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Parkinson’s Disease Detection from Resting State EEG Using Multi-head Graph Structure Learning with Gradient Weighted Graph Attention Explanations 7th

Parkinson’s Disease Detection from Resting State EEG Using...

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7th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

ISBN: (纸本)9783031787607

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Cut to the Chase: A Fast-Decoding Scheme for Symbiotic Backscatter Multi-User NOMA Systems 16

Cut to the Chase: A Fast-Decoding Scheme for Symbiotic Backs...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wen, Zhen Ding, Haiyang Elkashlan, Maged Li, Dong Yuen, Chau Moualeu, Jules M. Chen, Mingkai Liu, Zhongwei School of Information and Communications National University of Defense Technology Wuhan China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Science and Engineering Macau University of Science and Technology China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa

ISBN: (纸本)9798350390643

This paper proposes a fast-decoding scheme based on the successive interference cancellation (SIC) framework for symbiotic backscatter multi-user non-orthogonal multiple access (NOMA) systems, which aims to decode the desired primary NOMA signal and the backscatter signal for the end-users in an efficient manner. Under the proposed decoding framework, we derive the symbiotic constraints on the decoding thresholds and power allocation ratios to guarantee the minimum decoding times and a shorter decoding times than the conventional solutions, respectively. Moreover, we derive the corresponding successful fast-decoding probability at high transmit signal-to-noise ratio (SNR). By keeping the primary channel statistics or the decoding threshold for the backscatter signal sufficiently low, our results demonstrate that the decoding speed of the proposed decoding scheme is most certainly faster than the conventional ones. © 2024 IEEE.

关键词： Signal to noise ratio

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IoT Based Battery Monitoring System for Electric Vehicle using ESP32 2

IoT Based Battery Monitoring System for Electric Vehicle usi...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Usha Rani, R. Divya, G. Lakshmi Swarupa, M. Navaneetha, Kandle Department of Computer Science Engineering CVR College of Engineering Ibrahimpatnam Hyderabad India Department of Electrical and Electronics Engineering CVR College of Engineering Ibrahimpatnam Hyderabad India

ISBN: (纸本)9798350305418

Since the system is powered by the battery, it is the most vital part of any gadget. Incorrect or excessive charging or draining of the battery might damage the battery and lead to system failure, hence it is important to monitor the battery's voltage level. By far most of current electrical and electronic gear utilize a committed instrument called a battery the board framework (BMS). The battery the board framework (BMS) screens and controls the battery's fundamental capabilities such voltage, current, temperature, and auto-off. The appropriate and get treatment of lithium-particle or lithium-polymer batteries is guaranteed along these lines. More established BMS just observed the battery and notified the user when there was a problem. Thanks to IoT, however, we can now provide instantaneous alerts to far-flung targets. They may check the remaining juice from any location using their phones or computers. The goal is to demonstrate the viability of the idea's basic premise. The hardware for the battery monitoring device is developed as part of the system development. In this study, the battery status data is sent to the Arduino IoT cloud using an IoT-based battery monitoring system, node, and MCU using an ESP32 chip. Whether the battery is being charged or discharged, the Arduino IoT cloud will display the voltage and percentage of charge remaining. © 2023 IEEE.

关键词： Battery management systems (BMS) Battery Voltage Electric Vehicles Internet of Things Lithium-ion Batteries SoC measurement

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A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11025-11040页

作者： Guan, Hao Sadati, Seyed Hossein Talebi, Ali Asghar Shafi, Jana Khan, Aysha Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Duyun China Department of Mathematics University of Mazandaran Babolsar Iran Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Mathematics Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank's data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. © 2024-IOS Press. All rights reserved.

关键词： Graph theory

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Assessing the Performance of AI-Generated Code: A Case Study on GitHub Copilot 35

Assessing the Performance of AI-Generated Code: A Case Study...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Li, Shuang Cheng, Yuntao Chen, Jinfu Xuan, Jifeng He, Sen Shang, Weiyi Wuhan University School of Computer Science Wuhan China University of Arizona Department of Systems and Industrial Engineering Tucson United States University of Waterloo Department of Electrical and Computer Engineering Waterloo Canada

ISBN: (纸本)9798350353884

The integration of Large Language Models (LLMs) into software development tools like GitHub Copilot holds the promise of transforming code generation processes. While AI-driven code generation presents numerous advantages for software development, code generated by large language models may introduce challenges related to security, privacy, and copyright issues. However, the performance implications of AI-generated code remain insufficiently explored. This study conducts an empirical analysis focusing on the performance regressions of code generated by GitHub Copilot across three distinct datasets: HumanEval, AixBench, and MBPP. We adopt a comprehensive methodology encompassing static and dynamic performance analyses to assess the effectiveness of the generated code. Our findings reveal that although the generated code is functionally correct, it frequently exhibits performance regressions compared to code solutions crafted by humans. We further investigate the code-level root causes responsible for these performance regressions. We identify four major root causes, i.e., inefficient function calls, inefficient looping, inefficient algorithm, and inefficient use of language features. We further identify a total of ten sub-categories of root causes attributed to the performance regressions of generated code. Additionally, we explore prompt engineering as a potential strategy for optimizing performance. The outcomes suggest that meticulous prompt designs can enhance the performance of AI-generated code. This research offers valuable insights contributing to a more comprehensive understanding of AI-assisted code generation. © 2024 IEEE.

