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Exploring Ensemble Architectures for Lung X-Ray Multi-Class Image Classification using CNN-LSTM

Procedia computer science 2025年 258卷 852-861页

作者： Rekha R Nair Tripty Singh Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwavidyapeetham India

Early diagnose of lung diseases is important as it aids In the treatment of the diseases and increment of the patient’s quality of life. Chest X-rays are a widely used and cost-effective diagnostic tool for examining the lungs and detecting abnormalities. However, interpreting chest X-ray images can be challenging, even for experienced radiologists, due to the complexity of lung anatomies and the subtle variations in disease manifestations. Thus, the ensemble-based approach to Lung X-Ray Multi-Class image classification has been proposed and used Convolutional Neural Network and Long Short-term Memory (CNN-LSTM). The proposed model is compared with Several models such as ensemble of DenseNet and InceptionV3, Vgg16 and Mobilenet V2 and deep bidirectional LSTM(DBLSTM). The proposed model applied for multiclass classification on lung disorders such as TB, normal, pneumothorax, pneumonia, COVID-19, and cardiomegaly. The proposed method provides a testing F1-Score of 94%, Recall of 99%, Precision of 95% and accuracy of 89.31% compared to other ensemble models. The proposed model can be utilized for medicos as a second opinion for diagnosis and treatment planning.

关键词： Convolutional Neural Network Long Short-term Memory Vgg16 Mobilenet V2 DBLSTM

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MedFS: Pursuing Low Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Device 23

MedFS: Pursuing Low Update Overhead via Metadata-Enabled Del...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Wu, Chao Ji, Cheng Chang, Li-Pin Zhu, Zongwei Gao, Congming Guo, Weichao Yu, Chao Wang, Yanzhi Department of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science National Yang Ming Chiao Tung University Taiwan Suzhou Institute for Advanced Research University of Science and Technology of China China School of Information Science Xiamen University China Guangdong Oppo Mobile Telecommunications Corp. Ltd. China College of Engineering Northeastern University United States

ISBN: (纸本)9781939133458

The increasing deployment of data-intensive applications on mobile devices poses a formidable challenge in designing flash-based file systems tailored to these needs. Studies have shown that adopting delta compression in log-structured file systems is promising for such an environment as it can effectively reduce the write stress and improve flash longevity. Unfortunately, delta compression suffers from large maintenance overhead. While prior works have introduced nonvolatile memory buffer or battery-backed DRAM to mitigate this, they are less appealing for cost-sensitive mobile devices. This paper introduces MedFS, a Metadata-enabled delta compression on log-structured File System for mobile devices, to achieve a good design trade-off in log-structured file systems employing delta compression. Through a comprehensive analysis of mobile applications and file update patterns, we develop delta-inlining technique, which consolidates delta updates within the inline area of the file's inode block. By leveraging the inherent inode structure and automatically flushing dirty inodes to storage, we effectively address the maintenance overhead associated with delta compression. Additionally, we propose a complementary delta maintenance strategy that selectively manages delta chunks in the data area, overcoming the space constraints of the inline area. Experimental results show that MedFS significantly reduces the write traffic by 55.1% on average, leading to the prolonged storage endurance by 122.7% and improved I/O performance by up to 37.3% over existing work. © 2025 FAST. All Rights Reserved.

关键词： Nonvolatile storage

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PQS-AKA: Post-Quantum Secure Lightweight Authenticated Lattice-Based Key Agreement for IoT Communications 4th

PQS-AKA: Post-Quantum Secure Lightweight Authenticated Latti...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Singh, Ajeet Tentu, Appala Naidu Rao, K. Gangadhara Department of Computer Science and Engineering Acharya Nagarjuna University Andhra Pradesh Guntur522 510 India C.R. Rao Advanced Institute of Mathematics Statistics and Computer Science University of Hyderabad Campus Telangana Hyderabad500 046 India

ISBN: (纸本)9789819757022

As the Internet of Things (IoT) ecosystem expands, the need for secure and private communication over public networks becomes increasingly crucial. A fundamental component of this security paradigm is the establishment of a secret session key among IoT devices, enabling the secure transmission of confidential data over the Internet. In this paper, we introduce a Post-Quantum Secure Lightweight Authenticated Lattice-based Key Agreement (PQS-AKA) protocol, specifically developed for the rapidly evolving IoT ecosystem. Traditional Authenticated Key Agreement (AKA) protocols, predominantly based on concepts like Diffie–Hellman or prime factorization, are becoming obsolete due to their vulnerability to quantum computing advancements and their inefficiency in the resource-constrained IoT environment. Current quantum-resistant methods, although leveraging lattice cryptography for security, are impeded by issues such as high certificate management overheads and substantial communication and computational costs. PQS-AKA not only offers a defense against the threats posed by quantum computing through the inherent hardness of lattice problems but also simplifies the process by removing the need for extensive certificate management. The core security strength of our protocol is thoroughly evaluated under the random-natured oracle model, demonstrating its robustness against both present and future quantum computing attacks. Performance evaluations reveal that the proposed protocol offers a significant improvement over existing solutions in terms of efficiency and practicality, making it a viable and secure choice for industrial IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quantum computers

