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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Suresh, Aakash Lakshmi, C. Gopirajan, P.V. Dhanasekaran, K. School of Computing Srm Institute of Science and Technology Chengalpattu Chennai TN Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Chengalpattu Chennai TN Kattankulathur603203 India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Chennai TN Kattankulathur603203 India

ISBN: (纸本)9798350387063

Humanity's exploration of the cosmos spans billions of years, prompting contemplation of our position in the universe. This study advocates for proactive communication with potential intelligent species, driven by uncertainty about their existence. The focus is on exploring Generative Adversarial Networks (GANs) to enhance exoplanet detection through data augmentation. Utilizing the extensive Kepler dataset, the study evaluates the accuracy of conventional machine learning techniques: Logistic Regression, Decision Trees, and Random Forests. (RF)-trained on original data. Subsequently, it assesses the effectiveness of models trained solely on GAN-generated synthetic data and compares their performance with that of models trained on original data. Lastly, it merges original and synthetic data to create a composite dataset, evaluating the ML model's accuracy. The results indicate that models trained solely on synthetic data achieve comparable accuracy to those trained on original data. Furthermore, the combined dataset yields improved accuracy-87%, 95%, and 96% in Logistic Regression, Decision Tree, and RF, respectively-highlighting the potential of GAN-based data augmentation in enhancing exoplanet detection. This study advances space exploration by integrating technology with astronomical research, facilitating more precise and reliable exoplanet detection methods. Leveraging GANs for data augmentation promises novel insights and discoveries about the universe. © 2024 IEEE.

关键词： Generative adversarial networks

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Automatic Stress Recognition System with Deep Learning using Multimodal Psychological data

Automatic Stress Recognition System with Deep Learning using...

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2022 International Conference on Electronic systems and Intelligent computing, ICESIC 2022

作者： Praveenkumar., S. Karthick, T. Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665483858

There are numerous applications for detecting stress states from physiological inputs. Human stress detection may be used to enhance the human experience as well as monitor and prevent stress-related disorders. Previous research has leveraged existing machine learning techniques for automated stress identification. However, there are also some limitations. Deep learning allows you to discover basic patterns in your body's reactions that are often difficult to notice. This study offers a convolutional network-based approach for detecting human stress utilizing multimodal data sets obtained by smart sensors for physiological and motion, which can aid humans in avoiding health issues caused by stress. Sensor information like triaxial accelerates, heartbeat, body temperature, respiration, electromyographic (EMG) data for physiological states stressed and non-stressed, are included in the WESAD data collection. The use of Long short - term memory method and the DNN method to detect stress in people in real-time is investigated in this study. Our research is based on their physical reactions. The study's findings provide an assessment of both models' ability to predict real-time users' mental expressions based on physiological parameter fluctuation. © 2022 IEEE.

关键词： Deep neural networks

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Assessing the feasibility of Blockchain Technology in Automotive Industry 9

Assessing the feasibility of Blockchain Technology in Automo...

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9th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering, UPCON 2022

作者： Balachander, S. Murugan, A. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (数字)9798350332506

ISBN: (纸本)9798350332506

Blockchain is a decentralised, distributed public ledger that has recently garnered enormous traction. The automotive industry, one of the most profitable markets worldwide, is undoubtedly responsible for the transformations in modern civilisation over the past century. This paper primarily focuses on the issue of the complexity of existing technologies in the field and the possibility of applying Blockchain technology as a replacement in the automotive sector. In addition, the researcher has considered compatible precedents for identifying lacunae in the existing technologies. As a result, the adoption of Blockchain technology provides access to a wide range of potential automotive applications that can develop modern business models moreover even transform the established automotive partaking economy. Further, after examining the advantages, limitations, opportunities, and risks, some suggestions are made to guide enterprises and researchers in future advancements in the cyber-resilient automotive sector. © 2022 IEEE.

关键词： Blockchain

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Analysis of Plant Leaf Diseases Recognition using Image Processing with Machine Learning Techniques

Analysis of Plant Leaf Diseases Recognition using Image Proc...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Vasudevan, N. Karthick, T. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India

ISBN: (纸本)9781665495295

Agriculture plays a foremost role in countries growth. The physical recognition of disease in the plant is more timeconsuming and necessity of expert labor is high. One of the most vital aspect in agriculture field is plant disease that reduces quality andquantity of agricultural crops. The machine learning approaches can easily recognize these diseases compared to physical method. So the machine learning techniques in addition toimage processing is used to identify diseased leaf. These methods will help in recognizing plant diseases thus increasing the yield of plants. This paper concentrates on the previous research work that used the characteristics of machine learning techniques in addition to image processing. It describes the various plant disease types, image processing methods and also various techniques of plant disease systems are analyzed with accuracy. Finally, the various challenges in the recognition of plant leaf diseases are identified and listed. © 2022 IEEE.

