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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Mansi Vyas, Vaibhav Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computer Science and Engineering- AIML ABES Engineering College UP Ghaziabad India

ISBN: (纸本)9798331530389

Using blockchain for student career prediction offers key benefits, including enhanced data security and privacy through encryption and immutability, ensuring personal information protection. It provides transparency and trust with a tamper-proof record of achievements and skills, fostering confidence among employers and educational institutions. Decentralization eliminates the need for a central authority, reducing risks of data loss or manipulation. Additionally, blockchain enables accurate traceability of student progress, ensuring accountability in career prediction data. Using machine learning to develop a prediction model for student career prediction offers substantial benefits by enabling personalized, data-driven insights that can significantly enhance career guidance. With this vast amount of data from various sources, identifying complex patterns and correlations that may not be evident through traditional analysis. This allows for highly accurate, individualized career recommendations, reducing biases and ensuring fair evaluations. Moreover, this model will continuously improve over time, adapting to new data and refining predictions, ultimately leading to more informed and effective career planning for students. Applying machine learning algorithms on student data stored on blockchain will provide exact information about students including their strengths and weaknesses both. This will lead to a better prediction and effecting planning. © 2025 IEEE.

关键词： Prediction models

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A Comprehensive Comparison of Machine Learning Classification Techniques for Early Detection of Alzheimer's Disorder

A Comprehensive Comparison of Machine Learning Classificatio...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vairale, Prathmesh Dubey, Vandana IES-IPS Academy Department of Computer Science and Engineering-AIML Indore452012 India IES-IPS Academy Department of Computer Science and Engineering Indore452012 India

ISBN: (纸本)9798331512965

Alzheimer's disorder (AlzD) is a chronic condition in which brain cells degenerate and eventually die. People over the age of 65 are most likely to get this condition. There is no cure for this, but nowadays proper medications and treatments are available to slow down the disease progression temporarily. Detection of AlzD in its early stage may help doctors and physicians provide proper medicines as early as possible to slow its development. Artificial intelligence is a blooming field in every sector nowadays that can help in AlzD detection to save us from time-consuming and expensive conventional methods. Various machine learning algorithms are researched to prevent AlzD. This paper uses different linear, distance, and tree-based supervised classifier algorithms. The used dataset contains different psychological parameters like a person's age, number of visits, Mini-Mental State Examination (MMSE), and years of education, on which machines can be trained to predict whether the person is demented or non-demented. The main goal of this research paper is to compare these classification algorithms which give the best result in the early detection of AlzD. RFC, KNN, and MLP algorithms outperform all other algorithms with an accuracy of 96.77%, followed by NB and LR algorithms with 95.70%. © 2025 IEEE.

关键词： Random forests

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Explainable AI for Leukemia Diagnosis: Modified ResNet with Hard Attention Mechanisms 1

Explainable AI for Leukemia Diagnosis: Modified ResNet with ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramanujam, E. Sekar, K. Bavashree, G.S. Prabakar, Shreya Sai National Institute of Technology Department of Computer Science and Engineering Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India

ISBN: (纸本)9798331529246

Leukemia is a highly complex and aggressive form of cancer, necessitating accurate and timely diagnosis for effective treatment. This study investigates the use of Explainable Artificial Intelligence (XAI) techniques to enhance computer-aided diagnosis (CAD) systems for leukemia. A modified ResNet architecture, integrated with a hard attention mechanism, is proposed to improve model performance and interpretability. The approach emphasizes robust segmentation of white blood cell (WBC) nuclei, utilizing advanced image processing techniques and U-Net models for precise feature extraction. Model evaluation focuses on both accuracy and interpretability, employing Grad-CAM and clustering space analysis to assess network focus and transparency in decision-making. The results underscore the strengths and limitations of the proposed architecture, offering insights into further advancements in XAI to improve the reliability and transparency of CAD systems. This work ultimately aims to advance clinical outcomes in leukemia diagnosis by providing more reliable and interpretable diagnostic tools. The proposed model produces 99.7% of accuracy for detection and classification of leukemia. © 2025 IEEE.

