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An In-dept. Exploration of Advanced Driver Drowsiness Detection Systems for Enhanced Road Safety 2

An In-Depth Exploration of Advanced Driver Drowsiness Detect...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Abirami, A. Bhuvaneswari, S. Vishnuvardhan, M. Karthik, Yadam Shankar, Saisuraj Easwari Engineering College Dept. of Computer Science and Engineering Chennai India Student of Easwari Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

Drowsiness, among drivers, plays a role in causing road accidents leading to loss of life, injuries, and financial setbacks. To address the issue of driver fatigue related accidents it is crucial to develop methods for detecting driver fatigue. This survey paper presents an overview of approaches used to identify driver fatigue. These approaches can be grouped into three categories: Behavioural, Vehicular and Physiological parameters. Behavioural parameters, such as eye closure percentage, blink rate patterns, yawning, head movements and facial expressions offer intrusive techniques that examine the physical state of the driver's body and provide information about their drowsiness level. Vehicular parameter-based techniques evaluate drowsiness by observing vehicle behaviour such as lane deviations, pedal usage, and unpredictable steering movements. Physiological indicators are also employed to detect tiredness. The paper also discusses the methodology employed in the research study including image processing techniques classifiers used and measurement metrics utilized, proposed methodology of fatigue detection with adding some extra features and Lastly the paper presents conclusions based on a study that can guide future researchers in identifying potential areas for further exploration in this field. © 2024 IEEE.

关键词： Physiological models

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Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model's performance. Generated mashups used for evaluation can be found in https://***/***/. © 2024 IEEE.

关键词： Graph neural networks

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Analyzing Yelp Open-Source data-Set in Azure data-bricks 7

Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mugunth, D. Maheshwaran, R. Maheswari, M. Jany Shabu, S.L. Refonaa, J. Sathyabama Institute of Science And Technology Dept. of Computer Science And Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350359299

A data-driven application, Yelp has always served and will always serve as one. It is one of the first companies that allows local businesses to receive reviews from their customers. Communities have always developed it in cooperation with each other. According to Yelp, the data-set contains information regarding businesses, reviews, users, and check-ins, and has continuously been updated since 2015. A project titled "Analyzing Yelp Open-Source data-set in Azure data Bricks"will analyze this data-set which has been made available as an open source for sentiment analysis and descriptive analysis. This study analyzes local business performance, business distribution, review ratings, and other factors, as well as check-in rates in American business locations over time. This analysis shows that the Yelp is losing reviews, tips, elite users, and check-ins. © 2024 IEEE.

关键词： Brick

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Intensify Stroke Prediction Model Associated with Hypertensive State 3

Intensify Stroke Prediction Model Associated with Hypertensi...

引用

3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Islam, Md. Rabiul Rahman, Mst. Sahela Paul, Radha Rani Hosen, Md. Fahad Khairunnahar, Mst. Nayeem, Md. Julker Pundra University of Science & Technology Dept. of Computer Science & Engineering Bogura5800 Bangladesh

ISBN: (纸本)9798350388282

Hypertension and stroke are significant worldwide health issues with high prevalence and complex health hazards. Hypertensive state implications include death or major complications such as myocardial infarction. This study leverages data from the Internet to foresee the prognosis for individuals with hypertensive strokes. There are two main components to the clinical outcome prediction in this study. The value of various variables is ranked by our model through the use of cross-validation techniques, recursive feature disposal, and classification algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Our empirical cross-validation results demonstrate the remarkable predictive reliability of both LR and RF once repetitive features are removed, even with a smaller set of features and classifiers. Particularly in terms of accuracy, F1 score, and area under the receiver operating characteristic curve (AUC), logistic regression performs better than the other two classifiers. Likewise, we conducted independent prediction analyses for hypertension, hypertensive stroke, and stroke. The highest result of 96.15% is obtained using logistic regression for stroke prediction. The random forest classifier has an accuracy of 91.25% for hypertension, and combining stroke with a hypertension support vector classifier gives 91.72% accuracy. This demonstrates how well it might provide patients with hypertensive strokes with precise and trustworthy projections. Finally, our study illuminates the potentially useful prediction powers of particular algorithms in determining the prognosis of patients with hypertensive strokes. © 2024 IEEE.

