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Autism Spectrum Disorder Detection Using Convolutional Neural Network

Autism Spectrum Disorder Detection Using Convolutional Neura...

引用

Visual Analytics and data Visualization (ICVADV), International Conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Neuroimaging Autism Visual analytics Face recognition Transfer learning Imaging data visualization Sensitivity and specificity Real-time systems Convolutional neural networks

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Alpha-x Framework for Assessing Network Robustness in Nakagami-m Fading and Co-Channel Interference Environments

Alpha-x Framework for Assessing Network Robustness in Nakaga...

引用

Delhi Section Conference (DELCON), IEEE

作者： Deepak Upadhyay Karan Siwach Shiv Ashish Dhondiyal Abhay Upadhyay Mridul Gupta Kunj Bihari Sharma Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

This article explores how the Alpha-x framework can improve network robustness under Nakagami-m fading and co-channel interference (CI), The study evaluates network performance using key metrics such as outage probability, spectral efficiency, and capacity degradation. The simulations show a 50% reduction in outage probability, a 20% boost in spectral efficiency, and a 33.3% lower capacity degradation compared to traditional models. These results demonstrate how the Alpha-x framework enhances network performance by adapting to various fading conditions and interference levels in wireless communication systems. The framework not only improves reliability and efficiency but also shows practical relevance in network planning and management. This can potentially lead to better user experiences and increased operational resilience. Future research could focus on optimizing the underlying algorithms and testing their performance on a larger scale in real-world environments. As telecommunications systems evolve, frameworks like Alpha-x could play a crucial role in ensuring reliable and efficient wireless networks.

关键词： Fading channels Degradation Adaptation models Spectral efficiency Probability Robustness Power system reliability Telecommunication network reliability Interchannel interference Capacity planning

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Detection and Localization of Car Damages Using Deep Learning

Detection and Localization of Car Damages Using Deep Learnin...

引用

Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Prachi Parihar Manas Sachdeva Deepak Gupta Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

In the automotive industry, accurate and efficient vehicle damage detection is crucial for reducing the cost of claims and improving assessment procedures. This paper presents an automated method for vehicle damage detection using deep learning based You Only Look Once models, specifically YOLOv3, YOLOv5, and YOLOv8, in conjunction with two optimizers, AdamW and SGD. The COCOtoYOLO dataset contains pictures with six damage kinds. Gaussian Blur, Median Blur, Grayscale conversion, and Contrast Limited Adaptive Histogram Equalization (CLAHE) improved picture quality and feature visibility. The results reveal that YOLOv8, optimized with SGD over 200 epochs, achieved the highest mean Average Precision (mAP-50) of $72 \%$, surpassing other configurations. This study demonstrates the effectiveness of YOLOv8 and SGD in vehicle damage detection. The highest F1-score of ${9 3 \%}$ for glass shutter class claims the model’s robustness for different types of car damages.

关键词： YOLO Deep learning Location awareness Industries Technological innovation Transfer learning Glass Robustness Tires Automobiles

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AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI data

AI-Powered Personalized Deep Learning Framework for Early De...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

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A Secure and Efficient Group Authentication Framework for Blockchain-Enabled Vehicular Edge Computing Systems

A Secure and Efficient Group Authentication Framework for Bl...

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Disruptive Technologies (ICDT), International Conference on

作者： Pawan Meena Suryansh Dash Sachin Monika Poonam Rani Dept. of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Maintaining privacy and trust within Vehicular Edge Computing (VEC) systems requires proper authentication methods that ensure safety and effectiveness in dynamic settings. Current authentication solutions face three main drawbacks including excessive computational costs together with complex identity management systems and large security exposure. The proposed lightweight Multi-Entity Authenticated Group Key Agreement protocol serves blockchain-based VEC networks according to this research. MAGKA enhances authentication through bulk certification removal and reduces reliance on identity authentication and provides seamless vehicle traffic across edge networks. The solution provides VEC authentication with robust security and scalability demonstrated through security analysis of its ability to withstand attacks.

关键词： Protocols Costs Scalability Authentication Vehicular ad hoc networks Blockchains Safety Security Vehicle dynamics Edge computing

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Decoding the News: An Integrated Approach for News Text Classification and Expert System Recommendations

Decoding the News: An Integrated Approach for News Text Clas...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Vivek Prajapati Arpit Mishra Prathmesh Shinde Neha Raut Dept. of Artificial Intelligence and Data Science Vidyavardhini’s College of Engineering and Technology Mumbai India

In the era of information overload on online platforms, efficient access to news content has become crucial for individuals leading busy lives. This paper addresses the need for categorizing vast amounts of online news data into specific sections like sports, entertainment, business, finance, etc., catering to users’ personalized interests. Leveraging Machine Learning and Deep Learning algorithms, the study utilized a BBC News dataset from Kaggle and supplemented it with an additional dataset generated through web scraping using BeautifulSoup. With up to 20,000 rows covering sports, business, entertainment, politics, and technology categories, the research applied Random Forest, Support Vector Machine (SVM), Gaussian Naive Bayes (NB), and Multi-Head Attention Transformer algorithms. The findings and recommendations extend to technologies suitable for both news text classification and user-specific content recommendation in this evolving digital landscape.

