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

作者： Ashok, K. Mattew, Jimsha K. Chandana, K.S. Maharathi, G.N. Abhilash, G.M. Somani, Nithish Computer Science and Engineering New Horizon College of Engineering Bengaluru India Artificial Intelligence and Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331522667

This research study presents a comprehensive multilingual approach to crop disease diagnosis aimed at transforming agricultural practices. The proposed system leverages advanced deep learning models such as ResNet, EfficientNet, and MobileNet to automate plant phenotyping using high-resolution images. Beyond disease identification, it provides intelligent plant parenting recommendations to enhance crop breeding strategies. The system is equipped with multilingual support and offline capabilities, ensuring global accessibility, especially in areas with limited internet access. Additionally, the model delivers outputs by detecting diseases and offering diagnosis in the user’s native language, facilitating better decision-making for farmers. Experimental results from real-world scenarios demonstrate the system’s high accuracy and potential to drive sustainable agriculture, accelerate genetic progress, and improve crop prediction. © 2025 IEEE.

关键词： Smart agriculture

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Revamping Primary Programming Education with Augmented Reality Teaching via EnvisionAR 5

Revamping Primary Programming Education with Augmented Reali...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Annie Silviya, S.H. Anitha, R. Gayathri, P. Hiranmaie, N. Harini, R. Hareene, D. Rajalakshmi Institute of Technology Computer Science and Engineering Chennai India Rajalakshmi Engineering College Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350385649

This study introduces a transformative approach to primary programming education, leveraging Augmented Reality (AR) teaching methodologies through the innovative EnvisionAR platform. In response to the dynamic technological landscape, this research addresses the need for engaging, immersive learning experiences for young learners. The EnvisionAR platform, a pioneering fusion of AR technology and tailored curriculum, caters to the cognitive development stages of primary school students. By seamlessly integrating virtual elements into the physical environment, students engage in interactive, hands-on learning that cultivates creativity, problem solving skills, and a profound understanding of programming concepts. This research signifies a broader potential for AR integration in diverse educational disciplines. © 2024 IEEE.

关键词： Augmented reality

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Secure Healthcare Monitoring: Algorithms and Techniques for Heart Disease Detection and Data Privacy 1

Secure Healthcare Monitoring: Algorithms and Techniques for ...

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

作者： Poonam Mallapur, Sujata V. Sharnbasva University Department of Computer Science and Engineering Kalaburagi India Sharnbasva University Department of Artificial Intelligence & Machine Learning Kalaburagi India

ISBN: (纸本)9798331529246

This survey offers the review of Healthcare Monitoring Systems (HMS) and Privacy Preservation (PP) approaches. The main objective is based on the detection of heart disease and maintain the security for patient data. The machine learning models such as SVM, decision trees, and neural networks for heart disease detection and encryption approaches such as AES, ECC, and homomorphic encryption for securing the data are the major topics that reviewed in this survey. Moreover, the survey highlights the performance evaluation for heart disease prediction and the datasets used for heart disease. It also ensures the privacy to tackle the challenges in real time monitoring. A higher accuracy of 99.8 was reached by FHE-based encryption method. Moreover, it discusses the research gaps like scalability, efficiency, and regulatory compliance which motivates the need of novel privacy-preserving techniques for healthcare systems, which is highlighted in the future scope. © 2025 IEEE.

关键词： Differential privacy

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Identification of Infectious Disease Like COVID-19 Gene Biomarkers Using a Clear artificial intelligence Approach

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SN computer Science 2025年第4期6卷 1-13页

作者： Dubey, Shivendra Verma, Dinesh Kumar Kumar, Mahesh Department of Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Jaypee University of Engineering and Technology MP Guna India

This study offers an approach that uses SARS-COV-2 mNGS (meta-genomic next-generation sequencing) samples to apply XAI (explainable artificial intelligence) methodologies derived from the use of machine learning methods. The research's data set contains 15,979 expressions of genes from 234 infected persons, of whom 39.68% (93) had SARS-COV-2 that was positive and 60.29% (141) had SARS-COV-2 that was negatives. The SARS-COV-2 related gene was chosen using the LASSO (least absolute shrinkage and selection operator) technique. The class imbalanced issue has been resolved using the SVM-SMOTE (Support Vector machine - Synthetic Minority Oversampling Technique) approach. To identify SARS-COV-2 related biomarkers possibilities and increase the comprehensibility of the final approach, an explainable strategy that utilized SHAP (SHAPley Additive exPlanations) as well as LIME (local interpretable model-agnostic explanations) approaches were used. The XGBoost (92.98% accuracy) approach performs better for the identification of infectious diseases like COVID-19 than the LR (91.17% accuracy), SVM (87.68% accuracy), and RF (91.18% accuracy) models. The three most significant genes connected to COVID-19 by the SHAP method were FAM83A, LGR6, and IFI27. The LIME findings demonstrated that the likelihood of a positive class was particularly influenced by the elevated degree of IFI27 expression of genes. Infectious diseases like COVID-19 could be accurately predicted using the suggested framework (XGBoost). The findings demonstrate that SHAP and LIME, along with the exploit of ML methods, it could be clarify the biological marker prognosis for infectious disease and give health center a clear knowledge about how potential risk factors affect the forecasts made by the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomarker Gene LIME SARS SHAP

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Advanced Energy Consumption Forecasting in Smart Grids Using Quantum Boltzmann machines 2

Advanced Energy Consumption Forecasting in Smart Grids Using...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Velayutham, P. Balaji, V.S. Krissh Shankaran, N. Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388916

Smart grid systems are seen as the next big thing to energy distribution and management with more efficiency, stability, and sustainability in power distribution and energy management systems. It is challenging to predict the amount of energy consumption in smart grid system because of its randomness resulting from various factors such as consumer's behavior, weather conditions. This is mainly because traditional forecasting techniques are usually inadequate in capturing these complexities and as a result resources are poorly allocated and organizational processes become inefficient. The work provides the energy consumption forecasting in smart grids through the application of Quantum Boltzmann machine(QBM) in the smart grid utilizing historical data. Some of the variables incorporated in the data are reaction time, power balance, and price elasticity coefficients customized through smart meters. Once raw data is preprocessed, QBMs are developed to establish an empirical probability model that will predict actual future energy utilization characteristics. The QBM is tested against the actual consumption data and focuses on the visualization of energy profile and identification of points like local extremes. © 2024 IEEE.