关键词： Logistic regression

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Twitter Scraping and Sentiment Analysis on Stock Price

Twitter Scraping and Sentiment Analysis on Stock Price

引用

2024 International Conference on Advances in data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Iniyan, S. Thangakumar, J. Rajendran, P. Selvi Hindustan Institute Of Science And Technology Dept. Of Computer Science And Engineering Chennai603103 India

ISBN: (纸本)9798350364828

social media is useful in the community for sharing and discussing various events with others. The study's main objective is to predict the sign of the stock by sentimental analysis regarding the Hindenburg report on Adani group stocks by obtaining tweets from Twitter. The study's findings will aid in understanding the effect of the Hindenburg report on the stocks of the Adani group. Excel files are downloaded from Twitter containing tweets from social media users. Tweets are cleaned up by eliminating noise data after extraction. Machine learning algorithms, such as Logistic Regression (LR), Linear support classification (SVC), Random Forest (RF), and voting, were used to classify the data into positive, Neutral, and negative groups. © 2024 IEEE.

关键词： Logistic regression

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AI based Pentest For EHR and Other Health Monitoring Devices 5

AI based Pentest For EHR and Other Health Monitoring Devices

引用

5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kumaran, U. Gurupriya, M. Chinta, Hitesh Surya Maganti, Akshita Uma Sai Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Bangalore India Amrita School of Computing Dept. of Computer Science Bangalore India

ISBN: (纸本)9798331504403

The increasing reliance on Electronic Health Records (EHRs) and health monitoring devices has made healthcare systems vulnerable to cyberattacks. This research proposes an AI-based penetration testing framework to address these security concerns. By integrating machine learning techniques, our framework offers a more realistic approach to learning actual attack patterns, improving detection accuracy and response time compared to traditional methods. The framework's ability to minimize false positives and adapt to new threats demonstrates its effectiveness in strengthening healthcare system security. This research highlights the potential of AI-driven penetration testing in safeguarding patient information and protecting the integrity of healthcare systems. By identifying and addressing vulnerabilities, this framework can contribute to a more secure and resilient healthcare environment. © 2024 IEEE.

关键词： Electronic health record

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Power Control of Preamble Transmission for Grant-free Random Access in Cell-Free IoT Networks 15

Power Control of Preamble Transmission for Grant-free Random...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Youn, Jiseung Cho, Sunghyun Hanyang University Dept. of Computer Science & Engineering Seoul Korea Republic of Hanyang University ERICA Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (纸本)9798350364637

As Internet of Things (IoT) technologies are inves-tigated to support key 5G services, significant research efforts are being made to enable machine-type communication (MTC). Cell-free IoT (CF-IoT) systems, characterized by favorable prop-agation and macro diversity, are being studied in conjunction with grant-free random access (GFRA) as a potential enabler to achieve massive connectivity for MTC. However, existing GFRA schemes in CF-IoT transmit preambles at a fixed power level resulting in severe preamble collisions at nearby access points (APs). In this paper, we analyzes the analysis of preamble collisions in massive connectivity scenario and proposes a sim-ple power control scheme to address preamble collision. The proposed scheme mitigates preamble collisions by transmitting preambles only to the nearest AP in high-density access scenes. Simulation results show that the proposed method improves the random access success rate compare to existing GFRA method by 10%. © 2024 IEEE.

