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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Kaur, Shivneet Kaur, Parneet Gosain, Monika Kaur, Roopkamal Department of Computer Science & Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Headaches are the most prevalent medical condition in the globe. The migraine is one of the various types of headaches. Along with their excruciating headaches, migraineurs often have nausea, vomiting fits, and light and sound sensitivities. Individuals have unique symptoms, some of which may be more intense than others. A migraine episode is about to begin when an aura, a symptom, appears. Based on the nature of symptoms, migraines can be divided into two basic categories: aura-producing and aura-less. Every individual has different triggers for migraine. This study provides an overview of the state-of-the-art deep learning and machine learning techniques for migraine identification. The primary subjects of this study include migraine kinds, phases, causes, and symptoms. © 2025 the Author(s).

关键词： Diseases

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***: Learning-based Governance for Decentralized Finance (DeFi)

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IEEE Transactions on Services Computing 2025年第3期18卷 1278-1292页

作者： Xu, Jiahua Feng, Yebo Perez, Daniel Livshits, Benjamin University College London United Kingdom Exponential Science Foundation United Kingdom Nanyang Technological University School of Computer Science and Engineering Singapore Imperial College London Computer Science Department United Kingdom Imperial College London United Kingdom

Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "***", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where *** has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, *** outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric–protocol profitability. Overall, the comprehensive evaluations confirm that *** is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols. © 2025 IEEE. All rights reserved.

关键词： Decentralized finance

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Credit card fraud detection using state-of-the-Art AI methods 4th

Credit card fraud detection using state-of-the-Art AI method...

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Sharma, Piyush Gagandeep Department of Computer Science and Engineering Chandigarh Engineering College-CGC Landran India

ISBN: (纸本)9781032911571

Credit card fraud has become a major problem in the financial industry, causing huge financial losses for financial institutions and cardholders. Because internet banking and e-commerce are being used more often there have been an exponential rise in the online transaction and similarly in the online fraud happening nowadays. As fraud procedures become more complex, it is clear that legitimate fraud is not sufficient to detect fraud. As a result, machine learning (ML) algorithms are increasingly used for fraud detection due to their ability to adapt to and learn from patterns in data. Various machine learning methods to detect and prevent credit card fraud. This study examines different learning techniques such as logistic regression, decision trees, random forests, support vector machines (SVM), neural networks, and ensemble methods, highlighting their advantages and disadvantages in statistical analysis. Machine learning offers effective solutions for credit card fraud detection, and a comprehensive approach that considers algorithmic robustness, interpretability, and ethics is essential. Future research directions aim to improve the accuracy and efficiency of machine learning-based fraud detection systems while ensuring fairness and transparency. © 2025 the Author(s).

关键词： Logistic regression

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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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An Explainable AI for Prostate Cancer Analysis 5th

An Explainable AI for Prostate Cancer Analysis

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Kumar, M. Rakesh Athish, B.S. Devi, A. Chandini Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819771776

Prostate cancer comes under SDG 3—Good Health and Well-Being is a global health concern affecting millions of men, demanding precise analysis for early detection and effective treatment. This project pioneers, innovative techniques for prostate cancer analysis, utilizing AI-driven segmentation and advanced analytical methods to address this pressing concern. Prostate cancer poses a significant worldwide health challenge, potentially metastasizing to neighboring organs and bones, leading to pain and fractures. The primary aim of this project is to enhance the precision and efficiency of prostate cancer analysis for early detection and timely intervention. Approximately one in eight men receive a prostate cancer diagnosis during their lifetime, with a higher prevalence among older individuals and non-Hispanic black men, particularly those aged 65 and older. Awareness levels vary, with 47.3% of survey respondents aware of the condition, primarily through media outlets (21.2%) and healthcare professionals (9%). This project addresses varying prostate cancer rates in distinct regions (peripheral zone: 70–80%, transitional zone: 10–20%, central zone: 5% or less). It employs AI-driven segmentation, image processing, and machine learning for accurate tumor detection, classification, and risk assessment. An innovative aspect is developing an explainable AI system for transparent and expert-empowered analysis. The project uses AI to diagnose prostate cancer accurately and early. It combines AI-driven segmentation, advanced analytical methodologies, refined algorithms, and a user-friendly interface. The aim is to enhance patient care through accurate and interpretable prostate cancer analyses in underserved areas. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Integrated Road Safety System for Real-Time Waterlogging Detection and Traffic Management Using Google Maps API

Integrated Road Safety System for Real-Time Waterlogging Det...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vignesh, R. Pabitha, C. Shamritha, V. Prathija, A. Srm Valliammai Engineering College Department Of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Potholes, waterlogging, and possible electrical hazards have a significant impact on urban traffic safety, especially during the monsoon season. Road damage, traffic jams, car accidents, and even potentially fatal electrical leaks from damaged power lines in flooded areas are all consequences of these situations. A thorough machine learning-based method for detecting potholes in real time, determining the extent of waterlogging, and identifying electrical leaks in water is presented in this study. The device measures the risk of water accumulation, estimates pothole depth, and records road conditions using computer vision algorithms. It also finds electrical leaks in water and sends out timely alerts to reduce the risk of electrocution. Using the Google Maps API, the system automatically reroutes traffic to safer routes, reducing the amount of time that cars and pedestrians are exposed to dangerous situations. Our system provides a strong, scalable foundation for enhancing road safety and urban mobility during inclement weather by combining pothole recognition, waterlogging severity assessment, and electrical danger detection. © 2025 IEEE.