关键词： Diseases

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Leveraging Hybrid CNN Model for Enhanced Classification on Imbalanced Datasets 1

Leveraging Hybrid CNN Model for Enhanced Classification on I...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Murugan, R. Sekar, K. Sariga, G. Nikhilesh, V. Tejashwini, J.S. Dheepa, T. National Institute of Technology Silchar Department of Ece Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India Kongunadu College of Engineering and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

This study addresses the challenge of class imbalance in the CIFAR-10 dataset, where certain classes are significantly underrepresented. To tackle this issue, data augmentation and resampling techniques, particularly oversampling, are employed to achieve a balanced class distribution. A Convolutional Neural Network (CNN) is trained using a class-weighted loss function to effectively mitigate residual imbalances. The model's performance is evaluated on test images, with a ResNet model incorporated for comparison. This hybrid approach enables a comprehensive assessment of classification accuracy and robustness. The proposed model achieves accuracy, precision, and recall scores of 0.95, 0.88, and 0.93, respectively. The results demonstrate significant improvements in the prediction accuracy of minority classes, underscoring the effectiveness of the applied methods. This study highlights the critical role of oversampling and class-weighted loss functions in addressing class imbalance challenges, ultimately enhancing the performance of image classification models. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart food choices: the role of reliable and safe AI in nutrition recommender systems

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Life Cycle Reliability and Safety engineering 2025年 1-11页

作者： Jayaswal, Ruchi Ansari, Mohd. Aquib Mewada, Arvind Department of AIML Symbiosis Institute of Technology Maharashtra Pune 412115 India School of Computer Science Engineering and Technology Bennett University UP Greater Noida 201310 India

Inappropriate food consumption can lead to various medical problems and illnesses. Individuals usually lean on drugs instead of taking preventative action in their food consumption due to a lack of sufficient knowledge about healthy nutrition. The impact of well-established applications on clinical outcomes is still not completely known. A major share of the worldwide population has already become overweight, which is resulting in a variety of ailments caused by bad eating habits, such as increasing consumption of high-energy and high-fat foods. Thus, to address this, a diet recommendation system will likely provide a better solution for people’s healthy eating habits. This study provides a unifying paradigm for food recommendation systems and discusses key challenges impacting them, such as incorporating different contexts and domain information. We examine existing solutions to these problems before elaborating on research constraints and future initiatives in this subject. © The Author(s), under exclusive licence to Society for Reliability and Safety (SRESA) 2025.

关键词： Caloric requirements Dietary suggestions Health and wellness Multi-sensor fusion Nutrition recommender systems Personalized meal preferences

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NEMO: ML based fish species classification 2

NEMO: ML based fish species classification

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Singh, Manvendra Gaur, Sreesh Tyagi, Sarthak Sharma, Udit Yadav, Prince Mishra, Adarsh Department of Computer Science KIET Group of Institutions Delhi Ghaziabad India Department of CSE-AIML ABES Engineering College U.P. Ghaziabad India

ISBN: (纸本)9798331530389

This paper describes the creation of an ML-based tool for image-based identification of fish species commonly found in Indian oceans, addressing the issues that fishermen experience in accurately reporting their catch. Motivated by the Smart India Hackathon (SIH) issue statement from the Indian National Centre for Ocean Information Services (INCOIS), our goal is to improve marine fishing advice services. We developed a dataset that includes photos of 21 fish species typically captured by Indian fishermen, precisely chosen to ensure clarity and little background noise. Using this dataset, we applied transfer learning to eight different pre-trained models to determine the most effective one for deployment in a mobile application. The Kotlin-developed program has an easy-to-use interface with catch history, fish nutritional values, and pertinent weather data. Our findings show that the installed models can correctly recognize fish species, with MobileNetV2 reaching a peak accuracy of 97.61%. In addition to expediting the species identification process, this cutting-edge tool provides fishermen with crucial information, which eventually leads to more accurate species-level catch reporting. © 2025 IEEE.

关键词： Fish

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Intelligent Control Mechanisms for Renewable Energy Integration Using Machine Learning Models

Intelligent Control Mechanisms for Renewable Energy Integrat...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Chahar, Suman Kaur, Kawaljit Kaswan, Kuldeep Singh Malik, Kiran University Institute of Computing Chandigarh University Mohali India Galgotias University Department of Computer Science and Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Department of Computer Science and Engineering-AIML Greater Noida India

ISBN: (纸本)9798331508685

The deployment of renewable energy sources (RES) in power systems used to be a major challenge owing to their intermittent and uncertain nature. Therefore, in this paper, the use of ML models as an intelligent control system to improve the operation of RES is studied. We survey different types of ML approaches, covering supervised learning for energy predic- tion, unsupervised learning for clustering demand, reinforcement learning for energy storage management, and deep learning for real time data processing. The intent of these advanced control strategies are to balance and stabilize the grid, harmonize demand response, and augment energy storage. We highlight the use-cases and impacts of ML from real-world cases across various geographies, showing operational gains and peak-demand savings. While these technologies hold great promise, they also face challenges like data privacy, interpretability of models, and availability of high-quality datasets. Moving forward, there is a need to improve transparency of algorithms and to strengthen the collaboration of different stakeholders. Our work paves the way for a systematic and automated approach to renewable energy integration, which can serve as a critical factor to build a reliable and efficient future modern power systems with machine learning. © 2025 IEEE.

