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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Blaise, Don Bosco Manoj Kumar, G. Jeswanth, S. Bhuvaneswari, G. Rajesh, C. Bharathi, A. Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Skin cancer is a highly prevalent form of cancer that frequently arises from the proliferation of abnormal cells on the skin. Early detection is crucial for effective treatment and better prognosis. Traditional diagnostic methods face challenges in accuracy and timeliness, making automated techniques a valuable tool in disease identification. This article proposes a deep learning framework based on an ensemble, aimed at enhancing the detection and classification of various types of cancer on the skin. The approach integrates four optimized Convolutional Neural Networks (CNNs) using a weighted average ensemble technique. This model addresses common challenges in data preparation, model selection, and overfitting through comprehensive preprocessing and hyperparameter tuning, resulting in improved accuracy and robustness compared to existing systems. © 2025 IEEE.

关键词： Convolutional neural networks

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AI-Powered Mental Health Screening and Support for Homeless Children

AI-Powered Mental Health Screening and Support for Homeless ...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Raga Madhuri, C.H. Bandaru, Jaya Sankar Krishna Srinu, Medisetti Vardhan, Gangadhari Midhun Anand Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Department of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798331536336

Mental health illness is a significant global public health threat exacerbated by the lack of effective early identification and intervention measures. This project aims to address these challenges by focusing on mental health recognition among homeless children. Individuals who are particularly vulnerable due to the loss of their parents. We created a model that screens mental health status by analyzing the responses to screening pediatric symptoms checklist (PSC) questions using an SVM algorithm. This model achieved 90% accuracy in predicting the mental health status of children. This approach makes it more accessible and effective for caretakers in orphan homes. The idea of providing some clear guidelines from the obtained results would make sure these children are well taken care of at the right time, which ultimately improves the mental health status of this group of people. © 2025 IEEE.

关键词： Pediatrics

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Advancing Precision Agriculture: Machine Learning-Enhanced GPR Analysis for Root-Zone Soil Moisture Assessment in Mega Farms

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 98-109页

作者： Namdari, Himan Moradikia, Majid Zekavat, Seyed Askari, Radwin Mangoubi, Oren Petkie, Doug Data Science Program WorcesterMA01609 United States Michigan Technological University Department of Geological and Mining Engineering and Sciences HoughtonMI48105 United States Physics Department WorcesterMA01609 United States

In this article, we investigate an intelligent ground penetrating radar (GPR) that facilitates root-zone soil moisture estimation, a key parameter in precision agriculture. To create an intelligent GPR, we must train machine learning (ML) methods applied to the GPR-received signal. This process requires a large number of labeled GPR data that would be time-consuming and labor-intensive if created via field measurements. This article uses gprMAX software to emulate drone-coupled GPR received signal to generate large-scale data for training ML models. The data are created via a 1.5 GHz Ricker waveform considering a three-layer soil consistent with a realistic soil horizon model. The approach is structured as follows: first, we generate a synthetic dataset using gprMAX. Feature engineering techniques are then employed to extract meaningful components from the GPR signals, followed by a rigorous selection process to identify the most effective ML model for soil moisture prediction. Finally, we validate our model by integrating synthetic data with real GPR data collected at the SoilX lab at Worcester Polytechnic Institute, enhancing prediction accuracy and generalization capability. Our proposed model achieves an overall average root-mean-squared error of 0.5%, and 1.56 cm for moisture and depth estimations, respectively. The proposed intelligent GPR, when installed on a drone, enables high horizontal (e.g., 10 m) and vertical (e.g., 1.5 cm) resolution and high penetration depth (beyond 2 m) megafarm root-zone 3-D moisture map creation. Thus, it offers much higher capabilities when compared to traditional methods, such as synthetic aperture radar and satellite imaging. These results facilitate efficient farming practices, such as optimizing irrigation models, for better crop yields and food security. © 2023 IEEE.

关键词： Soil moisture

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Spatio-Temporal NDVI Prediction for Rice Crop

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SN computer science 2025年第3期6卷 1-13页

作者： Dey, Anamika Sarkar, Somrita Mondal, Arijit Mitra, Pabitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Bihta 801106 India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India

