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Geotechnical Frontiers 2025: Geotechnics of Natural Hazards

作者： Agarwal, Ekansh Zhang, Xiong Luo, Ning Dept. of Engineering College of Engineering and Computer Science Texas A&M Univ.–Corpus Christi Corpus ChristiTX United States Dept. of Civil Architectural and Environmental Engineering Missouri Univ. of Science and Technology RollaMO United States

ISBN: (数字)9780784485996

This study focuses on modeling a rigid pavement slab on expansive soil in extreme climates to understand the interaction between weather, pavement, and soil. Lightweight superstructures such as pavements are particularly prone to damage due to the differential settlement of expansive soils. In the context of climate change, the occurrence of severe droughts followed by intense rainfall has risen, causing severe impacts on crack initiation and development, and thus infiltration patterns in the structures built on expansive soils. Pavements are typically designed for a limited lifespan, and early failure may occur if climate change is not considered during design. This is especially important in coastal areas when coastal flooding becomes more frequent. The interplay between climates, soils, and pavements leads to a spatial and temporal change in soil moisture and even soil structure, causing significant variations of volume change. Despite the dominant impact of expansive soils on pavement systems, a deep understanding of their role in pavement structural behavior remains challenging to both the researchers and the practitioners. This paper addresses this gap through a comprehensive numerical study using two-dimensional (2D) finite element modeling. We adopted the unsaturated soil constitutive relationship to understand the stress changes during the expansion and shrinkage of expansive soil. We employed four user-defined subroutines in ABAQUS to perform a coupled hydro-mechanical stress analysis. The study uses historical climate events as a reference to assess the impact of climate events on pavements. Mechanical, hydraulic, and soil-vegetation-atmosphere (SVA) boundary conditions are considered. The numerical findings highlight the vulnerability of pavements founded on expansive soils to climate change-induced events and indicate that a coupled hydro-climate-geotechnical analysis is necessary. Unprecedented climatic events lead to more uneven settling of pavements

关键词： Subroutines

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Improved Sampling Methods for Evaluation of Classification Performance

Improved Sampling Methods for Evaluation of Classification P...

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International Conference on Artificial Intelligence in information and Communication (ICAIIC)

作者： Yue-Shi Lee Show-Jane Yen Yi-Jie Tang Dept. of Computer Science & Information Engineering Ming Chuan University Taoyuan Taiwan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

To evaluate the classification accuracy of a classification algorithm on a certain dataset, it is necessary to sample training and test data from the original dataset. After building the classification model from the training data, the test data can be used to evaluate the classification accuracy. However, in order to evaluate the classification accuracy of a classification algorithm on an original dataset, it is usually necessary to sample the training/test data many times, build the classification model many times, and then average the accuracies of all the evaluation, which will consume a lot of time and resources. To address this, we propose a sampling method so that the sampled training/test data can replace the results of building multiple models and averaging multiple classification accuracy, that is close to the evaluation results of the original data. Our approach introduces different methods for calculating the similarity of data distributions and incorporates feature weights in the similarity calculation process to select training/test sets that are close to the distribution of the original dataset.

关键词： Training Earth Accuracy Buildings Training data Sampling methods Data models Classification algorithms Artificial intelligence Testing

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Adaptive Privacy and Lightweight Security in Healthcare IoT: Challenges, Solutions and Future Directions

Adaptive Privacy and Lightweight Security in Healthcare IoT:...

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Innovation in Computing and engineering (ICE), International Conference on

作者： Mukul Chauhan Tayyab Khan Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

IoT in healthcare has transformed patient care by enabling and implementing the real-time monitoring for the end patients or users. However, the development of connected devices introduces significant privacy and security challenges, especially while securing the healthcare data. In this paper, a comparative review of adaptive privacy and lightweight security mechanisms in the area of healthcare IoT is shown. A comparison of current methods based on criteria including data security, privacy preservation, scalability, resource efficiency, and real-time implementation is one of the major contributions. Furthermore, this review recognizes the research gaps in the recent researches and highlights promising techniques, including advanced encryption, federated learning, and the edge computing. By addressing these challenges, the study aims to guide the development of robust, efficient, and scalable solutions that ensure privacy and security in dynamic healthcare IoT environments.

