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International Conference on Inventive Computation Technologies (ICICT)

作者： Prathmesh Waghmare Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Chronic Diabetes Mellitus (DM) is typified by glucoregulatory system dysregulation, which results in hyperglycemia and long-term consequences that impact several organs. With over 425 million people affected globally, early diagnosis and effective management of diabetes are crucial. Recent advancements in artificial intelligence (AI) and machine learning (ML) provide encouraging ways to enhance the treatment of diabetes. The goal of AI is to develop systems that can handle complicated data, which is particularly useful for managing diabetes. As a subset off AI, ML enables the creation of tailored treatment plans and predictive algorithms that improve patient outcomes and diagnostic accuracy. AI and ML integration in the treatment of diabetes has led to significant innovations, such as automated insulin delivery systems and AI-driven clinical decision support tools. These technologies improve glucose management and patient empowerment by providing real-time feedback and personalized recommendations. Additionally, AI applications in retinal screening and genomics have advanced the diagnosis and understanding of diabetic complications and genetic predispositions. Despite these advancements, challenges remain, including data privacy concerns, technical barriers, and limitations in AI model design. Robust data management, device compatibility, and continual AI algorithm validation are necessary to overcome these challenges. All things considered, AI and ML have enormous potential to change the way diabetes is treated by facilitating early diagnosis, individualized therapy, and better patient self-management, all of which will eventually improve the quality of life for those who have the disease.

关键词： Technological innovation machine learning algorithms Reviews Medical treatment machine learning Retina Prediction algorithms Real-time systems Diabetes Insulin

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Enhancing Prognostication in Colorectal Cancer with Integrated machine learning for Improved Survival Prediction

Enhancing Prognostication in Colorectal Cancer with Integrat...

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

作者： Velmurugan, R. Suresh, K. Thilagaraj, T. Sharma, Meenakshi Saranya, D. Bangalore India Department of Computer Science Bangalore India Moodlakatte Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Kundapura India Global Group of Institutes Punjab Amritsar India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India

ISBN: (纸本)9798331509828

machine learning methods are recently used to predict patient survival in colorectal cancer using such models as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), VGG16, and Support Vector machines (SVM). Taking advantage of a combination of CT, MRI scan images, and clinical records with drug recommendations, the study also checks to see how these models compare for distinguishing between patients in terms of their illness course-whether they are going to get better or worse over time. The results reveal VGG16 has better accuracy than CNN, RNN and SVM;as the highest-performing model tested, it also demonstrates superior precision, recall and F1-score. The research findings also validate these proposed models as they compare favorably with existing literature. This presents a promising proposition: a new, revolutionary approach to using artificial intelligence to boost prognostic accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Pneumonia Treatment in Children by machine learning Techniques

Enhancing Pneumonia Treatment in Children by Machine Learnin...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Ashwini Gajbhiye Swapnil Gundewar Prateek Verma Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Pneumonia is a health challenge that affects many people more so the children, thus leading to high death rates across the world. There is a persuading evidential proof which shows that the techniques in the application of the machine learning (ML) have helped in improving the diagnosis and treatment of pneumonia. This paper discusses a number of ML techniques such as CNNs, SVMs and Random Forest that can be used to enhance the reliability and effectiveness of detecting pneumonia from enhanced chest x-ray images. The classification shows how other methods like Min-Max normalization and a specific type of image pre-processing called Contrast-Limited Adaptive Histogram Equalization (CLAHE) can make image quality even better as well as increase the accuracy of the model. Here, by using the sophisticated dalliance of ML models it is possible to dramatically minimize the incidence of diagnostic errors and enhance the results of pneumonia treatments thus witnessing an important evolutionary leap in the specialty.

关键词： Deep learning Pediatrics Pneumonia Accuracy Mortality machine learning X-rays Reliability X-ray imaging Biomedical imaging

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Next-Gen AI in Healthcare: Shaping the Future of Precision and Preventive Medicine

Next-Gen AI in Healthcare: Shaping the Future of Precision a...

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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： Yash Tale Prateek Verma Tanushri Pake Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Healthcare professionals benefit from revolutionary changes because of current developments in artificial intelligence technology, which optimise precision medicine and preventive medical practices. artificial intelligence technology transitioned from basic rule-based systems into complex deep learning models that accurately analyse intricate medical data over decades. This paper investigates AI's significant influence on healthcare, including diagnostic and treatment operations, current robotics, and automated patient care systems. AI systems assist medical staff by improving precise diagnoses and simplifying treatments while boosting patient supervision through automated intelligence. The advantages of AI medicine simultaneously trigger crucial legal and moral dilemmas about healthcare data security, automated system bias, and AI-developed judgmental processes. The paper examines these issues regarding AI health care deployment while presenting guidelines to establish responsible and equitable use of AI technology in medical applications. The discussion explores how AI will influence genomic research and robotic surgery while demonstrating its potential to transform healthcare practice globally. This paper examines the complete transformation of contemporary medical care through an analysis of AI effects with identified difficulties and forthcoming potential.

