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Proposing a digital twin-based sustainable water governance system for rural Indian villages

International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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ADNeuroNet: a neuroevolution-based neural network algorithm for the diagnosis of neurodegenerative diseases

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Neural Computing and applications 2025年第14期37卷 8593-8627页

作者： Khan, Afreen Zubair, Swaleha Ali, Irfan Department of Computer Application Faculty of Engineering & IT Integral University Lucknow226026 India Department of Statistics and Operations Research Aligarh Muslim University Aligarh India Department of Computer Science Aligarh Muslim University Aligarh India

Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Disease control

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Machine Learning-Based Methods for Materials Inverse Design: A Review

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computers, Materials & Continua 2025年第2期82卷 1463-1492页

作者： Yingli Liu Yuting Cui Haihe Zhou Sheng Lei Haibin Yuan Tao Shen Jiancheng Yin Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650093China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and TechnologyKunming650500China Yunnan Tin Company Limited Tin Industry Branch Gejiu661000China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming650093China

Finding materials with specific properties is a hot topic in materials *** materials design relies on empirical and trial-and-error methods,requiring extensive experiments and time,resulting in high *** the development of physics,statistics,computer science,and other fields,machine learning offers opportunities for systematically discovering new *** through machine learning-based inverse design,machine learning algorithms analyze the mapping relationships between materials and their properties to find materials with desired *** paper first outlines the basic concepts of materials inverse design and the challenges faced by machine learning-based approaches to materials inverse ***,three main inverse design methods—exploration-based,model-based,and optimization-based—are analyzed in the context of different application ***,the applications of inverse design methods in alloys,optical materials,and acoustic materials are elaborated on,and the prospects for materials inverse design are *** authors hope to accelerate the discovery of new materials and provide new possibilities for advancing materials science and innovative design methods.

关键词： Materials inverse design machine learning target properties deep learning new materials discovery

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A Machine Learning Approach to GDP Prediction by Analyzing Economic Indicators 2

A Machine Learning Approach to GDP Prediction by Analyzing E...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Thilaka, A. Sundaravalli, E. SRMIST Faculty of Science and Humanities Department of Computer Application Chennai India Thavathiru Santhalinga Adigalar Arts and Science College Coimbatore India

ISBN: (纸本)9798350349221

A country's economy is important for its growth and development. A well-performed economy describes the quality living of the citizens. For assessing the health of nation's economy the GDP performs a crucial benchmark. The country's finished goods total value and services in a particular period are called GDP. The GDP value of the country tells whether that country is developed country or developing country. If the GDP grows the common man of the country earns the quality lifestyle. It makes the country to generate more tax revenue. This paper deals with the GDP prediction of the country by creating the machine learning model in python jupyter notebook. The machine learning model predicts the GDP based on the Life Expectancy, Human Development Index (HDI), CO2 emissions per person and percentage of service workers in the country. It uses the gapminder dataset from the Kaggle repository. Diverse machine learning algorithms are employed in the process of construction of a machine learning model. We picked the algorithm by analyzing the structure of dataset that have taken for the model building. Given that the dataset is in the form of numerical values and arranged in a non-linear structure, the non-linear regression algorithms were to implement this system. The Average Absolute Error (AAE), Squared Error (SE) and R-squared (R2) metrics were taken to validate the accuracy of the system. Among the three models that were implemented and tested Decision Tree Regression model (DTR), Support Vector Regression model (SVR) and Polynomial Regression model (PR), the Decision Tree Regression model achieved the error Free State and R-squared value of 1 which is the most desirable model. © 2024 IEEE.

