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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters 3

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ramkumar, M. Siva Saranya, N. Gopan, Gokul Baig, Rahmath Ulla Josha Daniel, S. Babu, M. Sns College of Technology Department of Ece India Karpagam College of Engineering Department of Artificial Intelligence and Data Science India Department of Mechanical Engineering Chennai602105 India Cambridge Institute of Technology Department of Artificial Intelligence and Machine Learning K R Puram Bangalore560086 India Hindustan College of Engineering and Technology Department of Eee India Hindusthan College of Engineering and Technology Master of Engineering in Embedded Systems India

ISBN: (纸本)9798331509675

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment. © 2025 IEEE.

关键词： MATLAB

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit 2

Intrusion Detection in IoT Environment Using Stochastic Grad...

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2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024

作者： Krishnamurthy, Revatthy Alabdeli, Haider Rajani, N. Ramesh, Vankudoth Kumar, Bura Vijay Rajalakshmi Engineering College Department of Artificial Intelligence & Machine Learning Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Dept. of Ece Bengaluru India Cvr College of Engineering Department of CSE-DS Mangalpalle India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA). © 2024 IEEE.

关键词： Genetic algorithms

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ARIMA Modeling for Reliable Potable Water Identification and Quality Prediction 5

ARIMA Modeling for Reliable Potable Water Identification and...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Selvaraj, P. Shalma, H. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telengana Hyderabad India

ISBN: (纸本)9798331504403

This work aims to employ machine learning methods and visualization techniques for evaluating water quality by measuring its potability, a numerical indicator of water quality. The work utilizes water quality parameters to determine the stability condition using pH, Temperature, Dissolved Oxygen, Conductivity, Nitrate, Fecal Coliform, and Total Coliform to evaluate the overall water quality. ARIMA is utilized to identify and categorize water potability based on its features. Using Tableau's interactive dashboarding, we created an interactive dashboard to visually simplify water potability metrics in India. The study utilized both real datasets from various locations around the country using government data sourced directly from the Central Pollution Control Board (CPCB). Based on the results of the experiments, the ARIMA was found to be superior to other classifiers in accurately predicting water potability. © 2024 IEEE.

关键词： Water pollution control

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Organic Farming Automation to Revolutionize the Agricultural Industry Than Traditional Farming Practices Using IOT and Technological Development

Organic Farming Automation to Revolutionize the Agricultural...

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International Conference on Robotics, Control, Automation and artificial intelligence, RCAAI 2023

作者： Geetha Rani, E. Chalasani, Ramadevi Anusha, D. Dhanalakshmi, M. Vadlamudi, Sandyarani Department of Computer Science Engineering Alliance University Karnataka Bengaluru India Department of Computer Science Engineering Sir C R Reddy College of Engineering Andhra Pradesh Eluru India Department of CSE-Artificial Intelligence and Machine Learning SRKIT Andhra Pradesh Vijayawada India Department of Computer Science Engineering NHCE Karnataka Bengaluru India Department of Aritficial Intelligence and Machine Learning New Horizon College of Engineeing Karnataka Bengaluru India

ISBN: (纸本)9789819746491

Agriculture has remained the spine of human sustenance on earth. Since times unknown, humans have learned and developed methods to produce food and other resources for their livelihood. Agriculture has seen large developments, especially in the last century. Scientific methods have accelerated the process of agriculture to a large extent. These developments have improved productivity by leaps and bounds. Though these developments come with a lot of benefits, they also include several demerits that adversely affect the soil, humans, and livestock. To retain and multiply the benefits contributed by this scientific development and to overcome the demerits and challenges faced in today’s agricultural practices, this paper aims at discussing methodologies that can be used to build an agricultural model that is much simpler and more efficient. The model aims at automation using IoT that could lead to minimized labor efforts by almost refraining human intervention in agricultural practices. machine learning algorithms can be used for weather and soil-based crop detection for better productivity. The downside caused due to the use of synthetic chemicals can be overcome using organic methods. A web portal to facilitate direct producer-to-consumer retail can be deployed to curb the exploitation of farmers by the elimination of middlemen. Hence, the paper aims at developing an agricultural model based on IoT, machine learning, organic farming, and a web portal for direct producer-to-consumer retail. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Smart Finance: An overview of artificial intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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machine learning Induced Selective Identification of Dopamine Using Doped Carbon Quantum Dots 8

Machine Learning Induced Selective Identification of Dopamin...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Das, Ruma Paria, Abhirup Giri, P.K. Haldia721657 India Haldia Institute of Technology Department of Computer Science & Engineering Artificial Intelligence & Machine Learning Haldia721657 India Indian Institute of Technology Guwahati Department of Physics & Centre for Nanotechnology Guwahati781039 India

ISBN: (纸本)9798331520762

The present work highlights the selective identification of dopamine (DA) with machine learning (ML) classification. Dopamine is an essential organic neurotransmitter that acts a dynamic role. With the inhouse experimental da-ta, the ML models are trained with more than thirty classification algorithms for the efficient selection of DA among various metal ions and biomolecules that exist in human body fluids. The experimental data was collected by measuring the turn-off photoluminescence intensity of nitrogen-doped car-bon quantum dots in the presence of various metal ions and biomolecules. A promising agreement between predicted result and experimental consequences endorses the effectiveness of the proposed ML prediction. Additionally, confusion matrices and receiver operating characteristic (ROC) are introduced to validate the outcomes by visual analysis. © 2025 IEEE.

