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2023 4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023 - proceedings

2023 4th International Conference on Communication Systems, ...

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4th international conference on Communication Systems, Computing and IT applications, CSCITA 2023

ISBN: (纸本)9781665459877

the proceedings contain 43 papers. the topics discussed include: deep learning based Marathi sentence recognition using Devanagari character identification;deep learning based Marathi sentence recognition using Devanagari character identification;efficient detection of small and complex objects for autonomous driving using deep learning;implementation of exploratory data analysis on weather data;deep learning model for simulating self-driving car;summarization of video clips using subtitles;reliability stripe coagulation in two failure tolerant storage arrays;efficient video anomaly detection using residual variational autoencoder;situational portfolio forecasting and allocation with deep-learning approach;and recognition of emotions based on facial expressions using bidirectional long-short-term memory and machine learning techniques.

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Road Target Detection Method Based on Improved YOLOv8 4

Road Target Detection Method Based on Improved YOLOv8

引用

4th IEEE international conference on Power, Electronics and computer applications, ICPECA 2024

作者： Chen, Yuejun School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798350359589

With the rapid development of artificial intelligence technology, machine vision, as one of its key branches, has shown great application potential and value in many fields such as industrial automation, intelligent security, unmanned driving, and medical diagnosis. In this context, target detection, as the core task of machine vision, aims to accurately identify and locate objects of interest from complex scene images. For unmanned driving technology, target detection technology is very important, and whether it can accurately judge road targets determines whether unmanned driving can be realized. With the breakthrough progress of deep learning technology, many target detection algorithms have emerged, and the target detection performance has realized significant improvement in both accuracy and speed. However, there are still some shortcomings such as difficulty in detecting small targets and lack of robustness. In this thesis, a variety of driving scene datasets with weather and location are selected, and the YOLOv8 algorithm is optimized with the BRA algorithm, and its effectiveness is verified after comparing it with a variety of other algorithms. © 2024 IEEE.

关键词： deep learning target detection YOLOv8

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Streamlining Financial Predictions: A Web application for Stock, Cryptocurrency, and Market Sentiment Analysis 4

Streamlining Financial Predictions: A Web Application for St...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Vivekanand, Chettiyar Vani Kumar, P. Amizhthini, A. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. the traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. this study will propose a web application that uses advanced time series models to try and overcome such challenges. the system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models;Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis;Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting;Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets;and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. this solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. the goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model. © 2025 IEEE.

关键词： Ethereum

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the application of machine learning Techniques to Evaluate the Impact of Transmission Line compensation on Fault Classification 4

The Application of Machine Learning Techniques to Evaluate t...

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4th international conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Mazibuko, N. Akindeji, K.T. Moloi, K. Department of Electrical Power Engineering South Africa Department of electronics and computer engineering South Africa Durban University of Technology Smartgrid Research Center Steve Biko Campus Durban South Africa

ISBN: (纸本)9798350353266

the use of machine learning represents a significant advancement in the field of detecting defects in transmission lines. this study looks at how machine learning methods can be used to analyze the impacts of compensation in transmission lines in fault detection and classification. the main focuses is on the misclassification percentage based on different types of faults, including Line to Line (L-L), Line to Ground (L-G), Double Line to Ground (L-L-G), and three-phase faults (L-L-L). the study shows that introducing series capacitors in the fault loop will affects the impedance seen by the relay, and makes the fault classification task more challenges as the compensation level increases. see for line compensation alters the impedance measured by relays, resulting in a more complex fault classification technique as compensation levels increases. the first model is a two-bus system developed to test the R/X response at different types of faults. there after transmission line compensation is added at 50% and 100% compensation. Data is collected from these models and implemented on machine learning based classifier to assess the fault classification accuracy in the presence of transmission line compensation. It is concluded that transmission line compensation does affects the performance of the protection system and three phase faults are the most affected in terms of accuracy when the capacitive reactance (introduced by compensation) is present in the fault loop. © 2024 IEEE.

关键词： Adversarial machine learning

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Bibliometric Analysis of Trends and Directions in machine learning Applied to FinTech 4

Bibliometric Analysis of Trends and Directions in Machine Le...

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4th international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2024

作者： Arif, Erman Suherman, Suherman Widodo, Aris Puji Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350378368

Our article presents a comprehensive analysis of trends and directions in the application of machine learning in the FinTech sector. By utilizing data from academic databases such as Scopus, our objective is to showcase the advancements in this interdisciplinary research area. the study identifies key trends, influential authors, leading journals, and significant collaborative networks that shape the field. the methodology employed involves quantitative bibliometric analysis to evaluate publications from the past five years. the results indicate a substantial increase in the number of studies during this period, highlighting the importance of machine learning in addressing critical financial challenges, including fraud detection, algorithmic trading, credit scoring, and risk management. Additionally, we delve into the main themes, countries, and institutions that dominate the current research agenda. this analysis provides valuable insights into the present state and future direction of machine learning in FinTech, serving as a resource for researchers and practitioners seeking to comprehend and contribute to this dynamic and rapidly evolving field. We also discuss implications for future research, emphasizing the necessity for continuous innovation and interdisciplinary collaboration to maximize the potential of machine learning in transforming financial services. © 2024 IEEE.

