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

作者： Ravikumar, S. Eugene Berna, I. Babu, R. Arockia Raj, Y. Vijay, K. Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Erode India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

An exciting new direction in plant categorization and identification is the use of machine learning techniques for medicinal plant detection. Using a variety of photo datasets containing medicinal plants, this technique trains models using ANNs. The ML model learns the complex traits that distinguish various plant species by using data from pre-trained models or by developing bespoke architectures. Thanks to the trained model’s precision in visually recognizing therapeutic plants, everyone from researchers to ecologists can reap the benefits. Methods for medicinal plant identification that are both accurate and objective are crucial to biodiversity research and attempts to safeguard resources. Considerations of ethics, model calibration, and data curation should be incorporated into the construction of such systems. Gather any and all images of plants used for medical purposes. Various viewpoints, lighting scenarios, and plant types will enhance the set. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Plant diseases

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GENOCARE PROGNOSTICATOR MODEL: HOST GENETICS PREDICT SEVERITY OF INFECTIOUS DISEASE

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Scalable Computing 2025年第2期26卷 924-939页

作者： DUBEY, SHIVENDRA SINGH, SHWETA VERMA, DINESH KUMAR LODHI, SUDHEER KUMAR DUBEY, SAKSHI Department of Artificial Intelligence and Machine Learning Manipal University Rajasthan Jaipur303007 India School of Engineering and Technology Jagran Lakecity University Madhya Pradesh Bhopal462042 India Department of Computer Science and Engineering Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara391760 India

Scientific community understanding of the variance in severity of infectious disease like COVID-19 across patients is an important area of focus. The article presents an innovative voting ensemble GenoCare Prognosticator (GCP) model that incorporates XGBoost and Random Forest classifiers, two cutting-edge machine learning approaches. A large dataset that incorporates medical covariates like gender and age along with biological WES (Whole Exome Sequencing) data was used to train these models. Five-fold stratified cross-validation was used to process the dataset in order to improve model stability and avoid overfitting. Two medical covariates and sixteen recognized candidate gene variants were among the eighteen major features on which our GCP model had been verified using data from earlier studies. Specific post-hoc clarification of the model's predictions was provided by ExplainerDashboard, a Python open-source library, to improve interpretability. Furthermore, we utilized OpenTarget and Enrichr, two bioinformatic resources, to establish connections between the discovered variations in genetics and pertinent ontologies, biological pathways, and possible drug/disease relationships. Unsupervised clustering of SHAP key feature values was included in the analysis, which revealed intricate genetic interactions that affect the severity of the disease. Our results show that although gender and age are the main factors influencing the severity of COVID-19, complex genetic interactions cause severe symptoms in a specific subset of patients. This work contributes to our comprehension of the biological variables influencing the severity of COVID-19 and offers a reliable, comprehensible model that can help recognize patients at high risk and guide individualized treatment plans. © (2025), (West University of Timisoara). All rights reserved.

关键词： COVID-19

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Enhanced Vision Transformer with Lion Optimizer for Accurate Classification of Monkeypox Skin Images 4

Enhanced Vision Transformer with Lion Optimizer for Accurate...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Bhatnagar, Roheet Hassan, Esraa Saber, Abeer Shams, Mahmoud Y. Elbedwehy, Samar Manipal University Jaipur Department of Computer Science and Engineering Rajasthan India Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Machine Learning Kafrelsheikh33516 Egypt Damietta University Faculty of Computers and Artificial Intelligence Information Technology Dept. Damietta34517 Egypt Kafrelsheikh University Faculty of Artificial Intelligence Dept. of Data Science Kafrelsheikh33511 Egypt

ISBN: (纸本)9798350387490

Monkeypox is a contagious viral infection caused by the monkeypox virus (Mpoxvirus). Characterized by skin lesions, fever, respiratory difficulties, swollen lymph nodes, and neurological complications, MPX can be fatal. Recent outbreaks have spread the disease across Europe, Australia, the United States, and Africa. Traditionally diagnosed through PCR testing of skin lesions, this process poses significant risks to medical personnel due to potential exposure to the highly contagious virus. However, the usage artificial intelligence (AI) is revolutionizing diagnostics, providing a safer and more efficient approach to MPX detection. In this paper, a new AI technique based on vision transformer and lion optimizer for classifying Monkeypox Skin Images. The presented model used for classifying the dataset and results reached accuracy of 99.6%. © 2024 IEEE.

