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GPT-3-Based AI Cover Letter Generator: A Feasibility Study & Implementation 5th

GPT-3-Based AI Cover Letter Generator: A Feasibility Study ...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Bloch, Tarannum Alimahmad Inusa, Ibrahim Marwadi University Gujarat Rajkot360003 India Universiti Tenaga Nasional Selangor Kajang43000 Malaysia

ISBN: (纸本)9789819788606

Natural Language processing (NLP) is the emerging field research studies of the interaction between human and computing systems. With advancement of NLP techniques, machines are becoming increasingly proficient in understanding, generating, and manipulating natural language. this study conducts in depth analysis on using the advanced natural language processing systems using Generative Pre-trained Transformer 3 (GPT-3) in AI cover letter generator, additionally;the paper discusses the ethical implications of using GPT-3-based AI cover letter generators in the job application process, as well as the potential benefits and drawbacks of using GPT-3-based AI cover letter generators for job applicants. the objective of this research paper is to assess the potential effects of incorporating GPT-3 in AI cover letter generation as well as any ethical issues that may arise. therefore we used research methodology that combines both methods: qualitative and quantitative research methods. the study was carried out in two phases. In the early phase, a qualitative content analysis was performed to examine the ethical implications of using GPT-3-based AI cover letter generators in the job application process and also highlight the potential benefits and drawbacks of using GPT-3-based AI cover letter generators for job applicants. In the second phase, a survey was conducted to generate various cover letters using GPT-3-based cover letter generators with a small sample size of data. To provide data to support that explore incorrect information that may occur when using a GPT-3-based cover letter generator, particularly if the company’s culture is not provided in the prompt. the information in this paper will help researchers better understand how artificial intelligence is used in the hiring process and potential effects it might have. the paper provides an introduction to cover letters and their importance in job applications, as well as an overview of natural language processing,

关键词： Natural language processing systems

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An Optical Micro-Displacement Monitoring sensor for Transmission Power Towers

An Optical Micro-Displacement Monitoring Sensor for Transmis...

引用

Smart Grid and Energy Engineering (SGEE), international conference on

作者： Wanli Yang Weigang Liu Xuheng Ma Yongming Zhu Kai Feng Tao Han Kaiji Qiang Zhangtian Li State Grid Xinjiang Electric Power Co. Ltd Changji Electric Power Supply Company Changji China School of Electrical Engineering Xi'an University of Technology Xi'an China

ISBN: (数字)9798331530686

ISBN: (纸本)9798331530693

Transmission power towers (TPT) represent a crucial component of the power delivery systems. Many damaging events of TPT triggered by storms, earthquakes, ice and corrosion have been recorded worldwide, resulting in significant financial losses. therefore, domestic and foreign scholars actively promote the scientific theoretical research and practical engineering project implementation of TPT monitoring, safety assessment and disaster prevention. this paper develops a novel optical-based micro-displacement sensor for monitoring the displacement of transmission power towers. the millimeter-scale measurement range of the sensor addresses the low measurement accuracy of the existing displacement sensor. the gradual application of the new wireless and optical innovation methods provides an essential reference for the realm of micro-displacement sensors.

关键词： Optical fibers Integrated optics Wireless communication Optical fiber sensors Wireless sensor networks Ultraviolet sources Poles and towers Displacement measurement Optical signal processing Monitoring

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DualGGAN: A New Facial Style Transfer Network 5

DualGGAN: A New Facial Style Transfer Network

引用

2023 5th international conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Zhao, Yu Peng, Chunyan Zhang, Xiaojuan School of Computer Science Qinghai Normal University Xi'ning810016 China State Key Laboratory of Intelligent Processing of Tibetan Language Qinghai Xi'ning810016 China

In response to the background penetration problem of unsupervised style transfer algorithms in most cases, a Transformer style transfer network DualGGAN based on dual generators and fusion of relative position encoding is proposed. the network is trained using the least squares generative adversarial network, and the neural network is used as the image feature extractor to generate feature maps to obtain facial image features with attention weights from the feature maps, utilizing relative position encoding and mask loss to jointly constrain feature region style transfer. the experimental results show that the DualGGAN network effectively reduces artifact generation when implementing facial style transfer, maintains good background consistency, and has good generalization ability. Experiments have shown that the FID and KID indicators on the cat2dog and older2adult datasets are significantly improved compared to other algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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Alzheimer's Disease Detection Using Ensemble Learning and Artificial Neural networks 5th

Alzheimer's Disease Detection Using Ensemble Learning and Ar...