关键词： Adversarial machine learning

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Research on credit-risk models via machine-learning algorithms and logistic regression for predicting CBA consumer behaviour 4

Research on credit-risk models via machine-learning algorith...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Fang, Ce Bu, Te Fang, Fang International College Krirk University Bangkok10220 Thailand

ISBN: (纸本)9798350342710

Start with the pre-screening of a possible application and continuing through the account's administration during its life and potential write-off, risk assessment techniques are used to handle things a borrower's account. Typically, a logistic regression model is used to estimate how risky it is to lend money to a credit applicant. However, researchers have also looked at a variety of other classifiers, and while early results indicate that support vector machines are the most accurate, practical implementation of these results from the lab may be hampered by issues with data quality. With a regression-based R2 values of forecasted/realized tardiness of 85%, this article generates out-of-sample predictions that greatly increase the categorization rates of credit-card holder defaults and delinquencies. We estimate the cost reductions to range from 6% to 25% of total losses using conservative estimates for the costs and benefits of reducing credit lines based on machine-learning projections. © 2023 IEEE.

关键词： Cost reduction

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2023 4th international conference on Computing, Mathematics and Engineering Technologies: Sustainable Technologies for Socio-Economic Development, iCoMET 2023

2023 4th International Conference on Computing, Mathematics ...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

ISBN: (纸本)9798350335316

the proceedings contain 80 papers. the topics discussed include: a comparative survey of solutions to Russel’s paradox;towards a protein-protein interactions framework using graph analytics on Apache spark;social media competitive analysis: a case study of laptop brands;promising features of wind energy: a glance overview;image caption generation related to object detection and color recognition using transformer-decoder;electrical energy audit and analysis of energy conservation opportunities at university buildings;vehicle response control during stopping a train with dynamics of the train and driver;broadband computer-generated holography (CGH)-based Bessel beam generation;a survey on machine learning approaches in cryptocurrency: challenges and opportunities;performance analysis of outer rotor flux reversal machine at different rotor poles;a competitive numerical model for virus transmission in a computer network;the influential factors for pollution emissions in manufacturing business;and implications of energy management practices to reduce carbon emissions in Pakistan manufacturing industries.

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Chili Plant Health Status Classification Using Random Forest 4

Chili Plant Health Status Classification Using Random Forest

引用

4th international conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Tan, Julius Sutanto, Bryant Christoper Marvelim, Darren Pratama, Galih Dea Hasana, Silviya School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350318432

there are numerous agricultural products in the world, and chili is among the most important ones due to its role in adding spice to food, particularly in Indonesia. Consequently, diseases affecting chili plants can lead to significant economic losses for both farmers and the nation. However, overseeing the health status of all chili plants requires a substantial amount of time and manpower resources. Currently, machine learning offers a solution to efficiently detect chili diseases. this paper presents a method for detecting and classifying chili plant diseases using the Random Forest Algorithm. the aim is to distinguish between healthy plants and four different classes of chili diseases. the dataset employed comprises 40 images for each disease type, classified as either chili diseases or unclassified ones, which were used to train the system for accuracy. As a result, our proposed model has achieved an accuracy rate of 95% in classifying the health status of chili plants. © 2023 IEEE.

关键词： machine learning

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General Agent theory of Mind: Preliminary Investigations and Vision 4th

General Agent Theory of Mind: Preliminary Investigations and...

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4th international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 25th international conference on Human-computer Interaction (HCII)

作者： Kumar, Prabhat Raglin, Adrienne Richardson, John US DEVCOM Army Res Lab Adelphi MD 20783 USA

ISBN: (纸本)9783031358937;9783031358944

A theory of Mind (ToM) is a mental representation one agent has of another's emotion, desires, beliefs, and intentions formed through their interactions which help the agent predict the other's behaviors. the concept comes from work in cognitive science which addresses questions about the mechanism for inferring motivations behind human behavior. We aim to apply this concept to understand the degree to which we can impart ToM capabilities to artificial agents. While we do not aim to resolve the depths of human emotions, desires, and beliefs, we hope to recreate a proof-of-concept from a recent machine learning application and later scale to more realistic contexts.

关键词： cognitive science machine learning theory of mind reasoning artificial intelligence artificial reasoning

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QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancement: Fidelity-Improved Data Optimization in Quantum-Classical Systems 4

QEFMLA-FIDO: Quantum Evolution Formulation for ML Advancemen...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Kundu, Sourodeep Mishra, Deepti Tarasia, Nachiketa Barik, Rabindra Kumar School of Computer Engineering KIIT Deemed to Be University Bhubaneshwar India School of Economics and Commerce KIIT Deemed to Be University Bhubaneshwar India School of Computer Application KIIT Deemed to Be University Bhubaneshwar India

ISBN: (纸本)9798331528171

Using machine learning to improve quantum algorithms or quantum mechanics to improve machine learning is known as quantum machine learning. In order to include quantum physics into machine learning, we provide a unique model (QEFMLA-FIDO) in this research. the way an artificial neural network simulates the human brain is comparable to this method. Our suggested quantum-inspired technique can accelerate computationally challenging issues when executed on traditional hardware. Additionally, the method may easily be modified to operate on quantum technology, offering a far faster speedup than is achievable on conventional hardware. the Quantum Evolution Algorithm (QEA) improves computing efficiency by operating on both classical and quantum hardware and is motivated by the general development of quantum states. the technique solves complicated problems more quickly than conventional methods by iteratively evolving a system's Hamil-tonian to suit training data. It effectively enhances parameters by optimising the Quantum Fisher Information Matrix (QFIM). the QEA is suitable in real-world situations because it uses quantum parallelism to minimise computing complexity and is noise- resistant. It also has a predictive function that can detect improper outputs before they happen. © 2024 IEEE.

关键词： Hamiltonians

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