关键词： Adversarial machine learning

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Groundwater Level Forecasting using Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost 6

Groundwater Level Forecasting using Spatio-temporal Graph Co...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Naga Satish, G. Jadala, Vijaya Chandra Sravani, Kolisetti Giridhara Allam, Sangeetha Shanmuga Sundari, M. Thada, Jyothirmai BVRIT HYDERABAD College of Engineering for Women Hyderabad India Department of Computer Science and Artificial Intelligence School of Computer Science and Artificial Intelligence SR University Warangal506371 India Department of AIML Gokaraju Rangaraju Institute of Engineering And Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331522667

This research analyzes groundwater levels across multiple districts using data from over 100 observation wells in each district. To capture seasonal variations and predict groundwater behavior, this research has developed three models: periodic, polynomial, and rainfall-based. The periodic and polynomial models describe groundwater level fluctuations based on historical well data, while the rainfall model assesses the influence of precipitation on water levels in the wells. In addition, this research explores advanced predictive techniques by incorporating Spatio-temporal Graph Convolutional Networks (GCNs) and XGBoost. These methods enable a more nuanced understanding of spatio-temporal dependencies and improve predictive accuracy by leveraging both spatial relationships among wells and the temporal evolution of groundwater levels. The integration of traditional models with machine learning techniques aims to enhance groundwater management and inform decision-making. © 2025 IEEE.

关键词： Rain

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Social Networks on WEB 3.0 12th

Social Networks on WEB 3.0

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Cidri, Anirudh AshaRedddy, M. Bhaskar, T. Vijaya Krishna, A. Suryanarayana, G. Department of Information Technology Vardhaman College of Engineering Hyderabad India Department of Computer Science & Engineering-AIML CMR College of Engineering Hyderabad India Department of Information Technology G. Narayanamma Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819601462

The "Social Network on WEB3.0" project is poised to revolutionize online social dynamics by leveraging blockchain technology to create a decentralized platform. This initiative prioritizes user control and security, aiming to address the shortcomings of existing Web 2.0 networks. Users will have unprecedented control over their data and privacy, with blockchain ensuring transaction irreversibility. Key goals include user empowerment, enhanced data security, decentralization, incentivized participation, and adherence to legal and privacy standards. The project incorporates a token-based incentive system to stimulate user engagement and collaboration, fostering a vibrant online community. Leveraging Ethereum and MetaMask for transactions and smart contracts highlights the project’s adaptability and scalability. With a commitment to ongoing enhancement and user feedback, the initiative heralds a significant shift toward a digital landscape characterized by user-centricity, heightened security, and privacy awareness, offering individuals unparalleled authority over their online interactions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Integrating AI, IoT, and Drones for Sustainable Apple Orchard Monitoring in Society 5.0 2nd

Integrating AI, IoT, and Drones for Sustainable Apple Orch...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Datta, Ankana Paul, Sukalpa Pal, Anidipta Biswas, Sounav Bag, Anil Kumar Sengupta, Diganta Department of Computer Science and Engineering - AIML Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Applied Electronics and Instrumentation Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India Department of Computer Science and Engineering Heritage Institute of Technology Chowbaga Road Kolkata700107 India

ISBN: (纸本)9783031845420

Sustainable smart agriculture forms one of the focal points in Society 5.0. We propose an AI-IOT enabled framework capable of processing multi-modal data for apple orchard monitoring. Real-time data acquisition is done from ground sensors as well as drone fitted cameras. The ground sensors monitor soil moisture, pH levels, nutrient composition (nitrogen, phosphorous, and potassium), ambient temperature, and humidity, while ESP32-CAMs fitted on drones capture images of apples, leaves, and trees. IOT enabled Unmanned Aerial Vehicle (UAV) as well as the ground sensor framework feed the data to the cloud. YOLOv8 and ResNet152 have been used to process the images for classifying the health of the apple plants. Machine learning models predict the farm yield using the ground sensor data reflecting the soil conditions. Our framework fares better than prior art in terms of accuracy. Although existing literature exhibits processing of soil data for prediction of health and yield, our study takes into consideration three further nutrient components - nitrogen, phosphorous, and potassium. Our study shows accuracy of 98.19% (apple counting) 53.66% (apple classification) 96.00% (leaf classification). To the entirety of our understanding, this study is the first to use multi-modal data inclusive of extended soil nutrients. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Orchards

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