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AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease Prediction and Online Consultation for Accessible Medical Support 2

AI-Enhanced Healthcare: Densenet Based Alzheimer's Disease P...

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

作者： Wilfred Blessing, N.R. Gunasekaran, Hemalatha Hariharan, B. Al Jabri, Jaber Saleh Salim Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman SRM Institute of Science and Technology Kattankulathur Department of Computational Intelligence Tamil Nadu India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Aruthenganvilai India

ISBN: (纸本)9798331505745

Improvements in patient care and early disease detection possibilities are two of the ways artificial intelligence is transforming the healthcare industry. Alzheimer's disease, a neurodegenerative disease that worsens with time, is among the most difficult diseases to exactly identify in early stages. Patients thus delay in treatment and experience a decline in their quality of life. Research is still under progress, thus knowledge about how AI-driven prediction models could be combined with readily available healthcare services remains lacking. This work presents a special artificial intelligence-powered system consisting of DenseNet-based deep learning for Alzheimer's disease prediction with an online consultation platform. The proposed approach analyzes brain MRI data and classifies Alzheimer's stages with higher accuracy using a DenseNet architecture, well-known for its exceptional performance in the field of feature extraction. Comprising three thousand magnetic resonance imaging (MRI) images, the dataset has been preprocessed for noise reduction and tuned for models performance. At 98.5%, the DenseNet model exceeded other methods including VGGNet and ResNet in accuracy. © 2025 IEEE.

关键词： Neurodegenerative diseases

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Near-Field Communications: Shape and Structure Design for Uniform Planar Array

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IEEE Internet of Things Journal 2025年第11期12卷 15151-15165页

作者： Chen, Siyu Gu, Juping Duan, Wei Zhang, Lei Dang, Shuping Wen, Miaowen Ding, Zhiguo Ho, Pin-Han Nantong University School of Information Science and Technology Nantong226019 China Suzhou University of Science and Technology China Hubei Polytechnic University Department of Electronics and Information Engineering Huangshi435003 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

With the flourishing development of 6G wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this paper, multiple-input single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in details, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations. © 2025 IEEE.

关键词： Spheres

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High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

High-Fidelity Document Stain Removal via A Large-Scale Real-...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Li, Mingxian Sun, Hao Lei, Yingtie Zhang, Xiaofeng Dong, Yihang Zhou, Yilin Li, Zimeng Chen, Xuhang School of Computer Science and Engineering Huizhou University China The Ohio State University Department of Computer Science and Engineering United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China School of Electronic and Communication Engineering Shenzhen Polytechnic University China

ISBN: (纸本)9798331510831

Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145 x 2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer's superior performance over state-of-the-art methods on the Stain-Doc dataset and its variants *** and ***, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research. © 2025 IEEE.

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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AI-Enhanced Child Handwriting Analysis: A Framework for the Early Screening of Dyslexia and Dysgraphia

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SN computer science 2025年第5期6卷 1-26页

作者： Rangasrinivasan, Sahana Sumi Suresh, M.S. Olszewski, Abbie Setlur, Srirangaraj Jayaraman, Bharat Govindaraju, Venu Department of Computer Science and Engineering University at Buffalo Buffalo 14260 NY United States Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam 690525 India College of Education and Human Development University of Nevada-Reno Reno 89557 NV United States Amrita Institute of Advanced Research Elburn 60119 IL United States

