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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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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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Improved Bounds For Online Convex Optimization

Improved Bounds For Online Convex Optimization

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Nayak, Tanvi S. Bharath, B.N. Dept. of Computer Science and Engineering Indian Institute of Technology Dharwad Dharwad India Dept. of EECE Indian Institute of Technology Dharwad Dharwad India

ISBN: (纸本)9798350368741

In this paper, we consider the problem of online learning with convex objectives. Most of the existing work shows that static and dynamic regrets scale logarithmically or sub-linearly with the time horizon T. On the contrary, for the strongly convex setting, it is shown that the static and dynamic regrets scale with the variation term that measures the rate at which the function changes over time. In the convex setting, we show that a simple Online Gradient Descent (OGD) algorithm with single round at each time achieves a regret that is comparable with the strongly convex setting with an additional assumption of Quadratic Growth (QG) condition. More specifically, we show that the static and dynamic regrets scale as O(Var3(T)) and O(Var1(T)) + O(Var2(T)), respectively. Here, Vari, i = 1, 2, 3 represent variation terms that capture different aspects of changes in the optimal solution over time. We use experiments to corroborate some of our theoretical findings. © 2025 IEEE.

关键词： convex optimization Online learning QG condition regret bounds sparse signal processing

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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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Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning

arXiv

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arXiv 2025年

作者： Ahmmed, Fahud Raihan, Md. Zaheer Nahar, Kamnur Asadujjaman, D.M. Rahman, Md. Mahfujur Tamim, Abdullah Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Bangladesh Dept. of Computer Science & Engineering Rajshahi University Bangladesh

Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Skin diseases such as Actinic Keratosis and Psoriasis can be fatal. These are treatable if identified early. However, its diagnostic methods are expensive and not widely accessible. In this study, a novel and efficient method for diagnosing skin diseases using deep learning techniques has been proposed. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. This model includes several convolutional layers. The VGG16 model has been employed using ImageNet weights and modified top layers. The top layer is modified by fully connected layers and a final softmax activation layer to obtain the result. The dataset analyzed is publicly available and titled "Skin Disease Dataset". The VGG16 architecture does not include augmentation by default;data augmentation is typically performed through rotation, shifting, and zooming during preprocessing prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating reliability in classifying skin diseases. The modified pre-trained model showed promising results, increasing its potential for real-world *** Codes 68T07 © 2025, CC BY.

关键词： Convolutional neural networks

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Contention-Free Configured Grant Scheduling for 5G URLLC Traffic 23

Contention-Free Configured Grant Scheduling for 5G URLLC Tra...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Tianyu Zhang Xiaobo Sharon Hu Song Han Dept. of Computer Science and Engineering University of Connecticut Dept. of Computer Science and Engineering University of Notre Dame

ISBN: (纸本)9798350323481

5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.

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