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Diagnosis of Autism Spectrum Disorders Based on fMRI 24

Diagnosis of Autism Spectrum Disorders Based on fMRI

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Zhao, Xiaoxue Wu, Peng School of Information Science and Engineering University of Jinan Jinan China

ISBN: (纸本)9798400717482

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and repetitive behaviors, with its exact etiology remaining unclear. Early and accurate diagnosis is critical for effective interventions. This paper introduces a diagnostic method based on functional magnetic resonance imaging (fMRI) data, aiming to extract more effective features and understand the relationships between them. This study utilizes a multi-site fMRI dataset provided by the Autism Brain Imaging Data Exchange (ABIDE) to objectively identify functional connectivity patterns in ASD participants. A deep learning model combining autoencoders and Transformers is proposed to learn complex patterns within the brain's functional connectivity matrix and capture interactions between different brain regions. The study evaluated 883 subjects from the ABIDE cohort and, by comparing its performance with that of the ASD-DiagNet[12] model on the same dataset, validated the efficacy of the proposed method. Experimental results demonstrate that the method achieved high classification accuracy in identifying ASD, laying a foundation for future research and suggesting potential applications in clinical diagnosis of ASD. © 2024 Copyright held by the owner/author(s).

关键词： Diseases

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Research on Point Cloud Registration Method Based on Improved Harmony Search Algorithm 24

Research on Point Cloud Registration Method Based on Improve...

引用

8th international conference on Algorithms, computing and Systems, ICACS 2024

作者： Liang, Yuchong Mao, Yunsheng Guo, Rong School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9798400718304

When measuring and inspecting hull surfaces using a 3D laser scanner, the large volume and complex surface features of the hull necessitate multi-station scanning. Complete hull surface data is obtained by registering point cloud data from multiple stations. However, the registration process accumulates errors, resulting in the registration not meeting inspection accuracy requirements. To address the issue of cumulative errors in point cloud registration for large-scale laser scanning, a multi-station point cloud data registration error correction algorithm based on global control targets is proposed. This algorithm leverages the long-distance measurement and high accuracy of total stations to compensate for the large measurement errors of the scanner at long distances. By applying the coordinate information of global control targets measured by the total station to the point cloud data registration, cumulative registration errors are effectively avoided, thereby improving measurement accuracy. Experimental results show that the proposed algorithm reduces the cumulative error of the first and last point clouds to 16% of that of traditional registration, effectively eliminating registration errors and meeting inspection requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Laser applications

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Improved Phasmatodea Population Evolution Algorithm based on Dung Beetle Optimizer 24

Improved Phasmatodea Population Evolution Algorithm based on...

引用

2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Ren, Hucheng Feng, Xiangsheng Sun, Quan Xiong, Lei School of Information Engineeringa Nanchang Institute of Technology Jiangxi Nanchang China

ISBN: (纸本)9798400709869

To address the issues of low convergence accuracy, vulnerability to local optima, and prolonged execution time associated with traditional Phasmatodea population evolution algorithms, we propose a revised approach known as the Phasmatodea Population Evolution Algorithm based on the Dung Beetle Optimizer (DPPE). Initially, we eliminate the competitive mechanism and replace the acceptance criteria for new solutions with a greedy rule following mixed disturbances, which significantly reduces the computational time of the algorithm. Next, we employ the PWLCM chaotic mapping method for population initialization, enhancing the quality of the initial solutions. Additionally, we integrate the Dung Beetle Optimizer with a crossover strategy to boost the convergence accuracy of the algorithm. Lastly, we introduce the Gaussian-Cauchy mixed mutation operator to mitigate the likelihood of encountering local optima. To evaluate the efficacy of the DPPE algorithm, we benchmark it against other optimization algorithms using 11 recognized test functions. The results indicate that the DPPE algorithm outperforms its competitors. Furthermore, to underscore the potential practical applications of the DPPE algorithm, we successfully implement it in engineering design problems, where experimental findings demonstrate its capability to effectively address real-world challenges. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Mapping

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Predicting Sector Indices Using Machine Learning: A Comprehensive Analysis Incorporating Macroeconomic Indicators 3

Predicting Sector Indices Using Machine Learning: A Comprehe...