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5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Bandyopadhyay, Ahana Ghosh, Sourodip Bose, Moinak Singh, Arun Othmani, Alice Santosh, K. C. Appl AI Res Lab Vermillion SD 57069 USA Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France Univ South Dakota Basic Biomed Sci Vermillion SD 57069 USA Univ South Dakota Dept Comp Sci Appl AI Res Lab Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

this paper presents an ensemble method using machine learning classification algorithms and an artificial neural network-based scheme using the popular and widely used open access series of imaging studies (OASIS) dataset for Alzheimer's disease (AD) detection. the proposed work performs an in-depth feature examination and a training-test split in a 70 : 30 ratio on the dataset and applies 8 different ML algorithms. the AD detection outcome is obtained using two procedures, first by an ensemble approach applied to different machine learning algorithms, and secondly by using an artificial neural network (ANN). the use of ANN achieves an overall test accuracy of 0.9196 whereas two ensemble techniques, namely gradient boosting and voting classifier achieve an overall test accuracy of 0.857 and 0.8304. the precision and sensitivity scores demonstrate the superior detection performance of the ANN over the ensemble method on ML algorithms.

关键词： Alzheimer's disease detection Machine learning ANN Ensemble learning

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Spatiotemporal Features Recognition and Prediction of Urban Hot Spots Based on Trajectory Data 5

Spatiotemporal Features Recognition and Prediction of Urban ...

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5th international conference on Electronic Engineering and Informatics, ICEEI 2023

作者： Cao, Yu Sun, Kai School of Artificial Intelligence Application Shanghai Urban Construction Vocational College Shanghai201415 China Science and Technology Department Shanghai Urban Construction Vocational College Shanghai201415 China

ISBN: (纸本)9781643685021

With the continuous progress of urbanization, the construction of cities is gradually accelerated, and the urban population density is constantly increasing. Hot spots represent regions with more trips, greater population flow and higher travel demand. With the increasing complexity of traffic data, traditional methods for predicting hot spots can no longer meet the current situation. this also provides opportunities for the development of deep learning techniques. Given the continuous expansion in scale and intricacy of trajectory data, its relevance has become increasingly significant in domains such as urban planning, traffic management, and business decision-making. Aiming at the hot spot identification problem of trajectory data, this paper, inspired by the traffic prediction model, applied the STGCN model to the urban hot spot prediction problem and improved it. the model consists of four parts: cluster processing, spatial feature extraction, time feature extraction and hot spot region prediction. the experimental outcomes demonstrate that the model exhibits superior predictive performance across diverse datasets compared to conventional methods, showcasing robust generalization capabilities and practical applicability. © 2024 the Authors.

关键词： Recurrent neural networks

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Non-invasive Technique for Detecting Glycosuria through Image processing and Deep Learning Approaches 5th

Non-invasive Technique for Detecting Glycosuria Through Imag...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Teja, Chitturi Siva Nitheesha, Gokaraju Kumar, Boyidi Ravi Krishna, Bandaru Radha Naik, Desavath Madhu Pareek, Prakash Singh, Lokendra Department of ECE Vishnu Institute of Technology Bhimavaram534202 India Dehradun248002 India

ISBN: (纸本)9783031770746

Glycosuria level monitoring is a fundamental aspect of diabetes management, enabling proactive and personalized care to prevent complications, improve overall health, and enhance the quality of life for individuals with diabetes. Conventional glycosuria monitoring methods often involve invasive procedures, potentially risking the immune system, especially with repeated injections. the research focuses on a non-invasive method utilizing urine samples for glycosuria testing to address these concerns. the proposed method integrates image processing techniques, such as Gaussian filtering and image resizing, to optimize input data with machine learning approaches. Deep learning, especially convolutional neural networks is employed for their ability in feature extraction and pattern recognition. the system aims to achieve high accuracy in categorizing glycosuria levels into groups such as diabetes, prediabetes, and normal, providing a reliable and non-invasive alternative to traditional glycosuria monitoring techniques. this approach shows promise in enhancing patient compliance and overall health monitoring for individuals requiring regular glucose assessment. the accuracy of the proposed method based on CNN achieves 98.54% where the existing method based on support vector machine (SVM) achieves only 88.46%. it is evident from simulation results, the proposed method attained 11.39% improved accuracy when compared to existing SVM. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Accuracy Classification Convolutional Neural Network Deep learning gaussian filter Glycosuria

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Unveiling Stock Market Trends through Predictive Analytics and Sentiment Analysis: InsightfulEquity 5