关键词： Glycoproteins

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Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine based Ransomware Detection and Classification 2

Sine Cosine Reptile Search Algorithm with Grid Search Suppor...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Adnan, Myasar Mundher Devi, S. Vaag Deepthi, V.S. Raghunathareddy, M.V. Yamsani, Nagendar The Islamic University Najaf Iraq City Engineering College Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India Cyber Security Dayananda Sagar College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and artificial Neural Network (ANN). © 2024 IEEE.

关键词： +Support Vector machine Grid Search Min-max Normalization Ransomware Reptile Search Algorithm

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AI -Driven Detection of Gastrointestinal Disease based on Advanced Deep learning Techniques 4

AI -Driven Detection of Gastrointestinal Disease based on Ad...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Izankar, Sakshi V. Puri, Chetan Burle, Rushikesh Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331523923

Esophageal, gastric, and colorectal cancers (CRC) are some of the gastrointestinal (GI) cancers threatening global health with CRC being particularly concerning due to its high fatality rate. While early diagnosis is crucial in improving treatment efficiency, barriers such as image noise and visual subjective assessments during diagnostics such as colonoscopies often render it inconceivable. For better diagnostic accuracy in detecting gastrointestinal disease via endoscopic images, the research explores whether highly advanced artificial intelligence (AI) models such as Vision Transformers (ViTs) and pre-trained DenseNet201 can be effective. We compare these models to popular deep learning techniques such as Support Vector machines (SVM), ResNet50, VGG19, and GoogleNet. At a training accuracy of 99.9% and a validation accuracy of 98.8%, experiments on the KVASIR dataset indicate that Vision Transformer is superior to other models since it can learn long-range dependencies. The robustness of deep learning models for identifying GI disease is exemplified by the superior performance of DenseNet201, ResNet50, and InceptionV3, all of which possess high accuracy, precision, and recall values. The paper illustrates the potential of Vision Transformers, when combined with transfer learning and pre-trained CNN models, to revolutionize endoscopic diagnoses and provide a valuable resource to help physicians manage gastrointestinal conditions more effectively. © 2025 IEEE.

关键词： Endoscopy

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Touchless Mouse Control Via Hand Gestures Using Resnet and Mediapipe

Touchless Mouse Control Via Hand Gestures Using Resnet and M...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jagadeeswara Rao Palisetti Digumarthi Srilikhitha Karna Bhargava Basa Bhavana Devi Mettu Bharath Kumar Computer Science and Engineering(Artificial Intelligence and Machine Learning) Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Touchless control of systems using hand gestures is a trending technology in the field of Human-Computer Interaction (HCI). One of the most common ways to interact with systems is through the mouse. In this paper, a touchless mouse is focus on, which can perform the same operations as a physical mouse using finger and hand gestures. All of these operations are carry out by the system without requiring any external devices. In the physical mouse, we hold and operate it using the index and middle fingers. Similarly, we use the same hand pose to control the touchless mouse system. Nowadays, every system has an inbuilt webcam. This system uses a low-resolution webcam as input to capture hand gestures with 21 landmarks using OpenCV and MediaPipe. Based on the movements of the landmarks in the hand, the cursor can navigate, and various actions such as right-click, left-click, double-click, zoom in and zoom out, drag and drop files, multiselect files, increase, and decrease volume, adjusting the brightness, are easily performed, ResNet18 is used to stop the cursor movement. Using different finger and hand gestures. The system has the potential to replace traditional HCI tools in the future and help optimize hu-man-computer communication.

关键词： Hands Human computer interaction Performance evaluation Webcams Tracking Navigation Hospitals Brightness Gesture recognition Indexes

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End to End Model to Reduce the Inference, Jamming, and to Increase the Trust from the Compromised Secondary Nodes in Cognitive Radio Networks 2

End to End Model to Reduce the Inference, Jamming, and to In...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Dhananjaya, Shashank Narender, M. Sunitha, R. The National Institute of Engineering Department of Information Science and Engineering Mysore India The National Institute of Engineering Department of Computer Science and Engineering Mysore India B. N. M. Institute of Technology Dept. of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350306415

Wireless technology has advanced dramatically in recent years. Multimedia applications have exploded in popularity over wireless networks. The demand for radio spectrum has expanded as the demand for large capacity for multimedia applications has grown. The traditional fixed spectrum assignment approach has been challenged by the shortage of radio spectrum. As a result, Cognitive Radio (CR) has arisen as a new unique option for addressing spectrum underutilization by allowing users to access unused spectrum bands on the fly. In cognitive radio networks, radio spectrum scarcity, spectrum access, signal interference, security at various layers, trust, jamming, and spectrum management have all become key issues. Proposing a unique approach to address the interference problem produced by secondary nodes that have been hacked. To fulfil the need, we develop a mathematical modelling of the system that incorporates intelligence in the SU to cope with intelligent jammer. Intelligent jammer is cognitive in nature, having the ability to learn, to reason and to adjust its strategies against SU for maximum damage to the CRN. Many researchers have been working on reducing inference and the effect of jamming in the network. But still it is not resulted with the proper solution for the jamming problem in cognitive radio wireless sensor network. Hence proposing the methodology aiming to minimize the effect of Jamming in Cognitive Radio Wireless Sensor-Networks. At the end, the proposed research is aiming to solve the security problem to increase the trust in the cognitive radio network. The secondary users must be able to detect and forecast spectrum holes in the CR environment. The overall result shows the improved network performance by reducing the inference, jamming, spectrum sensing and trust issues in cognitive radio network. © 2024 IEEE.

关键词： Cognitive Radio Network Inference Jamming Q-learning Software Defined Radio Trust

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