引用

3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Murugan, Vasundra Jangale, Sumit Gangawane, Prasenjeet Gupta, Nidhi Sonawane, Prasanna Jadhav, Anil Symbiosis Centre for Information Technology Pune India

ISBN: (纸本)9798331515911

The influence of macroeconomic indicators on stock market movement continues to attract attention, particularly in emerging markets, especially countries such as India. The objective of this study is to predict NIFTY 50 sectoral indices namely Automobile, Energy, Banking, IT, FMCG, Metal, and Realty using macroeconomic indicators such as exports, imports, exchange rates, GDP, interest discount rates, and inflation rates. Traditional regression models like Lasso, Linear Regression, Elastic Net, and advanced machine learning models like XGBoost, Decision Tree, and Random Forest Regressor, Support Vector Regressor have been used for the prediction. The experiments show that the Random Forest Regressor came out with the best performance, providing a lower Mean Squared Error (MSE) and the highest R-squared values in almost all the sectors, except the Realty sector. It proves that advanced machine learning techniques go beyond ordinary regression methods in capturing nonlinear relationships. This study highlights the importance of macroeconomic factors as drivers of sector-specific stock performances in emerging markets and the potential of using machine learning models to understand them. © 2025 IEEE.

关键词： Inflation

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Internet of Things: An Aggregated Proof based Secure Healthcare Monitoring System- Comparative Analysis 6

Internet of Things: An Aggregated Proof based Secure Healthc...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Aswathy, R.H. Suresh, P. Jenifa, G. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Thandalam Chennai India Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331522667

The healthcare monitoring system plays a crucial role in remote monitoring. A highly secure healthcare system utilizing advanced cryptographic methods to protect sensitive information. The system integrates a multifactor authentication framework combined with aggregated proof mechanisms to enhance security. A detailed security analysis has been conducted to assess its effectiveness against various cyber threats. Compared to existing solutions, the proposed model demonstrates exceptional resilience to numerous known attacks, including Session Key Computation, Password Guessing, User & Gateway Node Impersonation, Replay Attacks, Sensor Node Spoofing, and Privileged Insider Attacks. These features ensure robust protection for healthcare data, addressing critical security concerns in modern applications. By leveraging the encryption and authentication strategies, the system provides a dependable and secure environment for the healthcare industry. This approach not only strengthens data privacy but also ensures operational integrity, making it a comprehensive solution for addressing current and emerging security challenges. © 2025 IEEE.

关键词： Authentication Protocol

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Indian Sign Language Alphanumeric Hand Gestures Recognition Using Deep Learning Techniques 5

Indian Sign Language Alphanumeric Hand Gestures Recognition ...

引用

5th international conference on Advanced Research in computing, ICARC 2025

作者： Mahendran, Shakana Mahendran, Ramya Vavuniya Campus of the University of Jaffna Department of Physical Science Fas Vavuniya Sri Lanka EUS. Information Technology Center Trincomalee Campus Trincomalee Sri Lanka

ISBN: (纸本)9798331530983

Alphanumeric sign language recognition plays a vital role in enabling effective communication between individuals who are deaf and the general population. This research focuses on classifying alphanumeric hand gestures in Indian Sign Language using real-time image processing. The study collects diverse hand shape images captured under varying backgrounds and lighting conditions, making it suitable for real-world applications. In the preprocessing stage, images are cropped and resized to a standardized 128×128 pixel format to ensure uniformity in the dataset. A two-pronged approach is adopted to classify the collected images. Sequential and parallel CNNs are employed to capture spatial and channel information effectively. Additionally, a channel attention module enhances the model's ability to identify crucial channel information. The Deep Convolutional Neural Network (DCNN) employed in this study achieved remarkable accuracy, reaching 99.976%. This outcome demonstrates the superior performance of this approach compared to other classification methods, whether utilizing real-time or non-laboratory experimental data. This research contributes to developing an efficient, accurate, and cost-effective system for classifying alphanumeric signs in Indian Sign Language. It facilitates easy and fast communication between individuals who are deaf and those who are not, promoting inclusive interactions. © 2025 IEEE.

关键词： Convolutional neural networks

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Toward a More Realistic Energy Consumption Model for IoT Nodes in Extreme-Edge computing Environments 10

Toward a More Realistic Energy Consumption Model for IoT Nod...