关键词： Classification Deep learning Diagnosis Monkeypox

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ARIMA Modeling for Reliable Potable Water Identification and Quality Prediction 5

ARIMA Modeling for Reliable Potable Water Identification and...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Selvaraj, P. Shalma, H. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telengana Hyderabad India

ISBN: (纸本)9798331504403

This work aims to employ machine learning methods and visualization techniques for evaluating water quality by measuring its potability, a numerical indicator of water quality. The work utilizes water quality parameters to determine the stability condition using pH, Temperature, Dissolved Oxygen, Conductivity, Nitrate, Fecal Coliform, and Total Coliform to evaluate the overall water quality. ARIMA is utilized to identify and categorize water potability based on its features. Using Tableau's interactive dashboarding, we created an interactive dashboard to visually simplify water potability metrics in India. The study utilized both real datasets from various locations around the country using government data sourced directly from the Central Pollution Control Board (CPCB). Based on the results of the experiments, the ARIMA was found to be superior to other classifiers in accurately predicting water potability. © 2024 IEEE.

关键词： Water pollution control

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Smart Finance: An overview of artificial intelligence Integration in Fintech 2

Smart Finance: An overview of Artificial Intelligence Integr...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Akre, Parth Dhananjay Pacharaney, Utkarsha Siraskar, Warsha Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Maharashtra Nagpur441110 India

ISBN: (纸本)9798331528713

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion. © 2024 IEEE.

关键词： Federated learning

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Optimizing Energy Efficiency and Network Performance in Smart Street Lighting Systems Using LoRaWAN technology 3

Optimizing Energy Efficiency and Network Performance in Smar...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sivakumar, S. Jayanth, R. Mughesh Kumar, N.R. Srm Institute of Science and Technology Dept. of Comp. Sci. and Engineering Specialization in Artificial Intelligence and Machine Learning Tamil Nadu Tiruchirapalli India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

This paper presents a groundbreaking Smart Street Lighting System utilizing LoRaWAN technology to overcome the challenges of scalability, energy inefficiency, and communication limitations inherent in older smart street technologies. The system employs seamless, long-range communication for real-time monitoring, adapting to dynamic urban environments, and achieving a balance between energy conservation and public safety. Advanced components, including a Passive Infrared (PIR) sensor and microwave motion sensor, enhance object detection and urban safety. A unique feature of the system is its ability to automatically dim the street lights to 1/4th intensity when no object is detected, while instantly adjusting to full brightness upon object detection. Furthermore, the incorporation of a response factor ensures accurate detection and swift response to communication disruptions. Notably, the system achieves an outstanding 70% energy savings through optimized lighting and dynamic remote control, significantly contributing to the evolution of intelligent and energy-efficient street lighting solutions in smart city infrastructures. © 2024 IEEE.

关键词： Smart city

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Revolutionizing Quality Control: A machine learning Approach for Precise Surface Defect Detection and Localization in Industrial Components

Revolutionizing Quality Control: A Machine Learning Approach...