关键词： Software design

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Laptop Price Prediction Using Real Time Data 1

Laptop Price Prediction Using Real Time Data

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Reddy, Chada Lakshma Reddy, K Bhargav Anil, G.R. Mohanty, Sachi Nandan Basit, Abdul Computer Science & Engineering Vardhaman College of Engineering Hyderabad India VIT-AP University School of Computer Science & Engineering Amaravati India National Urdu University Department of Computer Science & Information Technology Hyderabad India

ISBN: (纸本)9781665472753

Online laptop sales are at an all-time high as a result of the pandemic. A laptop is a must-have for working from home, as well as e-learning and other activities. The buyer is aided in making a purchasing decision by a feature-based pricing prediction algorithm. Based on real-time data scraped from an e-commerce website, this study proposes a model for predicting laptop costs. The suggested method collects data from a real-time environment and predicts the model's pricing with high accuracy. This study employs Support Vector Regression, Decision Tree Regression and Multi-Linear Regression to forecast laptop price. © 2023 IEEE.

关键词： Costs

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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Detection of Hardware Trojan in Nanocrossbar-Based Cryptographic Circuit 6th

Detection of Hardware Trojan in Nanocrossbar-Based Cryptogra...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Basu, Subhashree Ghoshal, Ranjit Kule, Malay Department of Information Technology St. Thomas’ College of Engineering and Technology Kolkata India Department of Computer Technology Indian Institute of Engineering Science and Technology Howrah Shibpur India

ISBN: (纸本)9789819780891

Many electronic components can be combined into a single chip using VLSI technology. However, as Moore predicted, this technology will soon reach a limit, making it impossible to reduce the size of VLSI circuits further. Scientists are focusing on developing nanotechnology as a result of the impending technological roadblock. Memristor technology presents itself as a viable substitute for transistor-based technology with the advantages of significantly higher speed, reduced dimensions, and low power consumption. This chapter studies the possibility of insertion of Hardware Trojan in a cryptography circuit developed from symmetric functions. Active and passive Trojans are inserted, and their effects on the actual result are analyzed to propose methods for detecting them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Memristors

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Hyperspectral Unmixing-Based Anomaly Detection 7

Hyperspectral Unmixing-Based Anomaly Detection

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Computational Imaging VII 2023

作者： Younis, Mohammed Hossain, Mazharul Robinson, Aaron Wang, Lan Preza, Chrysanthe Electrical and Computer Engineering Department China Computer Science Department The University of Memphis MemphisTN United States

ISBN: (数字)9781510661615

ISBN: (纸本)9781510661608

Research supporting improved anomaly detection performance benefits a wide range of technical applications. Thus, the definition of anomalies and the subsequent means to detect them are wide-ranging. This treatment presents an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing. Anomaly Detection is a binary classification that does not require prior information about the anomaly. For example, an anomaly detector applied to hyperspectral imaging (HSI) would take a hyperspectral image with hundreds of channels as an input and output a two-dimensional image map of pixel intensities based on a threshold procedure applied to the probability of that pixel being an anomaly. There have been many advancements in the field of HSI Anomaly Detection. Our ensemble method algorithm, presented here, addresses some of the shortcomings of current state-of-the-art techniques. We present details about the extracted end-members and use them for effective anomaly detection. Our current ensemble method opens the path for future machine-learning processes. We evaluated our method on multiple datasets and reported the F1-macro score. We suggest that the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve should not be used in Hyperspectral anomaly detection as an evaluation metric. © 2023 SPIE. All rights reserved.

关键词： Anomaly detection

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BIOMECHANICAL SIMULATION OF LATERAL ASYMMETRY IN TONGUE BRACING

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Canadian Acoustics - Acoustique Canadienne 2023年第3期51卷 196-197页

作者： Azreen, Jasia Mayer, Connor Liu, Yadong Shamei, Arian Stavness, Ian Gick, Bryan Department of Electrical and Computer Engineering University of British Columbia Canada Department of Language Science University of California Irvine United States Department of Linguistics University of British Columbia Canada Department of Computer Science University of Saskatchewan Canada

Tongue bracing occurs when the lateral edges of the tongue maintain contact with the palate or upper molars. Bracing is pervasive in speech: previous EPG data analysis shows lateral contact to be maintained for 97.5% of the total observation duration [Gick et al. (2017) JSLHR 60, 494]. This research has also indicated the presence of lateral bias or asymmetry in the tongue: a consistent tendency to release contact more on one side than the other, both in unilateral release and in sequential loss of contact during a bilateral release. However, in these cases of asymmetry, it remains unclear which side of the tongue should be considered dominant. The present study explores the question of dominance in lateral tongue bracing through biomechanical simulation of tongue muscle activation. Certain muscles have been identified as bracing agonists (GGP, GGM, MH, VERT, SL) and antagonists (GGA, STY, HG, TRANS, IL) [Liu et al. (2022) Phonetica 79, 523]. Agonists increase the likelihood of bilateral bracing and tend to raise or widen the tongue while antagonists decrease the likelihood of bilateral bracing and tend to lower or narrow the tongue. The current simulations explore whether a unilateral bracing outcome can be produced by asymmetric activation of agonists, antagonists or a combination thereof. Preliminary results from our simulation study show that reducing agonist activation or increasing antagonist activation on one side while keeping activations constant on the other side results in lower tongue-palate contact on the opposite side, due to the hydrostatic properties of the tongue, though the reduction is much greater in the former case. These results suggest that unilateral bracing may be instantiated primarily by contra-lateral muscle activation/deactivation from the side on which bracing contact is maintained, providing a starting point for further exploration of lateral tongue dominance. © 2023, Canadian Acoustical Association. All rights reserved.

关键词： Chemical activation

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