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

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

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Brain Tumor Detection using YOLO v7 and YOLO v8

Brain Tumor Detection using YOLO v7 and YOLO v8

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Vaghela, Rohan Sarda, Jigar Thakkar, Amit Vaishnani, Darsh Khokhariya, Avani Mehta, Pooja Charotar University of Science & Technology Department of Computer Science & Engineering Anand India Charotar University of Science & Technology Department of Artificial Intelligence & Machine Learning Anand India

ISBN: (纸本)9798331533311

The architecture of the brain is incredibly complex, with multiple areas managing various parts of the nervous system. An adult's health is greatly at risk from brain tumors because of their ability to severely impair organ function or possibly cause death due to their rapid growth of abnormal brain cells. Brain tumors can develop in any area of the skull or brain, including the protective layer around the brain and many other areas. Since these tumors can have a wide range of shapes, sizes, and locations, magnetic resonance imaging (MRI) is an essential tool for locating malignant tumors. MRIs are examined manually in order to diagnose brain tumors. However, manually identifying brain tumors is a challenging and time-consuming task that may result in errors. As a result, computer-aided brain tumor detection has been considered a crucial area of study in biomedical engineering. However, because tumor sizes and shapes vary enormously throughout subjects, developing a reliable brain tumor detector remains difficult. Thus, for the purpose of accurately identifying brain tumors in MRI scans, two popular deep learning detection algorithms - YOLO v7 and YOLO v8 - were tested and compared in this study. From the results, YOLO v8 model has achieved highest validation accuracy of 96.50% with average precision and recall value of 95.9% and 93.3% respectively, while YOLO v7 has validation accuracy of 87.6% with average precision and recall value of 81.9% and 85.4% respectively. © 2024 IEEE.

关键词： Deep learning

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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ClinicCare Manager: An Efficient Electronic Health Record (EHR) and Healthcare Management System Using Spring Boot and React 5

ClinicCare Manager: An Efficient Electronic Health Record (E...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bindu, S. Ashok, K. Krishnamurthy, Keerthana Chethan, C. Jatinkrishna, Gajula Srinivas Gagan, V. New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518097

Healthcare institutions often face challenges in managing patient data, optimizing clinic workflows, and ensuring timely reporting. Smaller clinics, in particular, may lack the resources to streamline services for both patients and healthcare providers. This work introduces a platform that automates patient data analysis and report generation to address these needs. Built using React, Spring Boot, and PostgreSQL, the application provides efficient, end-to-end service with robust data handling and fast processing. Performance metrics show efficient resource utilization, with CPU usage rarely exceeding 3 % and heap memory remaining well within the 2048 MB limit. Compared to other health care applications, this platform offers greater scalability and responsiveness. Additionally, the framework supports secure authentication and role-based access, making it a reliable and scalable solution that meets the expanding demands of health care environments. © 2024 IEEE.

关键词： Electronic health record

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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Gesture-Controlled Virtual Mouse and Finger Air Writing 14

Gesture-Controlled Virtual Mouse and Finger Air Writing

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Apoorva Shankar, Anushka Bondia, Anushree Rani, Ritu Jaiswal, Garima Sharma, Arun Computer Science Engineering-Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Artificial Intelligence and Machine Learning Indira Gandhi Delhi Technical University for Women Delhi India Centre of Excellence-Artificial Intelligence Indira Gandhi Delhi Technical University for Women Delhi India Artificial Intelligence and Machine Learning Bennett University Noida India

