97%, and Efficiency = 18.58%. BaZrS3's exceptional optical and electrical properties, coupled with environmental stability, position it as a promising material for advanced PSCs. © 2024 IEEE.

关键词： Perovskite solar cells

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Enhancing Cloud Computing Security through Deep Learning and Attention Mechanism Intrusion Detection Systems 4

Enhancing Cloud Computing Security through Deep Learning and...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shukla, Sunil Singh, Jagendra Ramya, T. Rahul, Satyakam Mallick, Arnab Kumar Pandey, Pawan Graphic Era Hill University School of Computing Dehradun India Bennett University School of Computer Science Engineering & Technology Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Mit Adt University School of Computer Science Engineering Pune India Indian Institute of Technology Madras Department of Computer Science Engineering Chennai India Raj Kumar Goel Institute of Technology Department of Computer Science Ghaziabad India

ISBN: (纸本)9798350349900

This research investigates the enhancement of cloud computing security through adaptive neural network intrusion detection systems, employing Deep Belief Networks (DBN) and Attention Mechanism. The study aims to address the growing concerns surrounding security threats in cloud environments by leveraging advanced machine learning techniques. Experimental evaluations were conducted to assess the performance of the proposed approach, with specific emphasis on detection accuracy, false positive rate, and computational efficiency. Results indicate that the DBN-based intrusion detection system achieved an average detection accuracy of 94.4%, with a false positive rate of 2.2% and a computational efficiency of 1 5 8. 4 milliseconds. In comparison, the IDS utilizing Attention Mechanism demonstrated superior performance, with an average detection accuracy of 96.2%, a false positive rate of 1.1%, and a computational efficiency of 153.2 milliseconds. These findings underscore the effectiveness of DBN and Attention Mechanism in bolstering the security posture of cloud computing environments. By integrating adaptive neural network-based intrusion detection systems, organizations can enhance threat detection capabilities, mitigate risks, and safeguard sensitive data and critical assets in the cloud. © 2024 IEEE.

关键词： Cloud security

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Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance 15

Comparative Analysis of Decision Tree with Existing Approach...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pathan, Shabana Sharma, Sanjeev Kumar Department of Computer Science & Engineering Oriental University M.P Indore India Department of Computer Science and Engineering Oriental Institute of Science and Technology M.P Bhopal India

ISBN: (纸本)9798331300579

Data mining has applications for a wide range of enterprises, including those in the banking, telecommunications, energy, medical, marketing, and finance industries. The real-world situations involve noisy, unpredictable, ambiguous, and untrustworthy data, which further muddies the waters. During the information-gathering process, uncertainty arises in various application domains and is comprised of measurement errors, outdated information (data staleness), and repeated entries of the same type. This could lead to the prediction model performing poorly. The suggested method uses Deep Learning to control the uncertainty in the dataset before building Decision Tree models for categorization. This study compares two proposed approaches for decision tree optimization with the state-of-the-art approaches based on F1 score, accuracy, precision, recall, and evaluation parameters. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Developing an ML Model for Detecting the Cyber Attacks in Electric Vehicles 1

Developing an ML Model for Detecting the Cyber Attacks in El...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Roopesh Albert, Bertia Manuel, Alan Shine Hemram, Saimon Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Karunya Nagar Coimbatore641114 India

ISBN: (纸本)9798350372748

This study shows that electric cars (EVs) are a viable replacement for conventional combustion-engine vehicles, providing lower carbon emissions and more environmentally friendly modes of transportation. Depite the hype, the challenges like cybersecurity flaws and possible cyberattacks are also emerging in EVs. In addition to jeopardizing user privacy and vehicle safety, a number of issues seriously jeopardize the integrity and security of electric cars (EVs). This research offers a unique machine learning (ML)-based detection and prevention system designed to identify cyber threats in electric vehicles (EVs) in response to these problems. The suggested solution examines Controller Area Network (CAN) traffic data using cutting-edge ML algorithms to find unusual patterns suggestive of cyber threats. The solution improves the cybersecurity resilience of electric vehicles (EVs) by including proactive preventive measures and real-time threat detection capabilities. This ensures vehicle safety and guards against new and existing cyber-attacks. Through thorough evaluation and validation procedures, the efficacy of the suggested system is proven, underscoring its strong performance in identifying and averting cyberattacks in electric vehicles. This research helps to create safer and more secure mobility solutions for electric vehicles by implementing proactive cybersecurity measures and integrating cutting-edge ML technology. © 2024 IEEE.

关键词： Crime

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An incremental clustering using bat-spotted hyena optimiser with spark framework

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International Journal of Intelligent Information and Database Systems 2023年第2期16卷 167-195页

作者： Vidyadhari, Ch. Sandhya, N. Ramakrishnaiah, N. IT Department Department of Computer Science and Engineering JNTUK Kakinada India VNR Vignana Jyothi Institute of Engineering and Technology Telangana 500090 India CSE Department University College of Engineering JNTUK Kakinada India