关键词： Risk assessment

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Real Time Traffic Light Timing Optimisation Using Reinforcement Learning

Real Time Traffic Light Timing Optimisation Using Reinforcem...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Clement Andrew, A. Shri Vaisalini, L.C.K. Kavitha Devi, M.K. Thigarajar College of Engineering Department of Computer Science and Engineering Madurai625015 India

ISBN: (纸本)9798331529833

A major problem especially in urban areas is traffic congestion which causes increased travel times, pollution, and compromises safety. This paper presents the design of the Smart Traffic light timing Management System, optimized in real-time traffic light timings using reinforcement learning techniques. This system scans data from different sources, including vehicle count in a network and waiting time of the vehicle at a traffic stop, which are utilized to continuously to alter traffic signals based on current situations. Utilizing reinforcement learning algorithms, the system continuously learns and adapts to changing traffic scenarios, striving for minimal congestion. The simulations run in SUMO (Simulation of Urban Mobility) indicate a noticeable reduction in wait times and improved flow across roads. The results form the basis for the inclusion of advanced data analytics in traffic management: it represents an effective method of mitigating urban congestion problems. The study concludes that by leveraging real-time data and machine learning, the traditional traffic systems can be transformed into an intelligent responsive network, capable of enhancing the efficiency and safety of urban transportation. © 2025 IEEE.

关键词： Traffic congestion

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Lighting the Smart Way: A Technological Odyssey in Urban Street Illumination 10

Lighting the Smart Way: A Technological Odyssey in Urban Str...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vinu, M.S. Priya, P Kanaga Thilakaveni, P. Hermina, J. M. Ashforn Prashanthini, K. Misba, M. Nehru Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Psg Polytechnic College Department of Computer Engineering Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Sri Ramakrishna Engineering College Department of Robotics and Automation Coimbatore India Bethlahem Institute of Engineering Department of Computer Science and Engineering Nagercoil Karungal India

ISBN: (纸本)9798350384369

The development of intelligent street light systems has ushered in a new era of efficiency and sustainability in urban infrastructure. The proposed work studies the integration of modern sensors and Internet of Things (IoT) technologies to increase the functionality of street lights. The major components include Light Dependent Resistors (LDR) for automatic light management, an MQ135 sensor for air quality monitoring, and a fog detection system employing a laser module and LDR. These sensors are integrated with the ESP8266, providing cloud integration for real-time data visualization and monitoring. Additionally, solar panels are employed to enhance energy efficiency. The proposed methodology digs into the precise implementation, data analysis, and the potential of this system to alter urban lighting infrastructure © 2024 IEEE.

关键词： Air quality

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Intelligent surveillance support system

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Discover Internet of Things 2023年第1期3卷 1-14页

作者： Saketh, Meduri Nandal, Neha Tanwar, Rohit Reddy, B. Pradyun Department of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Geethanjali College of Engineering and Technology Hyderabad India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Engineering Sreenidhi Institute of Engineering and Technology Hyderabad India

The Intelligent Surveillance Support System(ISSS) is an innovative software solution that enables real-time monitoring and analysis of security footage to detect and identify potential threats. This system incorporates advanced features such as face recognition, alarm on theft detection, visitors in/out detection and motion detection, to provide a comprehensive and reliable security solution. The implementation of this software aims to improve the efficiency of surveillance systems, thereby enhancing the safety and security of public and private spaces. The focus of this study is on performing the aforementioned tasks in real time while utilizing enhanced algorithms from the OpenCV Library, such as LBPH and Haar Cascading, which enhance the use of machine perception and help us produce outcomes with an accuracy of about 95% after multiple runs. With the rapid advancements in technology and the increasing need for surveillance in today’s world, the Intelligent Surveillance Support System holds immense potential in the field of security and surveillance. © The Author(s) 2023.

关键词： Face recognition

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Hybrid Convolutional Neural Network and Long Short-Term Memory Approach for Facial Expression Recognition

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Intelligent Automation & Soft Computing 2023年第1期35卷 689-704页

作者： M.N.Kavitha A.RajivKannan Department of Computer Science&Engineering Kongu Engineering CollegePerunduraiErode638060Tamil NaduIndia Department of Computer Science&Engineering K.S.R.College of EngineeringTiruchengode637215Tamil NaduIndia

Facial Expression Recognition(FER)has been an importantfield of research for several *** of emotional characteristics is crucial to FERs,but is complex to process as they have significant intra-class *** characteristics have not been completely explored in static *** studies used Convolution Neural Networks(CNNs)based on transfer learning and hyperparameter optimizations for static facial emotional *** Swarm Optimizations(PSOs)have also been used for tuning ***,these methods achieve about 92 percent in terms of *** existing algorithms have issues with FER accuracy and ***,the overall FER performance is degraded *** address this issue,this work proposes a combination of CNNs and Long Short-Term Memories(LSTMs)called the HCNN-LSTMs(Hybrid CNNs and LSTMs)approach for *** work is evaluated on the benchmark dataset,Facial Expression Recog Image Ver(FERC).Viola-Jones(VJ)algorithms recognize faces from preprocessed images followed by HCNN-LSTMs feature extractions and FER ***,the success rate of Deep Learning Techniques(DLTs)has increased with hyperparameter tunings like epochs,batch sizes,initial learning rates,regularization parameters,shuffling types,and *** proposed work uses Improved Weight based Whale Optimization Algorithms(IWWOAs)to select near-optimal settings for these parameters using bestfitness *** experi-mentalfindings demonstrated that the proposed HCNN-LSTMs system outper-forms the existing methods.

关键词： Facial expression recognition Gaussianfilter hyperparameter optimization improved weight-based whale optimization algorithm deep learning(DL)

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