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SIST 2024 - 2024 ieee 4th international conference on Smart Information systems and Technologies, Proceedings

SIST 2024 - 2024 IEEE 4th International Conference on Smart ...

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4th ieee international conference on Smart Information systems and Technologies, SIST 2024

ISBN: (纸本)9798350374865

The proceedings contain 110 papers. The topics discussed include: review of machine learning models in cyberbullying detection problem;quantum computing in the object-oriented model of quality management;regression and machine learning methods for predicting human movements based on skeletal data;application of game theory methods to optimize the stakeholder management process;conceptualizing the ICT project management in the sustainability context;human-computer testing of students’ knowledge;fuzzy inference system for test case prioritization in software testing;mobile application of convolutional neural networks for melanoma classification;an investigation of sensing and technologies for supporting the intelligent transport management system in urban area;and a comparative study of supervised machine learning and deep learning techniques with feature selection methods for classifying Parkinson’s disease based on speech impairments.

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A Smart Agriculture Friendly intelligent Flying Robot Design to Identify Plant Leaf Diseases by using Digital Image Processing with Learning Approach

A Smart Agriculture Friendly Intelligent Flying Robot Design...

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2023 international conference on Innovative computing, intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Kavitha, M.S. Harikrishnan, R. Shankar, S. Vijay Krishnakumari, S. Sherly, S. Irin R.M.K.Engineering College Department ECE Chennai India S.A. Engineering College Department of ECE Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Loyola ICAM College of Engineering and Technology Department of ECE Chennai India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350319200

Smart agriculture practices have become crucial in meeting the growing global food demand while minimizing resource use. In this context, the development of intelligent systems and automation plays a pivotal role. This paper introduces a novel solution: a smart agriculture-friendly intelligent flying robot equipped with cutting-edge digital image processing techniques and a learning approach to identify plant leaf diseases. The suggested system merges state-of-the-art robotics and aerial technology with advanced machine learning techniques, specifically the VGG-SVM model. The flying robot is equipped with a high-resolution camera to capture images of plant leaves in real-time, and the VGG-SVM model processes these images for disease detection. VGG offers deep feature extraction capabilities, while SVM provides accurate classification, making the model adept at discerning healthy from diseased leaves. Through digital image processing, the robot captures high-resolution images of plant leaves, which are subsequently subjected to advanced machine learning algorithms. These algorithms analyze and classify the images, providing real-time insights into the health of crops. The combination of precision aerial data collection and machine learning ensures early disease detection and targeted intervention, optimizing crop yield, reducing resource consumption, and ultimately contributing to sustainable agriculture. Proposed model excels with the highest accuracy of 0.97, indicating an overall high rate of correct predictions. © 2023 ieee.

关键词： Deep Learning Drones Plant Leaf Diseases SVM VGG

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Research on the Automated Decision-Making Effectiveness in Industrial Automation System 5

Research on the Automated Decision-Making Effectiveness in I...

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5th ieee international conference on advanced Information and Communication Technologies, AICT 2023

作者： Klymash, Mykhailo Hordiichuk-Bublivska, Olena Kyryk, Marian Nodzhak, Luybov Fabri, Liudvih Lviv Polytechnic National University Department of Telecommunication Lviv Ukraine Lviv Polytechnic National University Department of Foreign Trade and Customs Lviv Ukraine Lviv Polytechnic National University Department of Automated Control Systems Lviv Ukraine

ISBN: (纸本)9798350372571

The problems of automating production processes using the intelligent analysis of big data were discussed in the paper. Existing methods of processing information from various devices, and determining data patterns or regularities were studied. The effectiveness of the SVD and Funk SVD algorithms for processing big data was determined. A modified Funk SVD data analysis algorithm was proposed, which provides distributed calculations, and exchanges the results between local nodes in a safe way. The research results of the proposed algorithm demonstrate its high speed and accuracy. The recommender model for improving the Industrial Automation systems' efficiency based on the modified method was proposed. © 2023 ieee.

关键词： Big data

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Employing a Machine Learning-Based Approach for Enhanced Big data Analysis in Supply Chain Management

Employing a Machine Learning-Based Approach for Enhanced Big...