Dyslexia and dysgraphia are two specific learning disabilities (SLDs) that are prevalent among children. To minimize the negative impact these SLDs have on a child’s academic and social-emotional development, it is crucial to identify dyslexia and dysgraphia at an early age, enabling timely and effective intervention. The first step in this process is screening, which helps determine if a child requires further instruction or a more in-depth assessment. Current screening tools are expensive, require additional administration time beyond regular classroom activities, and are designed to screen exclusively for one condition, not for both dyslexia and dysgraphia, which often share some common behavioral characteristics. Most dyslexia screeners focus on speech and oral tasks and exclude writing activities. However, analyzing children’s writing samples for behavioral signs of dyslexia and dysgraphia can offer valuable insights into the screening process, which can be time-consuming. As a solution, we propose a co-designed framework for building artificial intelligence (AI) tools that could boost the efficiency of screening and aid practitioners such as speech-language pathologists (SLPs), occupational therapists, general educators, and special educators by simplifying their tasks. This paper reviews current screening methods employed by practitioners, the use of AI-based systems in identifying dyslexia and dysgraphia, and the handwriting datasets available to train such systems. The paper also outlines a framework for developing an AI-integrated screening tool that can identify writing-based behavioral indicators of dyslexia and dysgraphia in children’s handwriting. This framework can be used in conjunction with current screening tools like the Dysgraphia and Dyslexia Behavioral Indicator Checklist (DDBIC). The paper also proposes a methodology for collecting children’s offline and online handwriting samples to build a valuable dataset for developing AI solutions. The pr

关键词： Artificial intelligence Child handwriting Dysgraphia Dyslexia Early screening

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Unraveling Dementia Severity: A Deep Learning Approach for Brain MR Image-Based Prediction and Misclassification Analysis

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Procedia computer science 2025年 258卷 1199-1208页

作者： Rekha R Nair Tina Babu Tripty Singh Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

Alzheimer’s disease is the most prevalent cause of dementia, and its early diagnosis is crucial to prevent the progression to severe stages where cognitive abilities are severely impaired. This research paper presents an innovative approach to predict the severity of dementia through classification and grading. The research introduces an innovative adaptation of the DEMNET model, referred to as the DEMENtia network model. The research implements a novel methodology leveraging Convolutional Neural Networks (CNNs) to identify significant patterns within unorganized web-based data collections. The investigation employs a dataset com- prising four categories, obtained from the Kaggle platform. The developed model demonstrates exceptional performance, achieving 99.9% accuracy during training, 97.4% accuracy in testing, and an overall precision of 0.975. The DEMENtia network model suc- cessfully categorizes individuals into four groups: those without dementia, and those with moderate, mild, or very mild dementia. The model achieves a remarkable accuracy of 99.20% in classifying the Moderate demented class, a significant advantage over existing approaches. To understand this behavior, conducted an in-depth analysis by visualizing the pixel intensity distribution over the space. The proposed model validity has been confirmed through validation by a team of neurologists, ensuring its potential for real-world clinical applications. By accurately predicting dementia severity, the proposed model can aid in early diagnosis and treatment planning, contributing to improved patient care and management.

关键词： Alzheimer’s disease Dementia DEMENtia network model Convolutional Neural Network (CNN)

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Integrating GIS-Remote Sensing: A Comprehensive Approach to Predict Oceanographic Health and Coastal Dynamics

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 200-212页

作者： Krishnamoorthy, R. Tanaka, Kazuaki Begum, M. Amina Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India Department of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Department of ECE Sri Venkateswaraa College of Technology Tamilnadu Chennai India

The monitoring of oceanographic and coastal dynamics is essential for understanding the effects of climate change, predicting natural disasters, and managing coastal resources. Remote sensing technology, particularly through satellite imagery, has revolutionized the ability to collect vast amounts of environmental data over large spatial and temporal scales. However, the complexity of these datasets, which often include intricate spatial patterns and long-term temporal trends, requires advanced machine learning models capable of handling both dimensions effectively. In this study, we present a Hybrid GIS-Transformer Network for analyzing oceanographic and coastal dynamics using remote sensing data. The proposed model integrates the spatial feature extraction capabilities of the GIS network with the temporal analysis strengths of the transformer model. This approach enables a comprehensive understanding of both spatial and temporal patterns in high-resolution satellite imagery, allowing for more accurate detection and prediction of environmental changes. Our methodology was tested across multiple tasks, including coastal erosion detection, sea surface temperature (SST) prediction, wave height prediction, and ocean currents detection. The Hybrid GIS-Transformer model consistently outperformed existing models such as CNN, ResNet50, MobileNet, EfficientNet, and LSTM. For coastal erosion detection, the hybrid model achieved an accuracy of 96.12%, while for SST prediction, the accuracy was 95.43%. In wave height prediction, the model achieved an accuracy of 93.02%, and for ocean currents detection, an accuracy of 91.88% was recorded. These results demonstrate the model’s superiority in capturing both fine spatial details and long-term temporal dynamics compared with individual models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Coastal dynamics Deep learning Environmental monitoring Hybrid GIS-Transformer Machine learning Oceanographic analysis Remote sensing Sea surface temperature prediction Spatial feature extraction Temporal analysis Wave height prediction

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