关键词： Ethics Accuracy Precision medicine Biological system modeling Surgery Medical services Transforms artificial intelligence Predictive analytics Medical diagnostic imaging

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A Comprehensive Analysis of Secure Attack Detection on Virtual machines in Cloud Computing 4

A Comprehensive Analysis of Secure Attack Detection on Virtu...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Nemirajaiah, Naveen Kumar Adalagere Raju, Channa Krishna Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Computer Science and Engineering Tumakuru India Sri Siddhartha Institute of Technology Sri Siddhartha Academy of Higher Education Department of Artificial Intelligence and Machine Learning Tumakuru India

ISBN: (纸本)9798350352931

Cloud computing provides various computing services including processing power, storage, networking, software, analytics, and databases. A Virtual machine (VM) is a software-based simulation of a physical computer that runs an operating system and applications. The VM can run multiple functions on a single physical machine. Secure attack detection on VMs utilizes implementation techniques to detect and monitor malicious activities in the cloud environment. This detection involves Intrusion Detection Systems (IDS), anomaly detection methods, and real-time monitoring tools that analyse network traffic, system logs, and user behaviour. Optimization techniques are used to enhance the efficiency of the detection models. Swarm-based algorithms such as Intelligent Water Drop (IWD), Enhanced Shark Smell Optimization Algorithm (ESSOA), Brownian movement-centered gravitation search algorithm (BMGSA), Dual Conditional Moth Flame Algorithm (DC-MFA), Bat Optimization Algorithm (BOA), and Ant Colony Algorithm (ACO) are employed for this purpose. Additionally, Evolution-based algorithms such as Multi-objective Whale Optimization algorithm-based Differential Evolution (M-WODE), Gene Expression Programming (GEP), Genetic Algorithm (GA), and DragonFly-based Genetic Deep Belief Network (DGDBN) have also been used for anomaly detection. The performance metrics of makespan, energy consumption, Degree of Imbalance (DOI), and execution cost are used to evaluate these approaches. © 2024 IEEE.

关键词： Genetic algorithms

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Home Automation Using Wi-Fi: ESP32-Based System for Remote Control and Environmental Monitoring 3

Home Automation Using Wi-Fi: ESP32-Based System for Remote C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Abdul Lateef Haroon, P.S. Shafiulla, Mohammed Naveed, Shaik Mohammed Ahmed, Suhail Nawaz, Shaik Mohammed Kumar, Utkarsh Ballari Institute Of Technology & Management Department Of Electronics And Communication Engineering Ballari India Ballari Institute Of Technology And Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Computer Science And Engineering Ballari India Ballari Institute Of Technology & Management Department Of Artificial Intelligence And Machine Learning Ballari India

ISBN: (纸本)9798350318609

This project introduces a comprehensive home automation system leveraging Wi-Fi connectivity and ESP32 micro controllers, aimed at enhancing modern living through seamless automation and user-centric design. The background emphasizes the fusion of IoT technology with home automation, highlighting Wi-Fi's significance in enabling remote monitoring and control of household devices. Methodologically, the project integrates relay modules and DHT11 sensors, orchestrated through a user-friendly smartphone application. Results demonstrate successful implementation and testing, showcasing functionality, reliability, and user-friendliness. Real-time environmental monitoring and remote appliance control fulfill the objectives of enhancing home comfort, convenience, and energy efficiency. The conclusion underscores the project's contribution to advancing smart home technologies, offering users a scalable and customizable solution. Future research opportunities include enhancing system capabilities and addressing security and privacy concerns. © 2024 IEEE.

关键词： Microcontrollers

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis 3

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Chilupuri, Harshitha Ramesh, G. Praveen, J. Gude, Venkataramaiah Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Artificial Intelligence and Machine Learning Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad500090 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Electrical and Electronics Engineering Telangana Hyderabad500 090 India Gp Technologies Llc United States

ISBN: (纸本)9798331515911

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods. © 2025 IEEE.

关键词： Data Science

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A Hazelnut Tree Search Algorithm for Enhancing Large-Scale Electric Vehicle Charging on the Power Grid 3

A Hazelnut Tree Search Algorithm for Enhancing Large-Scale E...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Kavithamani, K. Priya, G. Adline Saranya, S. Anupriya, K. Ramkumar, M. Siva Kaliappan, S. Department of Electrical and Electronics Engineering Karpagam College of Engineering Tamilnadu Coimbatore641032 India Department of Information and Technology Sri Krishna College of Technology Tamilnadu Coimbatore641042 India Department of Artificial Intelligence and Machine learning Hindusthan College of Engineering and Technology Tamilnadu Coimbatore641050 India Dept of ECE SNS College of Technology India

