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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Nandhini Devi, S. Thenappan, S. Shankar, R. Dayana, R. Priya, R. Vasanthi, R. Sri Sairam Engineering College Department of MTECH- Cse Chennai Tambaram India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India Department of Electronics and Communication Engineering Meerpet Hyderabad India Jeppiaar Institute of Technology Department of Computer Science and Engineering Chennai Sriperumbudur India Mohamed Sathak A.J.College of Engineering Department of Information Technology Tamilnadu Chennai India St.Joseph's Institute of Technology Department of Cse Chennai India

ISBN: (纸本)9798350300857

Applying the support vector machine-based Genomic Selection (GS) to improve the agricultural yield. The proposed work employs GS to address the pressing need for enhanced techniques for agricultural productivity. The proposed work examines GS's history, methodology, and results to demonstrate its superiority over conventional breeding techniques. The author indicates that by using in-depth case studies and comparative research, GS may enhance agricultural productivity and disease resistance. The findings extensively impact agriculture, including increased product safety and adopting sustainable practices. Ethical concerns regarding the use of GS in agriculture are also discussed, along with suggestions for further research in this area. The proposed work contributes to the burgeoning field of agricultural development by disclosing a novel technique for constructing stronger, more productive farms. © 2023 IEEE.

关键词： Genome

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User Persona Mapping Technique for Human Interaction with Augmented and Virtual Reality Environments

User Persona Mapping Technique for Human Interaction with Au...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Nandhini Devi, S. Shiny, A. Sureshbabu, J. Mahalakshmi, S. Sundara Bala Murugan, P. Priya, R. Sri Sairam Engineering College Department of MTECH-C.S.E. Chennai Tambaram India Nanjappa Polytechnic College Department of E.C.E. Tamilnadu Coimbatore India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Management Studies Tamil Nadu Chennai India Mohamed Sathak A.J.College of Engineering Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798350300857

An in-depth analysis of user-centered design's function in the context of augmented and virtual reality demonstrates how 'User Persona Mapping' can help satisfy the target audience's requirements. As shown by this study, it is crucial to consider user needs and preferences throughout the design phase of augmented and virtual reality (AR/VR) interfaces. Essential to this analysis is the 'User Persona Mapping' technique, which entails the construction of detailed user personas based on extensive user research. By synthesizing user attributes, behaviors, and preferences, designers can better meet the expectations of their end users if they first construct a concrete representation of that demographic. Through real-world examples, this article demonstrates how this strategy can be used to improve the usability of augmented and virtual reality interfaces. This study contends that in the future of interactive technology, empathic design will be essential for ensuring that extended and virtual reality experiences are not only innovative but also authentically user-centric and engaging. © 2023 IEEE.

关键词： Augmented reality

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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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Internet of Things Enabled Gas Leakage Detection Over Industrial Areas using Powerful MQ Series Sensor and Controller

Internet of Things Enabled Gas Leakage Detection Over Indust...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Rahul Jalinder Jadhav P. Radhakrishnan Dattatraya Arun Jadhav B Ashreetha J. Divya Saptarshi Mukherjee Yashwantrao Mohite Institute of Management Bharati Vidyapeeth(Deemed to be University) Karad Electronics & Communication Engineering Tagore Engineering College Electronic and Telecommunication Department Keystone School of Engineering Pune Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Tirupati Andhra Pradesh India Dept of Computer Science and Design R.M.K.Enginnering College Department of Mtech Bioinformatics GIAR Goldsmith Institute of Advanced Research Kolkata West Bengal