Accurately predicting the Normalized Difference Vegetation Index (NDVI) is crucial for effective agricultural planning and decision-making. Despite much literature on NDVI prediction, most of these methods do not consider spatial-temporal variations. Generating a dataset for predicting NDVI is another major bottleneck, as such data needs to be collected over an extensive period. In this work, we develop a semi-automated framework to generate a comprehensive time-series dataset after necessary preprocessing using the information from Sentinel-2, POWER data Access Viewer, and the Google Earth Engine (GEE). We propose a deep learning-based model to predict NDVI based on its historical time series and meteorological and spatial information for the rice crop in India. Here, we use a Long Short-Term Memory (LSTM)-based model augmented with spatial information to predict the NDVI of the near-about region for which the data is unavailable. We compared the proposed approach with a method that uses a weighted average of nearest neighbors prediction. Also, we compare the proposed method with existing statistical techniques such as the Autoregressive (AR), AutoRegressive Integrated Moving Average (ARIMA), and Seasonal AutoRegressive Integrated Moving Average (SARIMA). Experimental results show that our methodology outperforms other techniques by a margin of 4% to 8%. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autoregressive integrated moving average Long short-term memory Normalized difference vegetation index Seasonal autoregressive integrated moving average Sentinel-2 Weighted average of nearest neighbors prediction

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Predictive Cardiovascular Health Assessment with Flask and Machine Learning Models

Predictive Cardiovascular Health Assessment with Flask and M...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Mahesh, Kurma Aishwarya, Paladi Nithin, Sabavath Hariharan, Shanmugasundaram Vaishnavi, Bandar Vasantha, S.V. Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331527495

Cardiovascular Health Forecaster Employing Flask is a cutting-edge online application that combines Python for its backend and Flask for its frontend. It anticipates heart disease risk with a smooth integration of Flask for data input and Python for machine learning, making it perfect for both users and healthcare professionals. As heart attacks take one life every minute, prediction automation becomes essential. Information science handles large amounts of data in healthcare, tackling the difficulty of predicting coronary heart disease. making use of a dataset on cardiac illness from the UCI AI repository. The AI algorithms-Light GBM, AdaBoost, Gradient Boosting, XG Boost, and Random Forests-are used in this study to predict and classify the risk of cardiovascular illness. The study shows Light GBM's remarkable 90% accuracy, making it a relative report on algorithm performance. © 2025 IEEE.

关键词： Cardiology

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Digital Twin-based Dynamic Resource Provisioning Using Deep Q-Network on 6G-enabled Mobile Edge Networks 17

Digital Twin-based Dynamic Resource Provisioning Using Deep ...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Alex, Suja A. Singh, Neha Adhikari, Mainak St. Xavier's Catholic College of Engineering Department of Information Technology Nagercoil India Indian Institute of Information Technology Lucknow Department of Computer Science Lucknow India Indian Institute of Science Education and Research Thiruvananthapuram School of Data Science Thiruvananthapuram India

ISBN: (纸本)9798331531195

The advancement of Digital Twin (DT) technology has enabled the creation of digital replicas of physical entitics, significantly enhancing the functionality and performance of mobile networks. However, this innovative approach also introduces considerable overload and performance challenges for mobile edge networks. To address these issucs, we propose a novel DT-enabled edge architecture for 6G-capable technology, employing a dynamic resource allocation strategy. The proposed approach, termed DT-based Dynamic Resource Provisioning (DT-DRP), leverages the capabilities of DT technology and Decp Reinforcement Learning (DRL) to dynamically allocate incoming tasks from mobile devices to edge devices. An intelligent DT server facilitates this allocation, optimizing resource use and improving network performance. The predictive capabilities of the framework are validated using the K-Nearest Neighbor (KNN) dlassifier at the edge network. Experimental results demonstrate that the proposed DT-DRP framework incorporates mobile devices into the DT network scamlessly, achieving superior performance in terms of minimum latency and high accuracy compared to bascline Q-learning algorithms across two medical datasets and various exccution scenarios. This research underscores the potential of DT technology in 6G networks and highlights its efficacy in enhancing mobile edge computing performance through intelligent resource management. © 2025 IEEE.

关键词： Mobile edge computing

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A Transfer Learning based Deep Learning Framework for Lung Disease Detection Utilizing CXR Images

A Transfer Learning based Deep Learning Framework for Lung D...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kansal, Kajal Kansal, Kanika School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Abes Engineering College Department of Computer Science and Engineering - Data Science Uttar Pradesh Ghaziabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527495

The classification of various lung diseases with the help of chest X-ray images is one of the significant and challenging issues in medical imaging applications. This work proposes a transfer learning-based deep neural network architecture to classify lung diseases using a publicly available dataset. The proposed DenseNet201 model uses the interconnected structure to optimize feature transmission, minimize computation redundancy, and improve the gradient flow. The proposed model achieved a better classification accuracy of 98.34% compared to the other existing methods. These results show that DenseNet201 effectively learns discriminative features from the input medical image data. The proposed framework is suitable for large-scale implementation of lung disease classification. It helps develop more efficient computer-aided diagnostic systems, assisting clinicians in decision-making. © 2025 IEEE.

关键词： Lung cancer

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in Engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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