ISBN: (纸本)9798350344837

The convergence of innovative strategies to human-computer interaction is evidently supported by two underlying research. One experiment involves controlling objects using hand motions, while the other involves identifying the symbols we write in the air. In the first, a ground-breaking method for manipulating the mouse cursor is introduced: gesture recognition. By fusing motion modelling, pattern recognition, and machine learning, this technique perfectly supports a range of mouse movements. The system, which was created in Python, effectively separates finger motions in order to accurately mimic mouse events. It is improved with coloured finger caps to guarantee the best contrast. The second research explores air-writing recognition, a novel technique that involves drawing letters and numbers by hand, in conjunction with the first. This is when individuals draw letters and numbers in the air with their hands. An approved CNN model is the result of extensive testing and sets new standards for recognising characters created in the air. It is quite effective at identifying the letters or numbers that someone is forming in the air. These research, which cover gesture-based control and progressive air-writing recognition and push the frontiers of technical interface development, collectively represent the evolution of human-computer interaction paradigms. Making computers and people interact in novel and fascinating ways is the main goal! © 2024 IEEE.

关键词： Palmprint recognition

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Aspect Sentiment Analysis with Triplet Extraction for E-Commerce 7

Aspect Sentiment Analysis with Triplet Extraction for E-Comm...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Meenakshi Sundaram, A. Joyce Beryl Princess, P. Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

In the age of e-commerce, it is critical for companies to comprehend the thoughts and feelings of their customers in order to enhance their goods and services. A powerful method for deriving insights from consumer feedback is the combination of aspect sentiment analysis and triplet extraction. The objective of this method is to recognize the characteristics or facets of a good or service under discussion, gather feedback on each component, and create connections between the components, feedback, and viewpoints. Businesses can gain important insights into customer feelings towards certain features like design, usability, price, and customer service by examining customer reviews, comments, and ratings. Opinion sources (experts, customers, etc.), sentiment expressions (positive, negative, or neutral), and opinion targets (aspects) are all identified from the text as part of the triplet extraction process. This gives companies a comprehensive grasp of the thoughts and preferences of their customers, which helps them design products, set prices, and provide customer service with more focus and eventually increase customer happiness. © 2024 IEEE.

关键词： Sentiment analysis

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs machine learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Comparative Analysis of Air Quality Forecasting Using machine and Deep learning Based Approaches

Comparative Analysis of Air Quality Forecasting Using Machin...

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2025 International Conference on artificial intelligence and Data engineering, AIDE 2025

作者： Sowmya, H.K. Chandresh, Koyal Jesy Janet Kumari, J. D'souza, Sonia Maria Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Visvesvaraya Technological University The Oxford College of Engineering Department of Computer Science and Engineering Belagavi Bangalore India

ISBN: (纸本)9798331527518

Many emerging nations are experiencing severe air pollution on account of the growing urbanization and industry. Because air pollution has an impact on global human health and sustainable development, governments and it is becoming source of concern in society. Anticipating air pollution thus became essential. Prediction of air quality has gained popularity by virtue of the volume and a range of data that air quality surveillance systems have collected. This paper uses a Python framework to analyze the air quality, implementing historical air quality data analysis for Bangalore city and predicting its quality exercising a number of machine and deep learning (D-learning) models. In the proposed work, 5 distinct machine learning(M-learning) models including Random Forest regression (RFR), Support Vector Regression (SVR), Linear regression (LR), Decision Tree (DT), and Long Short- Term Model of D-learning Technique (D-LSTM) have been accustomed to compute the Air Quality Index (AQI) of Bangalore city. This strategy has been put into practice in Google Collab with GPU run time. As stated by the measures including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), D-learning- based LSTM model has demonstrated better reliability than other M-learning models. Performance metrics values for LSTM is superior compared to the rest of M-learning models © 2025 IEEE.

关键词： Air quality

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Improving Software Defects Detection: An In-Depth Analysis of machine learning Methods and Static Analysis Tools for Greater Accuracy 2

Improving Software Defects Detection: An In-Depth Analysis o...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Bobade, Veer Puri, Chetan DMIHER Faculty of Engineering And Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India DMIHER Faculty of Engineering And Technology Department of Computer Science and Engineering Maharashtra Wardha India

ISBN: (纸本)9798331505745

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, Software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results. © 2025 IEEE.

关键词： Support vector machines

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