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This work presents a comprehensive overview of an Internet of Things (IoT)-enabled gas leakage detection system deployed in industrial areas using powerful MQ series gas sensors and controllers. The primary objective is to enhance safety measures by providing real-time monitoring and rapid response capabilities to potential gas leaks. The system incorporates MQ2, MQ7, and MQ135 gas sensors strategically placed in different zones within the industrial facility, capable of detecting a wide range of gases. These sensors continuously collect data, which is then transmitted to a centralized controller for analysis and decision-making. The system's architecture allows for remote accessibility, enabling authorized personnel to monitor sensor readings and take timely actions from anywhere with an internet connection. The MQ series gas sensors provide accurate and dynamic measurements of gas concentrations, including flammable hydrocarbons, toxic substances, and other hazardous gases. The system's integration with existing safety systems ensures a holistic approach to hazard prevention, allowing for coordinated responses with alarms, emergency shutdown systems, and ventilation controls. Key features of the system include scalability, cost-efficiency, and seamless integration with industrial processes. The scalability allows for the expansion of sensor networks to adapt to evolving industrial environments. The cost-efficiency is realized through the prevention of potential damage and operational downtime. Furthermore, the system integrates seamlessly with existing safety and automation systems, creating a unified safety infrastructure.

关键词： Gases Scalability System integration Ventilation Sensor systems Real-time systems Sensors

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Fake News Detection from Online media using Machine learning Classifiers

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Journal of Physics: Conference Series 2022年第1期2161卷

作者： Shalini Pandey Sankeerthi Prabhakaran N V Subba Reddy Dinesh Acharya MTech CSIS Department of Computer Science and Engineering MIT MAHE Manipal Department of Computer Science and Engineering MIT MAHE Manipal

With the advancement in technology, the consumption of news has shifted from Print media to social media. The convenience and accessibility are major factors that have contributed to this shift in consumption of the news. However, this change has bought upon a new challenge in the form of 'Fake news' being spread with not much supervision available on the net. In this paper, this challenge has been addressed through a Machine learning concept. The algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Naïve Bayes and Logistic regression Classifiers to identify the fake news from real ones in a given dataset and also have increased the efficiency of these algorithms by pre-processing the data to handle the imbalanced data more appropriately. Additionally, comparison of the working of these classifiers is presented along with the results. The model proposed has achieved an accuracy of 89.98% for KNN, 90.46% for Logistic Regression, 86.89% for Naïve Bayes, 73.33% for Decision Tree and 89.33% for SVM in our experiment.

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Support Vector Machine-Based Bioinformatics in Agriculture Genomic Insights for Crop Improvement

Support Vector Machine-Based Bioinformatics in Agriculture G...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Nandhini Devi S Thenappan S Shankar R R. Dayana R. Priya R. Vasanthi Department of MTECH- CSE Sri Sairam Engineering College Tambaram Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Teegala Krishna Reddy Engineering College (Autonomous) Meerpet Hyderabad India Department of Computer Science and Engineering Jeppiaar Institute of Technology Sriperumbudur Chennai India Department of Information Technology Mohamed Sathak A.J.College of Engineering Chennai Tamilnadu India Department of CSE St.Joseph's Institute of Technology Chennai India

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User Persona Mapping Technique for Human Interaction with Augmented and Virtual Reality Environments

User Persona Mapping Technique for Human Interaction with Au...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Nandhini Devi S Shiny. A J Sureshbabu Mahalakshmi S Sundara Bala Murugan P R. Priya Department of MTECH-C.S.E. Sri Sairam Engineering College Tambaram Chennai India Department of E.C.E. Nanjappa Polytechnic College Coimbatore Tamilnadu India Department of Computer Science and Design R.M.K. Engineering College Chennai Tamilnadu India Department of Management Studies St. Joseph's Institute of Technology Chennai Tamil Nadu India Department of Information Technology Mohamed Sathak A.J.College of Engineering Chennai Tamilnadu India

An in-depth analysis of user-centered design's function in the context of augmented and virtual reality demonstrates how “User Persona Mapping” can help satisfy the target audience's requirements. As shown by this study, it is crucial to consider user needs and preferences throughout the design phase of augmented and virtual reality (AR/VR) interfaces. Essential to this analysis is the “User Persona Mapping” technique, which entails the construction of detailed user personas based on extensive user research. By synthesizing user attributes, behaviors, and preferences, designers can better meet the expectations of their end users if they first construct a concrete representation of that demographic. Through real-world examples, this article demonstrates how this strategy can be used to improve the usability of augmented and virtual reality interfaces. This study contends that in the future of interactive technology, empathic design will be essential for ensuring that extended and virtual reality experiences are not only innovative but also authentically user-centric and engaging.