关键词： cell-free massive MIMO grant-free random access Internet of Things power control

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Blockchain Based Security System for Health Care 2

Blockchain Based Security System for Health Care

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ankit Singh, Rishipal Guru Jambheshwar University of Science & Technology Dept. of Computer Science & Engineering Hisar India

ISBN: (纸本)9798350383652

Security in healthcare systems based on blockchain technology is the subject of ongoing research. Block Chain technology has the potential to revolutionize many different sectors since it provides a decentralized, incontestable, and publicly verifiable record of every transaction activity. Bitcoin's foundational mechanism is referred to as 'block chain technology.' This technology allows for completely decentralized transaction processing. Healthcare aims to diagnose, treat, relieve, and cure any and all bodily and mental health issues, as well as to promote health and prevent sickness. Since all transactions in a blockchain are encrypted, it provides the highest level of security possible. Blockchain networks provide the same assurance of security for medical records. The integrity of the network's ledgers may be checked by checking file signatures on each node to make sure no alterations have been performed. The signature will no longer be valid when the record has been modified. The future of blockchain and big data applications is promising. Big data technology can manage any kind, amount, or speed of data, while blockchain enhances efficiency across all industries. It has been noticed that medical records are maintained on a blockchain for reasons of security. Proposed system has evaluated healthcare data for security. When compared to the results of cutting-edge research, however, the scalability and adaptability of traditional methods are clearly lacking. Better performance and security utilizing blockchain technology is essential, hence developing such a system is a priority. © 2024 IEEE.

关键词： Electronic health record

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A Transformer-Based Model for Image Caption Generation with Memory Enhancement

A Transformer-Based Model for Image Caption Generation with ...

引用

2024 IEEE International Women in Engineering (WIE) Conference on Electrical and computer Engineering, WIECON-ECE 2024

作者： Ome, Shadika Afroze Azhar, Tanvir Asaduzzaman East Delta University Dept. of Computer Science & Engineering Chattogram Bangladesh Chittagong University of Engineering & Technology Dept. of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798331535476

This study proposes a convolution-free transformer-based method for generating accurate descriptions of images. A Vision Transformer is utilized as the primary encoder, replacing traditional CNN, and a Meshed Memory Transformer is integrated for generating captions. The Vision Transformer splits images into fixed-size patches for extracting visual features, which are further processed by a memory-augmented encoder to integrate prior knowledge. It ensures key features are preserved and reused and the meshed decoder combines the text embedding with the outputs from each encoding layer to generate more contextually rich and accurate captions compared to conventional approaches. The model's performance is evaluated on the Flickr30K and the MSCOCO datasets, demonstrating that the Meshed Memory architecture in our approach improves both the image encoding and caption generation step. Our model outperforms the conventional 'CNN-LSTM' models and 'CNN-Transformer' methods, achieving a 1.02% improvement in METEOR and 1.31% in SPICE on the MSCOCO dataset. Additionally, on the Flickr30k dataset, our approach exhibits superior performance across most metrics, notably a 6.37% increase in the METEOR score. © 2024 IEEE.

关键词： Encoding (symbols)

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Localized Zeroth-Order Prompt Optimization 38

Localized Zeroth-Order Prompt Optimization

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Wenyang Shu, Yao Yu, Zongmin Wu, Zhaoxuan Lin, Xiaoqiang Dai, Zhongxiang Ng, See-Kiong Low, Bryan Kian Hsiang Dept. of Computer Science National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore China Singapore-MIT Alliance for Research and Technology Singapore School of Data Science The Chinese University of Hong Kong Shenzhen China

The efficacy of large language models (LLMs) in understanding and generating natural language has aroused a wide interest in developing prompt-based methods to harness the power of black-box LLMs. Existing methodologies usually prioritize a global optimization for finding the global optimum, which however will perform poorly in certain tasks under budget constraints. This thus motivates us to re-think the necessity of finding a global optimum in prompt optimization. To answer this, we conduct a thorough empirical study on prompt optimization and draw two major insights. Contrasting with the rarity of global optimum, local optima are usually prevalent and well-performed, which can be more worthwhile for efficient prompt optimization (Insight I). The choice of the input domain, including both the generation and the representation of prompts, affects the identification of well-performing local optima (Insight II). Inspired by these insights, we propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO), which incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency, which we demonstrate through extensive experiments. Our implementation is available at https://***/allen4747/ZOPO. © 2024 Neural information processing systems foundation. All rights reserved.

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