引用

10th international conference on Internet of Things, Big Data and Security, IoTBDS 2025

作者： Hammoud, Hassan Weis, Frédéric Leclerc, Melen Bonnin, Jean-Marie IRISA IMT Atlantique Rennes France IGEPP INRAE Le Rheu France IRISA Rennes University Rennes France

ISBN: (纸本)9789897587504

As Internet of Things (IoT) networks grow, accurately modeling the energy consumption of individual IoT nodes has become essential for understanding and managing energy use in diverse applications. In extreme-edge computing scenarios, where processing is pushed as close to the device as possible to support local data manipulation, memory operations play a substantial role in power consumption. However, existing models in the literature primarily focus on communication, processing, and sensing, often overlooking the contribution of memory operations to overall energy use. This paper presents an extended energy model for IoT nodes, incorporating memory-related energy usage alongside traditional factors. Results show that addressing memory usage within the energy model provides a more comprehensive understanding of consumption patterns, supporting more effective management strategies for IoT applications. Furthermore, we propose an approach that optimizes power consumption by implementing data management techniques that efficiently handle data retrieval and storage. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Energy utilization

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Transformer-Based Image Dehazing with Accurate Color Restoration 24

Transformer-Based Image Dehazing with Accurate Color Restora...

引用

13th international conference on computing and Pattern Recognition, ICCPR 2024

作者： Guo, Jixin Zhang, Ting School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9798400717482

In recent years, deep learning-based image dehazing algorithms have achieved significant improvements in effectiveness. However, existing algorithms rarely consider the issue of color restoration, leading to high chromatic aberration between restored and natural images, which not only affect the performance of high-level vision tasks, but also reduce the viewing experience of human. Aiming at improving color exactitude while image dehazing, we start with introducing the chromatic aberration description in image features, and propose a Transformer-based image dehazing algorithm with accurate color restoration (ACRT) which contains five Transformer blocks and skip connection. In the computation of the multi-head self-attention (MSA) for image patches, an additional convolution operation on the value matrix V is incorporated into the mechanism, enhancing the model's ability to attend more effectively to color information within the image. To improve training efficacy, the Charbonnier loss function is employed during model training. We test ACRT on both synthetic and real hazy dataset, RESIDE and BeDDE, then conduct comparisons with the popular state-of-the-art methods. Experiment results demonstrate the proposed idea outperforms existing methods in PSNR, SSIM and δE∗. © 2024 Copyright held by the owner/author(s).

关键词： Aberrations

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Evaluating and Mitigating HTTPS Interception in Thai E-Banking Websites: Challenges and Solutions 24

Evaluating and Mitigating HTTPS Interception in Thai E-Banki...

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13th international conference on Networks, Communication and computing, ICNCC 2024

作者： Chanbuala, Khathawut Puangpronpitag, Egachai Puangpronpitag, Darunee Puangpronpitag, Somnuk Department of Information Technology Mahasarakham University Mahasarakham Thailand Ministry of Justices Bangkok Thailand Research Group Mahasarakham University Mahasarakham Thailand

ISBN: (纸本)9798400717352

This study examines vulnerabilities in HTTPS implementation across 12 Thai e-banking websites, focusing on HSTS misconfigurations and the potential for SSL stripping and keylogger injection attacks. The results show that all sites are susceptible to SSL stripping due to a lack of HSTS preload, allowing interception of login credentials. Furthermore, keylogger injection after MITM and SSL stripping was effective even on sites with salted-hash passwords. To mitigate these threats, the paper proposes to use an On-Screen Keyboard (OSK) combined with salted-hash passwords. Experiments with this approach demonstrate its effectiveness in preventing keystroke logging while maintaining user experience. The research underscores the need for robust technical controls and user education to enhance the security of online banking systems against evolving cyber threats. © 2024 Copyright held by the owner/author(s).

关键词： HTTP

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Dynamic Spectrum Allocation with Service Priority in C+L-Band Elastic Optical Networks 4

Dynamic Spectrum Allocation with Service Priority in C+L-Ban...

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4th international Computational Imaging conference, CITA 2024

作者： Yang, Huijie Zheng, Nianying Dang, Jian Wang, Xiaoling Chen, Hong Chen, Bowen School of Electronic and Information Engineering Soochow University Suzhou China School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510688834

With the rapid development of cloud computing, video, smart home, and Internet of things (IoT), the bandwidth requirements of communication networks are also greatly increasing. This paper investigates dynamic spectrum allocation for both immediate and advance reservations in C+L-band elastic optical networks. We propose a dynamic spectrum allocation approach with service priority (DSA-SP) by considering the tolerable delay time. We consider the service importance when allocating the spectrum resources in C+L-band elastic optical networks. We discuss two types of the connection requests that include both immediate reservation and advance reservation based on the maximum tolerable delay time. The simulation results demonstrate that the proposed dynamic spectrum allocation approach achieves the low blocking probability, the high spectrum efficiency, and high average optical signal-to-noise ratio (SNR). © 2025 SPIE.

关键词： Signal to noise ratio

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