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Machine Learning-Driven Anomaly Detection in Blockchain Transactions for High-Security Digital Banking 1st

Machine Learning-Driven Anomaly Detection in Blockchain Tran...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Sen, Arijeet Chandra Kumar, Pramod Dave, Mansi Jitendra Parmar, Haresh Ramanlal Kalra, Akash Goyal, Mukul Government of India MTech Cyber Security BITS Pilani Delhi India Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana India C-9 Ishwar Nagar Society Maninagar Near Sardar Patel School Ahmedabad India D-304 Sanidhya Royal Tragad Tragad Road Nr. Satya-2 Ahmedabad Chandkheda India International Economics and Sustainability Brandeis University WalthamMA United States Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9783031860683

Blockchain technology has improved the digital transformation of the banking sector, boosting transaction security. Increasingly elaborate procedures are required to defend against increasingly sophisticated cyberattacks. The purpose of this research is to provide a machine learning-based anomaly detection system for blockchain-powered financial transactions. This system combines the analytical capability of machine learning with the reliability of blockchain to identify and prevent fraud. This assures the dependability and security of digital financial services. Using unsupervised machine learning, the proposed approach explores blockchain transaction patterns. These algorithms automate transaction data analysis and help identify fraud. The system's blockchain design allows it to evaluate hazards in real time and react quickly. This strategy ensures that defences stay effective as they develop by adapting security systems to new threats. Continue with care if this strategy works in a monitored online banking environment. Anomaly detection systems detect unusual financial activities, lowering the frequency of unreported fraudulent operations. The system's false-positive rate was low to nonexistent, suggesting that it seldom interfered with legitimate financial activities. To conclude, blockchain systems that use machine learning algorithms to detect anomalies impede digital currency scammers. The results demonstrate a considerable boost in financial transaction security, bolstering blockchain technology's use in banking and finance. This novel strategy is the first step towards changing banking security standards, and it has the potential to influence industry improvements. Furthermore, this approach may have an influence on future industrial breakthroughs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Secure routing protocols for wireless sensor networks 4

Secure routing protocols for wireless sensor networks

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4th IEEE International Conference on Computing Communication and Automation, ICCCA 2018

作者： Singh, Reetu Kathuria, Kajol Sagar, Anil Kumar MTech Computer Science and Engineering School of Computing Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781538669471

Recently, with the improvement of Wireless Sensor Networks (WSNs), numerous new routing protocols have been developed for WSNs. Routing protocols in WSNs, in any case, may vary depend on the application and network systems. Besides, WSNs are presented to various types of security threats. In this manner, it is headachy for us to pick secure routing protocol for application in WSNs. In regard that the decision of secure routing protocol for WSNs is personally related with the application prerequisites and routing protocol qualities and the assaults on routing protocol, there is no 'panacea' secure routing protocol. In this paper we give a review of secure routing protocols that can be utilized as a part of remote sensor systems since it is imperative to give a characterization of the accessible conventions. Along these lines, a few conventions were audited in this paper. The directing conventions can be grouped into two principle classifications to be specific topology based and convention task based. The principle objective of the work proposed in this paper is to give analysts an unmistakable thought regarding the accessible security based directing conventions and their properties. © 2018 IEEE.

关键词： Wireless sensor networks

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A Survey on Big Data Analytics

A Survey on Big Data Analytics

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2018 IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2018

作者： Ahmad, Saba Singh, Paramdeep Sagar, Anil Kumar MTech Computer Science and Engineering Galgotias University Greater Noida India SCSE Galgotias University Greater Noida India

ISBN: (纸本)9781538641194

A vast repository of terabytes of data is generated per day from Cloud Computing, (IoT) and modern information systems. Analysis of these enormous data needs a lot of efforts at different levels to extract knowledge for Decision making. The transaction from E -commerce involves online investing, buying and selling. Hence, developed data are complex in structure and high in dimension. The old methods were not sufficient to store and analyze the enormous volumes of data. Numerous analysts are doing their examination for good representation and investigation report. The point of this paper is to give the subtle elements of big data examination, difficulties, issues and different advancements. © 2018 IEEE.

关键词： Data Analytics

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