关键词： Smart power grids

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Hybrid Design for Privacy Preserved Image Representation in a Cloud Environment 12th

Hybrid Design for Privacy Preserved Image Representation in ...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. 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 Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Crafts-Connect - Empowering Rural Artisans and Preserving Cultural Heritage

Crafts-Connect - Empowering Rural Artisans and Preserving Cu...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijay, K. Reshma, R. Sanjay, S. Shriram Kumar, A.N. Sowmiya, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

India has always been a land steeped in cultural traditions, where artisans have honed their artistic and craft skills over centuries. What was the foundation of their lifeblood - skills and traditions to be passed from generation to generation? Taking cue from the heritage, Crafts-Connect, an online marketplace, attempts to revive this legacy and helps rural artisans gain on it. It has a double mission: economic empowerment and cultural preservation. We empower artisans to rise from poverty by showcasing unique, handcrafted products to a global market. With our ConnectCrafts, our mission extends beyond mere sales. We provide programs that will help in the skill development of artisans and make sure the craft is kept alive. Encouraging responsible production and community involvement also contributes to respecting the traditional methods of performing these lesser-known techniques. It facilitates a one-on-one relationship between artisans and consumers, the most potent form of economic interaction. They can receive feedback on their work, empowering them to fulfill custom product requests (on-demand product design) or even conceptualize and iterate new pain points/products, which in turn provides the designers insights with market knowledge. Crafts-Connect provides a direct channel for interaction between artisans and consumers. This two-way communication allows artisans to get feedback on their work and receive bespoke product orders. This interaction gives them design insights and market learnings, making the makers even more refined in their work. Overall, Crafts-Connect wants to be a positive social change force. We believe in creating a sustainable future through an innovative digital model that supports India's rich cultural heritage and the livelihoods of rural artisans. © 2024 IEEE.

关键词： Marketplaces

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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MarineLens: Empowering Conservation through AI-Powered Fish Species Detection and Insights

MarineLens: Empowering Conservation through AI-Powered Fish ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sorna Shanthi, D. Vijay, K. Bhuvaneswaran, B. Praveen, N. Priyan, B. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390049

MarineLens is a groundbreaking conservation tool that leverages the power of artificial intelligence to revolutionize fish species detection and insights in marine environments. Using advanced machine learning algorithms, MarineLens is able to accurately identify and track different fish species, providing researchers and conservationists with valuable data on population dynamics, behavior patterns, and habitat usage. By harnessing the capabilities of AI, this technology offers a cost-effective and efficient solution for monitoring marine life and assessing the impact of human activities on underwater ecosystems. MarineLens enhances the ability to make data-driven decisions and implement targeted conservation strategies to protect vulnerable species and preserve biodiversity. With its user-friendly interface and customizable features, MarineLens empowers users to generate actionable insights and visualize complex data in a comprehensible manner, facilitating collaboration and knowledge sharing within the conservation community. Enabling a proactive approach to marine conservation, MarineLens is a game-changer in safeguarding our oceans for future generations. © 2024 IEEE.

关键词： Scalability Oceans Marine vegetation Fish Biodiversity Environmental management Sustainable development Protection Marine animals Monitoring

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A Novel AI-Assisted e-Consult Platform Integrating Deep learning for Enhanced Healthcare Access and Diagnostic Precision

A Novel AI-Assisted e-Consult Platform Integrating Deep Lear...

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

作者： Vijayasekaran, G. Rajasree, R.S. Alam, Parvez Suryavanshi, Abhishek Ranjanagi, Vinayak Mulla, Umar Farooq New Horizon College of Engineering Dept of Computer Science and Engineering Bengaluru India Learning New Horizon College of Engineering Dept of Artificial Intelligence and Machine Bengaluru India

ISBN: (纸本)9798331509828

This study introduces the 'AI-Assisted E-Consult' initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult platform, serving as a digital interface that links patients, healthcare provider, and medical facilities. Utilizing state-of-the-art technologies such as AI and machine learning, the platform streamlines predictive health insights and appointment scheduling, thereby improving access to quality medical care. The platform employs Natural Language Processing (NLP) via the NLTK library and uses a Recurrent Neural Network (RNN) architecture trained on medical conversation datasets to power an AI chatbot. This chatbot analyzes symptoms and predicts potential health conditions, offering users preliminary insights into their health status. Furthermore, the system incorporates Haversine distance calculations to measure the geographical proximity between patients and doctors, enhancing logistical planning for consultations. To evaluate the platform's accuracy and performance, metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) will be used, ensuring precise predictions and operational effectiveness. The AI- Powered E-Consult platform is designed to eliminate healthcare barriers and maximize resource efficiency. Offering scalable, adaptable solutions tailored to the needs of contemporary healthcare, this platform represents a significant advancement toward a more accessible, efficient, and time-saving healthcare model, particularly for underserved populations. © 2025 IEEE.

关键词： Recurrent neural networks

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