关键词： Privacy Data privacy Technological innovation Reviews Scalability Medical services Real-time systems Encryption Security Edge computing

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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Petricia, V. Angel Jacksy, M. Bhavadharani, R. St. Joseph's Institute of Technology Dept. of Computer Science and Engineering Chennai119 India

ISBN: (纸本)9798331521394

Autism Spectrum Disorder(ASD) is a complex neurodevelopmental condition marked by challenges in social interaction, communication, and repetitive behaviors. Early detection is critical to enable timely intervention and improve outcomes for individuals with ASD. This research proposes an innovative framework that integrates Convolutional Neural Networks (CNNs) with advanced data preprocessing techniques, facial imaging, and behavioral analysis to enhance the accuracy and efficiency of ASD diagnosis. Leveraging data augmentation and transfer learning, the model achieves robust performance with high sensitivity and specificity. The novelty of this approach lies in its ability to identify subtle facial and behavioral patterns indicative of ASD, using a multidisciplinary strategy that combines neuroimaging, machine learning, and real-time diagnostic feedback. By advancing beyond traditional methods, this comprehensive framework holds the potential to revolutionize early ASD detection, making it more accessible, scalable, and precise. © 2025 IEEE.

关键词： Convolutional neural networks

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Botanical Bio Vision: Advancing Plant Leaf Disease Classifications with Convolutional Neural Network 6

Botanical Bio Vision: Advancing Plant Leaf Disease Classific...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Madhuvarshini, V. Iphiya, Magdiel D. Dept. of Computer Science and Engineering St. Joseph 's Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

In recent years, the agricultural sector has encountered major challenges due to the widespread presence of plant leaf diseases, which pose serious risks to crop yields and food security. Advancements in artificial intelligence, particularly through Convolutional Neural Networks (CNNs), offer a promising solution for the precise and efficient classification of plant leaf diseases. This study delves into the development and application of a CNN model built using TensorFIow to classify a variety of plant leaf diseases. By leveraging an extensive dataset containing images of both healthy and diseased leaves, the model is trained to accurately recognize and distinguish between several disease types. This approach not only automates the process of disease identification but also significantly improves diagnostic precision and speed, enabling swift and targeted interventions. Experimental findings reveal the CNN model's effectiveness, achieving notable improvements over traditional image processing methods. This research highlights the potential for incorporating deep learning methods into agricultural practices, paving the way for more intelligent and resilient crop management systems. © 2025 IEEE.

关键词： Bacterial Count Dairy Farming Fat Content Machine learning Milk Quality Predictive Models Protein Levels Sustainability

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A Novel Regularized Deep Learning Approach for Tuberculosis Risk Prediction 2

A Novel Regularized Deep Learning Approach for Tuberculosis ...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Kumar, Pankaj Singh, Vishav Pratap Singh, Ajay Pal GPC Patiala Department of Technical Education Punjab India Dept. of Computer Science & Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798331530389

Effective treatment of tuberculosis (TB) requires prompt and precise risk assessment, since it continues to be a major worldwide health concern. Labor-intensive and subjective approaches are frequently used in traditional methods. The neural network model for Tuberculosis risk categorization which includes the following categories: NoRisk, LowRisk, ModerateRisk, and HighRisk is found in this paper. Regularization, Batch normalization and the use of Reversed Linear unit (ReLU) activation functions is adopted by the model to enhance learning process. After successfully conducting the sophisticated and optimal simulation the proposed model attains 91% classification accuracy using a variety of physiological and clinical parameters. This potentially presents future deep learning techniques to automatically rate people for possible tuberculosis occurrence thus delivering a strong tool to medical practitioners to offer distinctive intervention techniques. With deep learning, the model is capable of detecting a very complex pattern and correlation between data sets inputs and its output, thus enhancing its predictive precision as well as offering recommendations that may assist in the early detection of actual clinical situations. © 2025 IEEE.

关键词： Risk assessment

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Cloud Computing Network in Remote Sensing-Based Climate Detection Using Machine Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 400-408页

作者： Srinivas, Jhade Raju, Ch VV Narasimha Sasikala, C. Bhaskar, Parumanchala Panyala, Amarendra Reddy Priya, Divya Department of Computer Science and Engineering - Data Science K G Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Information Technology Marri Laxman Reddy Institute of Technology and Management Dundigal Telangana Hyderabad 500043 India Dept of CSE Srinivasa Ramanujan Institute of Technology (Autonomous) Andhra Pradesh Ananthapuramu India Department of Computer Science and Engineering Santhiram Engineering College Nandyal India Department of Computer Science and Engineering (Data Science) Malla Reddy University Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Effective monitoring of the environment over a large area will require mobilization of a considerable amount of information. Otherwise, the use of traditional methods will prove to be costly and would take up so much time. Also, assessing the land cover changes between two times is one of the other methods which can be adopted to measure the environmental sustainability. Using ML techniques for predictive modeling in remote sensing has emerged as a useful approach in solving some of the environmental and climatic challenges. This research proposes a novel method in remote sensing image-based climate detection utilizing cloud computing and machine learning algorithms. Here, the cloud computing module is used in the collection of remote sensing images for the detection of climate changes. Then, the collected image is processed for noise removal as well as normalization. The processed image is segmented, and features are extracted utilizing a fuzzy K-clustering-based contour Gaussian model with graph cut Boltzmann CNN. Experimental analysis is carried out for various remote sensing-based climate change datasets in terms of training accuracy, random precision, AUC, recall, and F-measure. The proposed technique obtained 97% of training accuracy, 95% of random precision, 96% of recall, 93% of AUC, and 94% of F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate detection Cloud computing Contour Gaussian model Machine learning algorithms Remote sensing images