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2024 international conference on Advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Pradeepa, M. Sumathi, V. Ponnusamy, Sivakumar Suneetha, G. Singh, Gurjeet Kruthika, P. School Of Computer Science Engineering And Information Systems Vellore Institute Of Technology Tamil Nadu Vellore632 014 India Sri Sai Ram Engineering College Department Of Mathematics West Tambaram Tamil Nadu Chennai44 India Ksr College Of Engineering Department Of Computer Science And Engineering Namakkal TamilNadu Tiruchengode India Koneru Lakshmaiah Education Foundation Department Of Cse Andhra Pradesh Vaddeswaram India Ies College Of Technology Ies University Bhopal Department Of Mechanical Engineering Madhya Pradesh India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India

ISBN: (纸本)9798350364828

This study uses machine learning models as potent analytical tools to look into the issue of inventory cost and profit prediction in the automobile industry. Throughout a ten-year dataset from 2012 to 2023, the study carefully analyses several cost components and profit data, illuminating the complex relationships between these elements. To provide insights into inventory dynamics, the Convolutional Neural Network (CNN), k-nearest Neighbours (KNN), and Support Vector Machine (SVM) models are rigorously trained and tested. An impressive feat is achieved by the CNN model, which achieves a phenomenal accuracy of 98.7%, followed by KNN at 96.7% and SVM at 93.4%. Additional proof of the models' propensity to make accurate predictions comes from their precision, recall, F1 score, and confusion matrices. The research provides data-driven insights that players in the dynamic vehicle industry can use to improve inventory management procedures and financial results. This work serves as an example of how machine learning may fundamentally alter how people make decisions in this difficult domain. © 2024 ieee.

关键词： Forecasting

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Natural Language Processing and Collocations for Generating Enhanced Health data Insights 4

Natural Language Processing and Collocations for Generating ...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Nooji, Pavitha Savithramma, R.M. Ashwini, B.P. Pinto, Merwin Vishwakarma University Department of Artificial Intelligence Maharashtra Pune India Siddaganga Institute of Technology Vtu Department Of Cse Karnataka India

ISBN: (纸本)9798331529635

This research study explains the key factors of how Natural Language Processing and Collocations are used to improve the Health industrial sector by detecting medical terms and words to enhance healthcare industrial applications. Collocations are types of words that are seen very commonly appear together in a text corpus, each of them contributes to relationships between various medical terms for example a disease name or even a symptom occurrence, natural language processing techniques involve creating tokens, removing stopwords, tagging various types of speeches, lemmatizing words, or even stemming for an instance. The model employs a statistical method such as Pointwise Mutual Information or Chi-square to identify meaningful pairs of words in the medical context. Various metrics such as precision, recall, and F1 scores gave a good insight of the model performance to predict the terms in the medical text. The model would capture all expected complex medical terms and help gain proper insights from the medical context by especially picking out the collocations. The model is known for its relevance making it a powerful tool for healthcare units and advancing healthcare research. © 2024 ieee.

关键词： Natural language processing systems

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Conversational Interface for Textual and Visual data Interpretation using customized CNN

Conversational Interface for Textual and Visual Data Interpr...

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2024 international conference on computing and intelligent Reality Technologies, ICCIRT 2024

作者： Bharti, Devang Kumar Bhandari, Shikhar Shukla, Shikhar Koul, Apeksha School of Computer Science and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331510299

This paper proposes a novel multimodal chatbot for continuous text and vision processing to overcome the constraints of current text-only based interacting agents. While early systems like ELIZA (1966) and PARRY (1972) introduced basic conversational abilities, modern chatbots remain largely text based. To address this difficulty, we introduce "Salvatore" chatbot comprised of a uniquely designed CNN system to quickly analyze the text and images. For text context, we employ the Lemmatization and Tokenization methods, and in the case of images, Gaussian fluctuation, convolutions, binarization theorems, Canny edge detection, gradients, resizing dimensions, and pixels area average algorithms. All the functions tested on the model returned the expected results with 85% accuracy and 80% precision in case of object identification tasks. It operated without considerable obstacles, no lag time as well as sign of regression is observed across various versions. Further, fine-tuning builds the interactive ability continuously, and as such, this chatbot provides reliable interfaces for multi-modal dialogues. © 2024 ieee.

关键词： Chatbots

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Enhancing PVD Process Control Through LabVIEW Automation 2

Enhancing PVD Process Control Through LabVIEW Automation

引用

2nd international conference on Recent Trends in Microelectronics, Automation, computing, and Communications systems, ICMACC 2024

作者： Sanikommu, Nirmala Aruna, N. Valleti, Krishna Hyderabad India

ISBN: (纸本)9798350366570

In this research work, a novel Virtual Instrument (VI) for process control, monitoring, and data acquisition was created using Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software, distinguishing traditional hardware instrumentation from Virtual Instruments. A High-Power Impulse Magnetron Sputtering (HiPIMS) Physical Vapour Deposition (PVD) system has been developed to deposit coatings on a variety of substrates by controlling the process and related subsystems via graphical programming in the LabVIEW environment. It automates the thin film deposition process by sequentially controlling the integrated subsystems. The program also regulates the critical thin film deposition process parameters, including deposition pressure, substrate temperature, substrate rotation, and substrate bias. An upstream pressure control and Proportional Integral Derivative (PID) function to achieve the desired pressure. The results showed accurate pressure control (± 0.0005 mbar) within an operating range of 5×10-3 - 5×10-2 mbar. Furthermore, the deposited thin films demonstrated good repeatability and reliability. The presented results will be valuable in developing indigenous custom-made vacuum or PVD systems. © 2024 ieee.