关键词： Learning algorithms

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Depression Prediction of Human Using Advanced Technology and Medical Imaging

Depression Prediction of Human Using Advanced Technology and...

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2022 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2022

作者： Annapoorani, S. Saravanan, P. SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Kattankulatur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Depression is a typical condition that impacts 3.8 % in total population, with 5.0 % of adults and 5.7 % of oldaged persons suffering from it. Depression can be dangerous to one's health, especially if it is persistent and has a moderate or severe intensity. Despite the fact that there are effective treatments for mental diseases, as well 75% of developing countries do not obtain them. In this paper, a smart gadget (a mobile phone or a robot) and medical imaging are used to identify depression without the need for clinical advice. By comparing the various elements of input and evaluating their performance, efficient technology can be used to forecast depression and ensure privacy. © 2022 IEEE.

关键词： Deep neural networks

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Blockchain-Enabled Quality Assurance and Traceability in the Indian Confectionery Market

Blockchain-Enabled Quality Assurance and Traceability in the...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Subashini, B. Shanthi, P. Venkatesh, K. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamilnadu kattankulathur India St. Joseph's Institute of Technology Department of Information Technology OMR Chennai119 India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu kattankulathur India

ISBN: (纸本)9798331543617

India, whose population growth is its greatest asset cannot afford to let its people eat confectionery that is either tainted or somehow polluted, since this would lead to extensive malnutrition. Traditional food supply chains are characterized by centralization and the problems associated with it such as having a single point of failure and producing inconsistent products while compromising the quality, along with data loss. With the need for daily reports in all regions of India regarding food fraud, contamination, and adulteration, a clearly advanced decentralized model is sought after for the creation of a supply chain. This paper introduces the supply chain network for the Indian confectionery market based on blockchain to better address quality claims, contamination, and transparency. This enables real-time traceability within the ecosystem, by using IoT strategies along with the Ethereum blockchain leading to better product authentication and resulting in higher consumer confidence. Blockchain creates a secure data tracking function, while smart contracts can be used to define the process - such as IoT monitoring of temperature. The united method enables verified data to be available to the consumer, energy efficiency, and trust across the supply chain. © 2024 IEEE.

关键词： Smart contract

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Customer Churn Prediction Using Machine Learning Approaches

Customer Churn Prediction Using Machine Learning Approaches

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Srinivasan, R. Rajeswari, D. Elangovan, G. SRM Institute of Science and Technology Department of Computing Technologies Kattankulathur603 203 India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur603 203 India

ISBN: (纸本)9798350334357

Customer Churn (CC) is a major issue and important concerns for large organizations and businesses alike. Telecom industries are attempting to improve methods to predict possible customer churn due to the immediate impact on revenue, particularly in the telecom sector. This paper discusses the various ML algorithms used to construct the churn model that helps telecom operators to predict customers who are likely to churn. The experimental results are compared to predict the best model among various techniques. As a result, the use of the Random Forest combined with SMOTE-ENN outperforms best result than other in terms of Fl-score. According to our analysis, the maximum prediction is 95 percent based on Fl-score. © 2023 IEEE.

关键词： Machine learning

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Cloud Intrusion Detection System Based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies

Cloud Intrusion Detection System Based on Honeynet, Honeywel...

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International Conference on data science and Network Engineering, ICDSNE 2023

作者： Yasotha, B. Arthy, M. Shoba, L.K. Loganathan, Muralidaran Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Computer Science and Information Systems Engineering St. Joseph University Dares Salaam Tanzania United Republic of

ISBN: (纸本)9789819967544

The security aspect of cloud computing is much more challenging for researchers. Preventing the attack requires knowledge about the type of attack, its origin, and how vulnerabilities and tools are used for the attack. The cloud security methodology protects customer data, information, and applications from attackers. Due to digitalization, the volume of data is increased. The protection of data is very challenging for cloud service providers. This work proposes a new cloud intrusion detection system security infrastructure based on Honeynet, Honeywell, Honeypot, and Honeytoken Strategies. The proposed strategy effectively identifies intrusion detection, attack behavior, and attack scenario. The testing of data was carried out in OpenStack environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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