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2024 IEEE International Conference on Emerging Technologies in Computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Ajina, A. Jaya Christiyan, K.G. M S Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India M S Ramaiah Institute of Technology Department of Mechanical Engineering Bangalore India

ISBN: (纸本)9798350372502

This paper focuses on the creation of a machine learning application designed to efficiently identify and categorize surface imperfections in a variety of components. Conventional methods, relying on visual examination or manually crafted characteristics are prone to inaccuracies and are time-intensive. Leveraging the capabilities of artificial intelligence architecture, we not only achieve precise defect classification but also accurate defect localization. In contrast to traditional approaches, artificial intelligence can adeptly learn optimal visual features in a supervised manner. In real image data set we performed our experiments which illustrate that our approach outperforms current methods while maintaining computational efficiency. We have developed a machine learning model for surface defect detection in steel flat surfaces through image segmentation. This advancement has resulted in reduced defects, heightened manufacturing accuracy, increased production rates and enhanced product quality. © 2024 IEEE.

关键词： Image segmentation

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Investigating Youtube's Educational Videos: Patterns, User Conduct and Suggestion Techniques

Investigating Youtube's Educational Videos: Patterns, User C...

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

作者： Sundar, Koushik Eugene Berna, I. Vijayakumar, R. Ambeth Kumar, V.D. Vijay, K. Citibank NA Jersey CityNJ United States Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Design Chennai India Mizoram University Department of Computer Engineering Mizoram Aizawl India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331522988

This research voyage drew us far into the world of instructional videos on YouTube, a platform that promotes learning and discovery. Our goal was to identify patterns and trends in instructional films so that we might improve the user experience by suggesting better content. We conducted an analysis of data that we directly gathered from YouTube using certain technologies. The varied lifespans of trending videos across nations were one fascinating discovery. Certain videos were more well-known for a longer period of time than others. We were captivated by the cultural diversity found in video lifespans. We also looked at likes, dislikes, views, and comments to find robust user involvement worldwide. Through engagement data, different countries demonstrated their preferences for expressing oneself. It was discovered that a video's popularity was greatly influenced by the timing of the content's release. Content producers can stay relevant by keeping in mind this correlation. Sentiment analysis also uncovered emotional overtones connected to video tags, offering perceptions into what appeals to viewers. We also identified the optimal time, based on user involvement and behavior, to watch instructional videos. Ultimately, we used recommendation algorithms to tailor the YouTube learning experience by fusing content-based and collaborative filtering. All things considered, clarifies the complex world of instructional content and offers advice to users and producers on this dynamic platform. Our research will continue to influence the direction of more interesting instructional content as YouTube develops. © 2024 IEEE.

关键词： Video analysis

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Sensor Based Smart Wearable Devices for Women: A Review 2

Sensor Based Smart Wearable Devices for Women: A Review

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

作者： Khadse, Chinmaya Hajare, Shital Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs. © 2025 IEEE.

关键词： mHealth

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A Comprehensive Study on Parkinson's Disease Detection Through machine learning Techniques

A Comprehensive Study on Parkinson's Disease Detection Throu...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Burle, Rushikesh Gaurkhede, Sanskruti Fulkar, Bhushan Gundewar, Swapnil Pacharaney, Utkarsha Verma, Prateek Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798350375237

One of the biggest issues with worldwide public health is Parkinson's disease (PD). It is usually known that about are around one million Americans who have Parkinson's disease, whereas there are approximately five million cases of the condition globally. As a result, it's critical to identify Parkinson's disease early on and make the appropriate treatment plans. These days, machine learning is essential for analysing problems in real time and offering answers thanks to its widely used algorithms. These days, machine learning algorithms are used in the healthcare industry to identify patients' health problems. This research went into great detail about the methods used using algorithms that use machine learning to classify Parkinson's disease. A fault in the brain cells that generate dopamine, a necessary chemical for synapses to connect with one another, is the cause of Parkinson's disease. The brain's dopamine-producing neurons are in charge of control, flexibility, and event familiarity. When 60-80% of these cells disappear, inadequate dopamine is produced, which causes Parkinson's disease symptoms to manifest. In this case, the Random Forest, Decision Tree, K-Nearest Neighbours, and Support Vector machine techniques were used to identify the illness. Compared to Decision Tree and Support Vector machine, Random Forest and K-Nearest Neighbours offer the best performance. © 2024 IEEE.

关键词： Support vector machines

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