关键词： Two term control systems

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An Improved Bayesian Learner Based on Optimized Kernel Density Estimation and Wasserstein Soft Ensemble Strategy

An Improved Bayesian Learner Based on Optimized Kernel Densi...

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ieee international Workshop on intelligent data acquisition and advanced computing systems: Technology and Applications

作者： Zhiwei Ye Yuanhu Liu Jiazhi Lv Yingying Liu Zhenwei Wu Wanfang Bai School of Computer Science Hubei University of Technology China Fiberhome Telecommunication Technologies Co Ltd China Xining Big Data Service Administration China

Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the traditional Bayesian classifier in that the conditional possibility density is calculated by an assumed statistical model, our method estimated the possibility value without model assumption, they are obtained by kernel density estimator with optimized window width and the Wasserstein distance is used to solve the problem of how to cause the classifier difference in the integration process. The optimized window parameters are solveded by minimizing the Unbias Cross-Validation (UCV) objective function using Chaos-Particle Swarm Optimization (CPSO). Wasserstein distance is innovational used to evaluate the similarity between possibility distributions and generates weights of base classifiers. The final ensemble outputs are calculated by a soft voting strategy. The experimental results illustrate the effectiveness and improvements of the proposed method in terms of density curve fitting, ensemble ability, and overall classification accuracy.

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The SEIR Model for Predicting Malware Propagation in Computer Networks 5

The SEIR Model for Predicting Malware Propagation in Compute...

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5th international conference on intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Kharabsheh, Mohammad Al-Aiash, Issa Mughaid, Ala Almiani, Muder The Hashemite University Faculty of Prince Al-Hussien bin Abdullah II for IT Department of Computer Information Systems Department Zarqa13133 Jordan Gulf University of Science and Technology Kuwait

ISBN: (纸本)9798350354690

The propagation of infectious diseases has long been mathematically modeled to estimate their spread within populations, a concept increasingly applied to cybersecurity to analyze the spread of malware in computer networks. This paper investigates the use of an epidemiological framework, the Susceptible-Exposed-Infected-Recovered (SEIR) model, to predict the spread of malware across computer networks. The model parameters are modified to represent the infection rate and the susceptibility of nodes to be infected with malware, which allows the model to be adapted to various network topologies and how it can simulate the spread of mal ware over time in a specific network making the model a valuable tool for cybersecurity professionals enabling them to anticipate 0 demonstrate the model's potential, synthetic data was generated and used in the simulations, showcasing the model's capability to predict malware propagation within a network. These insights can enhance the strategic deployment of security interventions. © 2024 ieee.

关键词： Cyber attacks

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Bayesian-Informed Hyperdimensional Learning for intelligent and Efficient data Processing 24

Bayesian-Informed Hyperdimensional Learning for Intelligent ...

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43rd international conference on Computer-Aided Design, ICCAD 2024

作者： Barkam, Hamza Errahmouni Das, Tamoghno Poduval, Prathyush Jeong, SungHeon Yeung, Calvin Solitan, Mostafa Imani, Mohsen University of California Irvine IrvineCA United States

ISBN: (纸本)9798400710773

In machine learning (ML), near-sensor AI is transforming edge computing by reducing response times and data transmission, ultimately saving energy and bandwidth. Despite challenges like limited computational resources and the need for transparent decision-making, this approach aims to enhance the intelligence and autonomy of edge devices. Our research presents a novel framework that adds a layer of abstract intelligence to sensors, boosting system efficiency and accuracy through transparent, interpretable sub-symbolic AI. We combine Bayesian algorithms with hyperdimensional computing (HDC), inspired by the human brain’s operational efficiency, to deliver an energy-efficient solution matching the accuracy of traditional cloud systems without constant server dependence. This framework uses a binary classifier with Bayesian insights to choose the best data processing location—locally or in the cloud—adapting to data environments. Our method ensures cloud-level performance while significantly reducing energy consumption, improving the sustainability of sensor-based systems. It also enables continual adaptation and learning directly at the sensor level, enriching cloud models with fresh edge insights. Our results have shown to bridge the gap from around 38% quality loss between the standalone near-sensor HDC model and the SOTA cloud-based model to improve the quality loss to only 9% while simultaneously saving 45.34% of energy by not using the cloud. This framework paves the way for more sustainable, efficient, and accurate edge computing in the ML landscape by bridging the gap between simple near-sensor models and their advanced cloud-based counterparts. © 2024 Copyright is held by the owner/author(s).

关键词： data reduction

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