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Flood Susceptibility Assessment in the Teesta River Basin, India: An Advanced Geospatial Artificial Intelligence Approach Leveraging Spatial data Augmentation

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SN computer science 2025年第4期6卷 1-22页

作者： Sen, Deepanjan Das, Swarup Dept. of Computer Applications Dr. B. C. Roy Academy of Professional Courses West Bengal Durgapur India Dept. of Computer Science and Technology University of North Bengal West Bengal Siliguri India

Floods are a critical natural catastrophe that has a significant impact on the Teesta River Basin in India, resulting in substantial losses to agriculture, property, and life. This investigation introduces a sophisticated geospatial artificial intelligence framework for the evaluation of flood susceptibility, which employs spatial data augmentation for enhancing models’ reliability. The study uses six machine learning algorithms, involving K-Nearest Neighbours (KNN), Na¨ıve Bayes (NB), Decision Tree (DT), Conditional Inference Tree (CIT), BlackBoost (BBoost), and Model Averaged Neural Network (MANN), to integrate multisource geospatial data, including 30 × 30 m Cartosat-1 satellite derived Digital Elevation Model (DEM). A thorough flood inventory found 317 inundated areas, with original data sources systematically enhanced at a 1:3 ratio to expand the dataset, and the Boruta feature selection technique identified 12 essential flood conditioning elements. The models were assessed using metrics such as accuracy, Kappa, precision, recall, F1 score, and Area Under the Curve (AUC). The KNN model has the maximum accuracy of 91.62% (Kappa = 0.8325), precision of 90.41%, recall of 93.12%, and AUC of 97.80%, classifying around 35% of the basin as high-susceptibility zones. This study underscores the efficacy of machine learning and geospatial analysis in enhancing flood risk management tactics, offering critical insights for disaster management policymakers. Future efforts will concentrate on improving model robustness via comprehensive evaluation across many geographical settings, incorporating real-time meteorological data for dynamic flood forecasting, and creating user-friendly decision support tools for stakehol-ders in disaster management. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Boruta analysis Digital elevation model Flood susceptibility Geospatial artificial intelligence Spatial data augmentation

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Leveraging Artificial Intelligence, Internet of Things, and Digital Twins in Sustainable Agriculture

Leveraging Artificial Intelligence, Internet of Things, and ...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Thakkar, Bhavika Shaikh, Shaina Katkade, Pratiksha Shukre, Varsha Atul Dr. Vishwanath Karad MIT World Peace University Dept. of Computer Science and Applications Pune India

ISBN: (纸本)9798350385199

Sustainable agriculture plays a key role in tackling worldwide food security issues and reducing environmental harm. Recent progress in the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin technologies has demonstrated significant potential in transforming agricultural methods to be more sustainable. This study provides an in-dept. analysis of how AI, IoT, and digital twins are being incorporated into sustainable agriculture, with a focus on their opportunities, obstacles, and future prospects. IoT sensors and devices play a significant role in enabling real-time data collection, analysis, and decision-making within agricultural operations. This capability allows for precise monitoring of crop growth, soil conditions, weather patterns, and resource usage, ultimately leading to optimized resource management and increased productivity while minimizing environmental impact. Additionally, digital twins, which are virtual representations of physical agricultural systems, provide opportunities for simulation, experimentation, and optimization of farming practices in a virtual setting. The article explores the diverse applications of IoT, AI, and digital twins in sustainable agriculture, such as precision farming, smart irrigation, pest and disease management, predictive analytics, and supply chain optimization. Through the examination of case studies, the paper showcases how these technologies effectively enhance crop yield, resource efficiency, and overall sustainability metrics. © 2024 IEEE.

关键词： Smart agriculture

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Role of IoT in Smart Cities: A Review, applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

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Decision Support System for Crop Management using Convolutional Neural Network 3

Decision Support System for Crop Management using Convolutio...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Immaculate Mercy, A. Ruby, D. Logesh, T. Shahidha Shireen, S. Periyar Maniammai Institute of Science & Technology Dept. of Computer Applications Tamilnadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Effective crop management is critical for enhancing agricultural productivity and sustainability. Traditional farming methods often face challenges in accurately forecasting yields, optimizing resources, and determining the right harvest timing. To address these challenges, this study develops a decision support system for rice farming in South India by utilizing deep learning techniques, specifically convolutional neural networks (CNN s), combined with dimensionality reduction methods. The system uses a comprehensive dataset that includes factors such as rainfall, temperature, soil type, and rice variety to predict rice yield. Additionally, it optimizes the use of resources such as water, fertilizer, and labor on the basis of expected yields. The proposed CNN model, coupled with squared exponential kernel PCA (SEKPCA), achieved a prediction accuracy of 83.12% and an F1 score of 0.63, enabling farmers to make informed decisions and improve productivity. The system also evaluates resource requirements, offering insights into water usage, fertilizer, and labor needs. This approach enhances agricultural efficiency, reduces waste, and promotes sustainable farming practices. By integrating advanced deep learning technologies into traditional agriculture, the system contributes to more precise yield predictions and better resource management, paving the way for improved crop management and sustainability in rice farming in South India. © 2025 IEEE.