Recently, clustering techniques gained more importance due to huge range of applications in the field of data mining, pattern recognition, data clustering, bio informatics and many other applications. In this paper, a new approach called spotted hyena bat algorithm (SHBA)-based incremental clustering with spark framework is proposed. The SHBA algorithm is derived by integrating the spotted hyena optimiser (SHO) and bat algorithm (BA), that is highly desirable for handling high dimensional data and provides a unique solution with high satisfactory results. The process of incremental clustering is performed in a spark framework by considering the master and the slave nodes. The proposed approach effectively clusters the data, especially high dimensional data and is more robust against various attacks and provides more unified solution. Moreover, the proposed SHBA achieves higher performance by considering the evaluation metrics, such as Jaccard coefficient, rand coefficient, and clustering accuracy of 0.950, 0.943, and 0.962. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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Bilingual SMS Spam Detection Using Deep Ensemble Learning

Bilingual SMS Spam Detection Using Deep Ensemble Learning

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Sultana, Hafsa Tanvin, Jamal Uddin Tasnia, Fairooz Sharmin, Nusrat Military Institute of Science and Technology Department of Computer Science and Engineering Mirpur Cantonment Dhaka1216 Bangladesh

ISBN: (纸本)9798331535476

Spam messages have become a pervasive issue in digital communication, posing significant challenges to users and organizations alike. As the volume of text-based interactions continues to grow, particularly in low-resource languages, e.g. Bangla-English SMS messages, the need for effective spam detection systems has never been more critical. This paper introduces BESpam, a novel bilingual dataset comprising 5,730 instances of Bangla-English SMS messages aimed at addressing this gap. We explore the development of an ensemble model that leverages advanced transformer architectures alongside FastText to enhance spam classification performance. Our findings indicate that this proposed ensemble approach outperforms traditional machine learning methods and standalone transformer models, demonstrating the efficacy of combining contemporary natural language processing techniques with established feature extraction methods. The ensemble stacking method achieved a 99% accuracy, surpassing both the individual performances of mBERT (98%) and XLM-RoBERTa (98%), as well as traditional models like Logistic Regression and SVM, both of which scored 98%. This research not only advances the field of s pam detection but also serves as a vital resource for future studies focused on multilingual text processing, particularly in regions where SMS spam remains a pressing concern. © 2024 IEEE.

关键词： Bilingual Ensemble FastText mBERT Meta-model SMS Spam Stacking XLM-RoBERTa

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Comment Analyzer by Sentimental Analysis through Natural Language Processing 10

Comment Analyzer by Sentimental Analysis through Natural Lan...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Madhukar, Gadde Karthik, Gaddamidi Bhargav, Tanneru Nayak, Dungavath Yuva Kiran Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Faculty of Information and Communication Technology Kampar Malaysia

ISBN: (纸本)9798350353068

Comment analyzers were widely employed across industries for sentiment analysis, social media monitoring, and customer feedback evaluation. These tools facilitated insight into public opinions and sentiments expressed in comments on various platforms. Sentiment analysis is a natural language processing technique that determines the emotional tone of the text, helping to categorize it as positive, negative, or neutral, making it valuable for understanding public opinion and customer feedback. With the explosive increase of comments, it is difficult to analyze them manually. The issue arises because online content is huge and complicated, making it tough to grasp the subtle emotions and attitudes people have in their comments. This mystery surrounding digital conversations needs a solution: a Comment Analyzer with Sentiment Analysis. Text categorization in the form of sentiment analysis incorporates NLP, machine learning, data mining, information retrieval, Power BI for data analysis, and other study areas. These limitations highlight the need for more sophisticated sentiment analysis techniques, such as the Vader sentiment analysis provided in this study, to overcome existing system shortcomings and increase sentiment classification accuracy. The Comment Analyzer using Sentiment Analysis represents a pioneering approach to unraveling the cryptic sentiments embedded within the myriad comments and discussions that saturate the digital sphere. We are using data sets such as YouTube video comments and Ecommerce Product comments. © 2024 IEEE.

关键词： Sentiment analysis

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Machine Learning-based Framework for Malware Detection in Critical Infrastructures for Smart Cities

Machine Learning-based Framework for Malware Detection in Cr...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Bhatt, Aditya Dasadiya, Sujal Gohil, Aditya Gupta, Rajesh Kumar Jadav, Nilesh Tanwar, Sudeep Garg, Deepak Nirma University Institute of Technology Department of Computer Science and Engineering Ahmedabad India SR University School of Computer Science and AI Telangana Warangal India

ISBN: (纸本)9798350361537

technology is an inseparable part of today's world and is extensively used in every conceivable domain to provide a wide range of applications. technology has many critical infrastructures, which serve the function of delivering crucial services essential for the smooth functioning of smart cities. These infrastructures produce a large amount of data which is extremely sensitive and very potentially catastrophic if it falls into the wrong hands. Various techniques and concepts have been introduced and developed to deal with the detection of malicious data. But with the passing time, malware threats grow in both frequency and complexity, machine learning and artificial intelligence techniques can be combined to enhance malware detection. Many Machine Learning algorithms exist which may be confusing for users. Different algorithms vary in their results, accuracy, and prediction capabilities depending on the data they are trained and tested on. For data produced by critical infrastructures, the prediction must be as accurate as possible. Encouraged by the aforementioned facts, we propose an approach aiming to contrast and compare different machine learning algorithms for malware detection on portable executable files dataset. Different performance metrics are compared for all the considered algorithms, based on which, the best algorithm for critical infrastructure malware detection can be determined. © 2024 IEEE.

关键词： Adversarial machine learning

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