ISBN: (纸本)9798331532420

Electric vehicle (EV) adoption on a large scale would drastically change patterns of electricity demand, posing both opportunities and challenges for modern power systems. However, the increased and often unpredictable demand from simultaneous EV charging can strain grid infrastructure, leading to potential overloads and instability. This paper proposes the Hazelnut Tree Search Algorithm (HTSA) as an effective optimization method for large-scale EV charging. The objective of the proposed method is to minimize electricity costs and improve grid efficiency in the EV charging. By optimizing charging schedules, HTSA enhances grid stability and improves load balancing throughout the power grid (PG). By then, the proposed method is implemented in MATLAB platform and compared to other existing methods such as Ant Colony Optimization (ACO), artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and NSGA-II. Results show that HTSA reduces electricity costs to 4.3 INR/kWh, and achieves 96% of higher efficiency compared to other existing methods. This superior performance demonstrates HTSA’s potential for optimizing EV charging strategies while minimizing grid disruptions and enhancing the integration of renewable energy sources (RES). © 2024 IEEE.

关键词： Power grids

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Utilising SkyScript for Open-Vocabulary Categorization, Extraction, and Captioning to Enhance Multi-Modal Tasks in Remote Sensing

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Remote Sensing in Earth Systems Sciences 2024年第3期7卷 149-158页

作者： Nagaraj, Saranya Sivakumar, Shanmuga Priya Annabel, Lawrence Sherly Puspha Joshi, Vilas Ramrao Patil, Mithun Baswaraj Patil, Vishal Ratansing SRM Institute of Science and Technology School of Computing SRM Institute of Science and Technology Department of Artificial Intelligence and Machine Learning St. Joseph’s College of Engineering Department of Computer Engineering ISBM College of Engineering Department of Computer Science and Engg N K Orchid College of Engg and Technology Solapur Department of CSE (AIML) Pimpri Chinchwad College of Engineering Nigdi

The SkyScript dataset was developed by integrating large-scale remote sensing images from Google Earth Engine with geo-tagged semantic data from OpenStreetMap. This open-access dataset, consisting of 2.6 million image-text pairs covering 29,000 unique tags, facilitates various remote sensing tasks such as cross-modal retrieval, image captioning, and classification. The dataset ensures global representation and semantic diversity, although it exhibits a higher concentration of high-resolution images from the USA and Europe due to licencing constraints. The images, sourced from multiple collections with varying ground sampling distances, are paired with captions generated using a combination of rule-based methods and logistic regression models for tag classification. Experiments demonstrate that models pre-trained on SkyScript outperform those trained on other datasets in zero-shot classification and fine-grained attribute recognition, highlighting its potential for advancing vision-language models in remote sensing applications. Future improvements could involve enhancing geographic coverage and refining caption quality using advanced language models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Image captioning Remote sensing Sampling distance Vision language

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Enhanced Prediction Of Rheumatoid Arthritis Flares Through Ensemble learning Methods 2

Enhanced Prediction Of Rheumatoid Arthritis Flares Through E...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Priyadharshini, K. Vinitha, S. Sivaprakash, S. Kayalvili, S. Thilakavathy, P. Vasavi, M. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Artificial Intelligence and Machine Learning Jerusalem College of Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Artificial Intelligence Kongu Engineering College Erode India Vels Institute of Science Technology and Advanced Studies Chennai India ET Department CVR College of Engineering Hyderabad India

ISBN: (纸本)9798350383867

RA is an inflammatory, autoimmune disease with an unpredictable course, during which patients experience exacerbations (increases in symptoms) and its associated complications, such as deterioration of the joint and decreased quality of life. Tipping the exact point when remission is reached or RA flares is most essential for devising optimal treatment and enhancing patient's well-being. In this study, we put forward the usage of various types of ensemble learning algorithms aimed at improved the predictive power of RA flares. We use diverse data sources, including clinical data, medical imagery, genetic profiles and patient-reported data, and ensemble methods like Random Forest, Gradient Boosting, AdaBoost and Stacking processors to help us identify the patterns automatically. Thus ensembles of models are shown to be leading in performance, reaching superior accuracy, accuracy precision, recall rate, F1-score and area under the receiver operating characteristic curve (AUC-ROC). Feature importance analysis highlights the significant impact of clinical features which include the levels of CRP, ESR, age of the patient, duration of the disease on the likelihood of RA flares among the individual variants. The sensitivity analysis shows how well the ensemble model behaves under the generation of different model parameters and input data. The presented study emphasizes the immense facilitative ability of the ensemble methods of learning as predicting RA flares and this can also provide great information about the disease process and personalized medical decision-making. The areas for future research could be improved ensembles models, identification of novel feature selection approaches and validation of model performance in a variety of clinical settings. This will give the researchers an opportunity to implement the models in practice and make clinics more effective. © 2024 IEEE.

关键词： Sensitivity analysis

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