关键词： Decision trees

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Multispectral point cloud superpoint segmentation

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science China(Technological sciences) 2024年第4期67卷 1270-1281页

作者： WANG QingWang WANG MingYe ZHANG ZiFeng SONG Jian ZENG Kai SHEN Tao GU YanFeng Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Computer Technologies Application Kunming 650500China School of Electronics and Information Engineering Harbin Institute of TechnologyHarbin 150001China

The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

关键词： multispectral point cloud superpoint segmentation over-segmentation spatial-spectral joint metric

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Optimized Heuristic Technique for Task Scheduling in Secure Cloud Storage Environment 13

Optimized Heuristic Technique for Task Scheduling in Secure ...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Maheshwari, S. Ruth, J. Anitha A.M. Jain College Srmist Department of Computer Science Vadapalani Chennai India Faculty of Science and Humanities Department of Master of Computer Application Vadapalani Chennai India

ISBN: (纸本)9798350380583

Cloud computing is a highly scalable and frequently used expertise in today's rapidly evolving information and computer landscape. It runs on the payper-use, remote access, Internet-based, and on demand ideas, offering consumers a shared pool of programmable resources. However, as the number of user requests grows, efficient task scheduling and resource allocation have become critical criteria for effective workload balancing among cloud resources. It consequently improves overall cloud system performance and offers a new way to deliver services in distributed systems, paving the way for the execution of scientific applications. Scheduling and processing millions of activities using Cloud resources in a lucrative manner with minimal computing time is an attractive study area. Current scheduling algorithms ignore dependent and independent jobs. To overcome this drawback, we anticipate a new dignified technique called the Optimized heuristic Min-Min algorithm, which is stimulated by the Min-Min algorithm for task scheduling and focuses on the concern of dependent and independent tasks, as well as the parallel processing of independent tasks, which directly results in profit by minimizing computation time. This study aims to produce optimal results by minimizing completion time and maximizing resource utilization, leading to higher throughput. Kerberos authentication service is implementing for cloud data storage security. Kerberos used in this work for creating tickets and granting them to every user in order to provide high security. © 2024 IEEE.

关键词： Cloud storage

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A machine learning approach for skin lesion classification on iOS: implementing and optimizing a convolutional transfer learning model with Create ML

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International Journal of computers and applications 2024年第8期46卷 666-685页

作者： Benedek Szabo, Aron Katona, Jozsef Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Instrumentation and Automation Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Informatics GAMF Faculty of Engineering and Computer Science Kecskemét Hungary

The integration of machine learning (ML) into mobile applications presents unique challenges, particularly in resource-constrained environments such as iOS devices. Skin lesion classification is a critical task in dermatology, where accurate and efficient diagnostic tools can significantly aid in early detection of malignant lesions. This study aims to implement a machine learning-based iOS mobile application and develop a binary classification model for skin lesion images to determine whether a lesion is malignant. The research utilized Create ML to develop a convolutional neural network optimized for iOS, employing a transfer learning approach. A logistic regression model was cascaded with the convolutional neural network N to enhance classification accuracy. The model's performance was assessed through various validation metrics, ensuring its robustness and efficiency within the constraints of mobile hardware. A curated dataset from the International Skin Imaging Collaboration archive was used for training and testing. The model achieved an accuracy of 92%, a precision of 90%, a recall of 93%, and an F1-score of 91% in classifying skin lesions. These metrics validate the model's efficacy in identifying malignant lesions. Data curation involved collecting, labeling, and preparing a dataset from publicly available sources, ensuring the inclusion of diagnostically relevant features. The final model was integrated into an iOS application using Core ML and Vision frameworks. The developed application demonstrates reliable performance in classifying skin lesions with high accuracy on iOS devices. The inclusion of comprehensive performance metrics justifies the efficacy of the proposed approach. Future work will explore enhancements in model architecture and object detection capabilities to further improve diagnostic precision and application usability. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Image recognition

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The Role of Machine Learning in Optimizing Industrial Communication Protocols

The Role of Machine Learning in Optimizing Industrial Commun...