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Fractional Delay and Doppler Estimation for OTFS Systems with Doppler Squint Effect

Fractional Delay and Doppler Estimation for OTFS Systems wit...

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IEEE Conference on Wireless Communications and Networking

作者： Meiying Zhang Ruoxiao Cao Jun Qian Hengtao He Shenghui Song Jun Zhang Khaled B. Letaief Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Orthogonal time frequency space (OTFS) modulation is a promising technology for mitigating severe Doppler effects in high-mobility scenarios. However, existing OTFS channel estimation methods neglect the Doppler Squint Effect (DSE), which incurs serious performance loss. In this paper, we propose a channel estimation algorithm based on Newton's method to accurately estimate fractional delay and Doppler in OTFS systems with DSE. In particular, we obtain the maximum delay and Doppler grid spacing for codebook design to guarantee the convergence of the algorithm. Additionally, we derive the Cramér-Rao lower bound (CRLB) for testing channel parameter estimation performance of our proposed algorithm. Simulation results demonstrate that our proposed algorithm outperforms the orthogonal matching pursuit (OMP) algorithm in terms of normalized mean square error (NMSE), surpassing the Newtonized OMP algorithm with traditional dictionary matrix and approaching the CRLB performance.

关键词： Time-frequency analysis Simulation Matching pursuit algorithms Signal processing algorithms Channel estimation Integrated sensing and communication MIMO Delays Doppler effect Convergence

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Selective Attention UNet for Segmenting Liver Tumors

Selective Attention UNet for Segmenting Liver Tumors

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Patil, Darshan Sakarkar, Gopal Khatri, Pearl Khedkar, Atharva Gan, Hong-Seng Ramlee, Muhammad Hanif G. H. Raisoni College of Engineering Dept. of Artificial Intelligence Nagpur India Dr. Vishwanath Karad Mit World Peace University School of Computer Science Kothrud Pune India Banasthali Vidyapith Dept. of Computer Science Jaipur India Xi'an Jiaotong-Liverpool University School of Ai and Advanced Computing Suzhou China Universiti Teknologi Malaysia Dept. of Biomedical Engineering Johor Malaysia

ISBN: (纸本)9798350312492

In order to segregate liver tumours in medical imaging applications, a novel architecture called Selective Attention UNet is proposed in this article. The suggested architecture, which is based on the well-known UNet architecture, has a selective attention module that enables the network to concentrate on crucial tasks while suppressing unnecessary ones. Link skipping between the encoder and decoder routes is another element of the design that enables the network to effectively segment data using both low-level and high-level attributes. On the publicly accessible LiTS dataset, we assessed the performance of the suggested architecture and contrasted it with four fundamental models: FCN, UNet, UNet++, and SegNet. The Dice Similarity Coefficient (DSC) of 0.89 a mean IOU of 0.76 obtained in our experiments demonstrates that the suggested architecture beats all baseline models in terms of accuracy and robustness criteria. The project is accessible at: https://***/darshan8850/Liver-tumor-Segmentation © 2023 IEEE.

关键词： computer vision

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Enhanced Gender Detection in Surveillance Systems Using YOLOv8 and Back Body Pose Analysis

Enhanced Gender Detection in Surveillance Systems Using YOLO...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Nishintha A Veeramalai Sankaradass J Nivetha Gokul V Keerthikumar M R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

An important facet of gender detection in surveillance systems is improving security, crowd monitoring, and behavioral analysis. This work explains advanced techniques for gender detection applications using the latest version of the You Only Look Once object detection algorithm called YOLOv8. YOLOv8, known for its real-time processing capacity and increased accuracy, is an efficient tool for surveillance applications. This article discusses YOLOv8 implementation in gender identification in challenging environments. Challenges from lighting conditions, occlusions, and angles of the cameras continue being exposed. Experiments show that YOLOv8 is robust enough to detect and classify gender accordingly using real-time surveillance footage. We also utilized the back body pose for analysis, whereas others employed front face or body poses. The work here provides insights for optimizing model parameters and enhances detection precision. This offers a very valuable tool for modern surveillance systems.

关键词： YOLO Accuracy Surveillance Superresolution Lighting Streaming media Cameras Real-time systems Security Streams

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Masked Ciphertext Comparison without Masking Conversions using Register Rotation and Chinese Remainder Theorem

Masked Ciphertext Comparison without Masking Conversions usi...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Geumtae Kim Eunyoung Seo Yongwoo Lee Jong-Seon No Dept. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea Dept. Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea Dept. Electrical and Computer Engineering Inha University Incheon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we propose a novel method for masked comparison using register rotation technique without masking conversions. In key encapsulation mechanisms (KEMs), ciphertext comparison is essential to ensure the security, but it is one of the most computationally expensive operations in masking environment. Previous works on masked ciphertext comparison have mainly focused on masking conversion and its post-processing. However, the proposed method introduces another register-based approach, which eliminates the need for time-consuming masking conversion and solely relies on register rotation using Chinese remainder theorem (CRT).

关键词： Encapsulation Memory management Registers Information and communication technology Cryptography Convergence

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