关键词： Resource allocation

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Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting 14

Evaluating the Effectiveness of a Facial Recognition-Based A...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nijgal, Dhamini R George, Stebin Subramanian, Parameswaran Christ University Dept. of Computer Science Bangalore India Christ University Dept. of Data Science Bangalore India

ISBN: (纸本)9798350335095

Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. © 2023 IEEE.

关键词： Face recognition

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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Exploring the Integration of Generative AI in Modern Education Systems: A Comprehensive Analysis

Exploring the Integration of Generative AI in Modern Educati...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Dey, Avick Kumar Chauhan, Vineet Kumar Singh, Pradeep Kumar Sarkar, Ananya Dept. of Computer Applications West Bengal Durgapur India Galgotias College of Engg. & Tech Dept. of Information Technology Greater Noida India School of Computer Science and Engineering Galgotias University Greater Noida India DSMS College of Tourism and Management Dept. of Computer Applications West Bengal India

ISBN: (纸本)9798331517953

This paper aims at filling this gap through dissecting the applicability of the Generative AI in the current learning and teaching system. The paper offers an introduction to Generative AI technologies: GPT and DALL-E, and describes the chronological history and current progress of its educational applications. Other objectives include discussing about the uses of Generative AI in adaptive learning environment, smart learning systems, automation of organizational work and in talented minds improvement. Analyzing key successful AI cases, the paper reveals the challenges implementers encountered, and provides debates about the appropriate practices of AI. Research evidence points to the prospect of Generative AI benefiting students and educators in teaching and learning, which indicates the innovative potential of the technology, yet at the same time reveals the potential risks and challenges that need to be addressed concerning the ethical and privacy implications of Generative AI in education. Others may follow general recommendations for policymakers and educators on how to integrate artificial intelligence responsibly, as well as future work for this quickly burgeoning domain. © 2024 IEEE.

关键词： Contrastive Learning

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Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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An Architecture for Dynamic Load Balancing in Cloud Environment 2

An Architecture for Dynamic Load Balancing in Cloud Environm...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Lokesh, Gudivada Baseer, K.K. Dept. of Computer Science and Engineering Tirupati India Department of Data Science Tirupati India

ISBN: (纸本)9798350347579

Clouds are highly customizable infrastructures that offer a platform as a service and let customers subscribe on a pay-as-you-go basis to their requirements. The straightforward service-oriented cloud computing model is gaining popularity around the world. The number of people using the Cloud is constantly growing. Clouds use modern data centers to manage a massive number of users. The reliability of the Cloud depends on load balancing. Balancing virtual machine loads lowers energy consumption and task rejections by optimizing resource utilization. One can increase performance while using fewer resources using load balancing, resource management, quality of service, etc. The difficulty of overloading and underloading virtual machines in cloud computing can be lessened by load balancing in the *** research study thoroughly examined the load-balancing algorithms found in the literature. First, the traditional approaches are analyzed before moving on to more recent work on load balancing with heterogeneous techniques. Along with the tools available for the current investigation, various metrics are used to evaluate the load-balancing algorithms. The proposed article will primarily serve to assist in the development of new algorithms in the future. © 2023 IEEE.

关键词： Energy utilization

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Enhancing Software Effort Estimation with Ant Colony Optimization Algorithm and Fuzzy-Neural Networks 3

Enhancing Software Effort Estimation with Ant Colony Optimiz...

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3rd International Conference on Distributed Computing and High Performance Computing, DCHPC 2024

作者： Afshari, Mahnaz Gandomani, Taghi Javdani Shahrekord University Data Science Research Group Shahrekord Iran Shahrekord University Dept. Computer Science Shahrekord Iran

ISBN: (纸本)9798350381580

Software effort estimation remains a persistent challenge and requires serious attention in the early stages of software project management. Inherent uncertainties arising from incomplete and inaccurate requirements pose a significant barrier to reliable estimations. Despite numerous efforts and various techniques proposed for cost estimation, the pursuit of improving estimation accuracy remains essential. In response to this challenge, this study introduces a novel model that integrates an Adaptive Neuro-Fuzzy Inference System with the Ant Colony Optimization algorithm. The model is further compared with well-known evolutionary algorithms such as Differential Evolution, Genetic Algorithm, Artificial Neural Network, and Particle Swarm Optimization. Applying the proposed model to popular software effort estimation datasets, including Albrecht, Desharnais, and Kemerer, demonstrates its superiority over the mentioned algorithms. The improved estimation provided by this model can assist software project managers in better project cost estimation. © 2024 IEEE.

关键词： Fuzzy neural networks

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