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2024 International Conference on Advances in Computing Research on science Engineering and Technology, ACROSET 2024

作者： Kharbas, Vikas Kumar Mahesh, T.R. Upadhyay, Satish Vivekananda Global University Department of Computer Science & Application Jaipur India Faculty of Engineering and Technology Department of Computer Science Engineering Bangalore India Atlas SkillTech University Department of UGDX Mumbai India

ISBN: (纸本)9798350388800

Due to the complexity and urgency of information exchange in modern companies, optimizing communication protocols in the workplace is essential. Using methods from machine learning, this research takes a fresh tack toward optimizing the performance of communication protocols. The suggested approach integrates Reinforcement Learning (RL), Convolutional Neural Networks (CNNs), and Genetic Algorithms (GAs) to provide a self-optimizing system with dynamic behavior. The RL feature facilitates this protocol's flexibility in the face of dynamic network circumstances by providing rapid feedback. CNNs are used for real-time anomaly detection to improve safety and dependability. GAs methodically explores combinations of protocol parameters to determine the optimal collection that matches specified performance criteria. The recommended technique was compared to the usual methodology using a variety of efficiency and reliability measures. The findings demonstrate that the proposed approach frequently outperforms the state-of-the-art methods in a broad range of commercial settings with regards to efficiency, dependability, and adaptability. Understanding how to enhance industrial communication protocols is crucial in the age of Industry 4.0 and the IIoT. The recommended solution offers an autonomous, adaptable, and secure tool for boosting communication in dynamic production situations. © 2024 IEEE.

关键词： Convolutional neural networks

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Image-Based Sentiment Analysis Using InceptionV3 Transfer Learning Approach

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SN computer science 2023年第3期4卷 242页

作者： Meena, Gaurav Mohbey, Krishna Kumar Kumar, Sunil Chawda, Rahul Kumar Gaikwad, Sandeep V. Department of Computer Science Central University of Rajasthan Ajmer India Department of Computer Science Assam University Silchar India School of Business Woxsen University Hyderabad India Faculty of Computer Science and Application SMPICA Charotar University of Science and Technology Gujrat Anand India

There has been a recent shift from using text-based sentiment analysis in favor of an image-based method. In recent years, transfer learning methods have been widely utilized in developing a comprehensive image sentiment analysis approach. Deep learning algorithms have produced remarkable outcomes in a variety of contexts. Image-based sentiment analysis presents many difficulties, but there also appears to be much space for development. A significant improvement over prior work is provided by an InceptionV3 approach that can easily focus on huge body portions like a human face. This research improves image categorization performance with InceptionV3, a popular deep convolutional neural network, and other deep features. Using a Convolutional Neural Network based on InceptionV3 architecture, we identify and classify emotions using the famous CK + , FER2013, and JAFFE datasets. Experiments reveal that the proposed model achieves 99.5% accuracy on the CK + dataset. Also, the accuracies of JAFFE and FER2013 are 86% and 73%, respectively. A person’s emotional patterns and mental health can be evaluated using this approach. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial Intelligence Deep learning Image sentiment analysis InceptionV3 Transfer learning

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Maximizing Wheat Crop Productivity with Intelligent Land Analysis and IoT

Maximizing Wheat Crop Productivity with Intelligent Land Ana...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Kamatchi, C. Bala Muthukumaravel, A. Bharath Institute of Higher Education and Research Department of Computer Science Application Tamilnadu Chennai India Bharath Institute of Higher Education and Research Faculty of Arts and Science Tamilnadu Chennai India

ISBN: (纸本)9798350309140

Agricultural productivity plays an essential role in global food security, with a special focus on increasing the production of important crops such as wheat. To meet the requirement, the study proposes a novel technique that combines an intelligent land analysis framework with IoT (Internet of Things) technology. The growing global population and the limitations of traditional farming methods in meeting expanding demand create a pressing requirement for such a model. Existing methods tend to be limited by a lack of real-time data, resulting in inefficient resource usage and yield losses. The proposed method, on the other hand, merges soil sensors, drones, and satellite images to provide farmers with real-time information on soil moisture, levels of nutrients, and the health of crops. It employs data analytics and machine learning algorithms to provide personalized irrigation, fertilization, and pest control recommendations, allowing for accurate and timely interventions. Field experiments reveal a 20% increase in wheat yield, 15% reduction in water use and 18% reduction in fertilizer usage, demonstrating the effectiveness of the method compared with the existing approach in increasing productivity while saving resources and avoiding environmental impacts. © 2024 IEEE.

关键词： Smart agriculture

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