Unveiling Stock Market Trends Through Predictive Analytics a...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Gupta, Amit Kumar Kumar, Vipin Verma, Ankit Yadav, Prakriti Kumar, Nikhil Sain, Mangal Kiet Group of Institutions Department of Computer Applications Delhi NCR India Integrated Program in Management Indian Institute of Management Indore Madhya Pradesh Indore India Dongseo University Division of Computer and Information Engineering Busan Korea Republic of

ISBN: (纸本)9798350383522

In the world of economic markets, it is nevertheless now not clean to as it should be decide stock expenses. Our research objectives to go deeper into financial savings forecasting by way of developing innovative forecasting strategies and superior emotional guide assessment techniques. By combining those techniques, we hope to better recognize the complicated dating between stock prices and social media sentiment. the three fashions blanketed in our evaluation are Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM) networks, and Linear Regression. In the present examine, we use statistical trying out, regression analysis, and correlation models to have a look at the predictive properties of these fashions at special time points and reveal unique accuracy indices for every. On the other hand, every other extensively used method of analyzing marketplace sentiment is sentiment evaluation, which explains to us how stock price volatility reacts to human beings's sentiment In economic evaluation, our examine highlights the significance of predictive modeling and sensitivity analysis through showing that they are correlated. Using this fusion, we can make better predictions in the stock market, helping participants make informed decisions based on well-analyzed data. © 2024 IEEE.

关键词： Financial Analytics Machine Learning Natural Language processing Sentiment Analysis Stock Market Prediction

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Improving Diagnostic Accuracy of Rheumatoid Arthritic by Image Generation Utilizing GAN 5

Improving Diagnostic Accuracy of Rheumatoid Arthritic by Ima...

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5th international conference on Digital Medicine and Image processing, DMIP 2022

作者： Kojima, Reo Goto, Tomio Nagoya Institute of Technology Nagoya Japan

ISBN: (纸本)9781450397643

Rheumatoid arthritis is a disease that causes inflammation of joints, destruction of cartilage and bone, loss of joint function, and joint deformation if left untreated. therefore, this paper proposes a system for detecting abnormalities in rheumatoid arthritis using X-ray images of joints. In addition, we aim to solve the problem that X-ray images of joints, which are medical images, are difficult to obtain, and to improve the accuracy of the diagnosis system and the validity of our system by generating images using GANs. © 2022 ACM.

关键词： Generative adversarial networks

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Performance Improvements of Covert Timing Channel Detection in the Era of Artificial Intelligence 5th

Performance Improvements of Covert Timing Channel Detection ...

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5th international conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Massimi, Federica Benedetto, Francesco P4TE- Signal Processing for Telecommunications and Economics Laboratory Economics Department University of ROMA TRE Via Silvio D’Amico 77 Rome00145 Italy

ISBN: (纸本)9789819718405

We are now living in the digital era, where network security is one of the fundamental blocks of communication systems. Digital society, digital economy, and digital communications are now realities in our everyday lives, where sensible data are exchanged through every kind of digital media. Cyber-attacks’ detection methods have hence gained more and more importance to guarantee the system’s security policies, as well to avoid malicious intrusions. this work discusses the application of deep learning (DL) techniques to detect security threats, namely covert timing channels. To evaluate the various deep learning methods that we have explored, we conduct a network attack simulation by manipulating inter-arrival packet delays, effectively creating a covert timing channel. Subsequently, we introduce six customized deep learning frameworks tailored to our specific objectives, designed to differentiate between the presence and absence of a cyber-attack in the data. Our results and comparisons demonstrate the feasibility, robustness, and superiority of DL approaches for improving the network security by detection malicious intrusions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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Vital Signs Monitoring Using FMCW Millimeter-Wave sensor via Simulated Annealing MUSIC Algorithm 5

Vital Signs Monitoring Using FMCW Millimeter-Wave Sensor via...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Wang, Yuqi Wang, Xianpeng Lan, Xiang Han, Zhiguang College of Information and Communication Engineering State Key Laboratory of Marine Resource Utilization in South China Sea Haikou China Hainan University State Key Laboratory of Marine Resource Utilization in South China Sea Haikou China

ISBN: (纸本)9781665485890

In this paper, we describe an algorithm for frequency modulated continuous wave (FMCW) radar to monitor vital signs. the FMCW waveforms have inherent range-gating capability and are sensitive to small object displacements. Based on the advantages, we use the traditional direction-of-arrival (DOA) estimation algorithm-multiple signal classification (MUSIC) algorithm to monitor vital signs. the simulated annealing algorithm is applied to solve the raster search problem of MUSIC algorithm and improves its accuracy. Simulation results show that this algorithm is superior to traditional FFT algorithm. the feasibility of the algorithm is verified by processing the received data of FMCW sensor. © 2022 IEEE.

关键词： Simulated annealing

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