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AI-Enhanced Virtual Trial Room using Augmented Reality 6

AI-Enhanced Virtual Trial Room using Augmented Reality

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Mythili, N. Harini, M. Janalyn maroula, L. Dept. of Computer Science and Engineering St. Joseph’s Institute of Technology Old Mamallapuram Road 119 Chennai India

ISBN: (纸本)9798331522667

The Virtual Trial Room is an innovative augmented reality (AR) system that enables users to virtually try on apparel, enhancing the online shopping experience by simulating a physical fitting room. This technology leverages advanced computer vision, 3D modeling and machine learning to deliver an accurate and immersive experience. Data augmentation techniques are utilized to expand the training dataset, ensuring more precise clothing placement on different body types. The results demonstrate that our integrated approach, which combines AR and machine learning, significantly outperforms traditional virtual try-on systems, achieving enhanced user satisfaction, fit accuracy and personalized recommendations. The convolutional neural network (CNN) used in the system accurately detects body and facial landmarks, allowing for real-time garment fitting and recommendations. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing 3D Modeling Quality Through Orthorectified Image Accumulation

Enhancing 3D Modeling Quality Through Orthorectified Image A...

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2025 International Conference on Electronics, information, and Communication, ICEIC 2025

作者： Kwag, Cheolsu Kim, Euntaek Lee, Yongsuk Jung, Seungmin Hwang, Sung Soo Ryu, Hwangryol Moon, Youngmin Handong Global University Dept. of Computer Science and Electrical Engineering Pohang Korea Republic of School of Computer Science and Electrical Engineering Handong Global University Pohang Korea Republic of Research Institute of Industrial Science and Technology Smart Engineering Research Laboratory Pohang Korea Republic of

ISBN: (纸本)9798331510756

This study introduces a novel methodology designed to facilitate the capture of comprehensive image datasets, crucial for accurate 3D modeling of expansive indoor spaces. Leveraging orthophotos generated from panoramic camera data and visualizing image coverage density through heatmaps, our approach effectively identifies under-captured and unvisited areas. This enables targeted image acquisition, minimizes redundant reshoots, and enhances the overall quality and fidelity of 3D models. © 2025 IEEE.

关键词： Digital elevation model

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Defense and Recovery Strategies for Flash-Based Storage Under Ransomware Attacks: A Survey

Defense and Recovery Strategies for Flash-Based Storage Unde...

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2025 International Conference on Electronics, information, and Communication, ICEIC 2025

作者： Yin, Junhong Kang, Kyungtae Major in. Bio Artificial Intelligence Hanyang University Dept. Computer Science and Engineering Ansan Korea Republic of Hanyang University Dept. Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9798331510756

As ransomware evolves, traditional OS-based de-tection mechanisms face growing challenges, particularly from advanced attacks exploiting system vulnerabilities and escalating privileges. The continuous evolution of ransomware attacks has driven researchers to explore storage-level defense strategies, focusing on the potential of Solid-State Drives (SSDs) to enhance data protection and recovery. This paper presents a compre-hensive survey of the current state and future developments in SSD firmware-level ransomware detection and recovery, based on literature published between 2015 and 2024. Firmware-level I/O monitoring helps detect ransomware by identifying the typical 'read-encrypt-overwrite' pattern. This low-level access offers an advantage in detecting ransomware activity. However, conventional detection methods are often prone to false pos-itives. To enhance detection accuracy, some systems integrate machine learning models within the SSD controllers, analyzing I/O request patterns and entropy values of written data to assess potential threats. The Garbage Collection(Gc)mechanism plays a crucial role in data recovery by retaining older versions of data until reclaimed, which can aid in restoring encrypted content. Similarly, backing up legitimate I/O operations incurs performance overhead. To address this, some systems utilize techniques such as neural networks and remote backups to reduce unnecessary performance degradation. Future research should prioritize developing lightweight solutions tailored for SSDs with limited computational resources. Emphasis should be placed on enhancing the timeliness of real-time detection to effectively respond to the rapidly evolving nature of ransomware attacks. © 2025 IEEE.

关键词： Firmware

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