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检索条件"任意字段=2nd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2023"

共 37 条记录，以下是21-30 订阅

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2nd International conference on Signal and Data Processing, ICSDP 2022

2nd International Conference on Signal and Data Processing, ...

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2nd International conference on Signal and Data Processing, ICSDP 2022

ISBN: (纸本)9789819914098

The proceedings contain 48 papers. The special focus in this conference is on Signal and Data Processing. The topics include: Process Mining-Based Behavioral Modeling of Learners in Self-paced learning Environment;blockchain Scalability: Solutions, Challenges and Future Possibilities;Solution of Unit Commitment Problems in GAMS Computational Environment;taxonomy and Implications of machine learning for Internet of Things: Qualities, Uses and algorithms;auto Organizer: A machine learning-Based Tool for Automatic Organization of Files;twitter Spam Detection Using Different machine learning Techniques;on the Role of Perceptual Information in Image Classification;bengali Text Classification Based on Probability Measure;comprehensive Analysis on the Performance of Antenna Design Using machine learning Techniques;design and Analysis of Novel Miniaturized Metamaterial Structures for Multiband Applications;hybrid Particle Swarm Optimization Based Deep learning Model for the Stage Classification of Lung Cancer;Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System;a Stacked Multichannel Feature Map Based U-Net Model for Brain Tumor Segmentation;spatial Attention Gated U-Net Structure for Capsule Endoscopy Image Super-Resolution;feature Engineering and Selection for the Identification of Fake News in Social Media;early Detection of Pathological Myopia in Fundus Images Using Deep learning;an Investigation on the Extractive Summarization of Kannada Text;rice Plant Leaf Disease Detection—A Comparison of Various Methodologies;combating Fake News with machine learning and Deep learning Methods;Employing Soft computing-Based GGA-MLP for Hyperparameter Optimization in COVID-19-Infected Lung Image Data Classification;Design of Compact UWB MIMO Antenna with High Isolation Using Square Swirl Shape EBG Structure;forecasting with Fuzzy Time Series and Variation.

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eKisaan -A B2B Agricultural Platform to Enhance the Production of Crops using machine learning 2

eKisaan -A B2B Agricultural Platform to Enhance the Producti...

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2nd International conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Iyer, Shruti Srikant Venkatesan, Bharati Varadarajan, Shrinidhi Shaikh, Bushra Sies Graduate School of Technology Department of Information Technology Navi Mumbai India

ISBN: (纸本)9781665491990

Agriculture is the industry that deals with growing crops and raising livestock. Modern agronomy, plant breeding, agrochemicals like chemical fertilizers, and technological advancements have all contributed to a significant increase in crop yields, but they also have negative effects on the ecosystem and the environment. The effects of agriculture include antibiotic resistance, deforestation, aquifer depletion, global warming, and other environmental problems. One of the main challenges in agriculture is that a crop's price has dropped because it was not grown at the right time and the right season. Due to lack of right information and fluctuating weather conditions, there's a need to develop a system where the farmers can be provided with the information on the types of crops to be grown or nurtured. The main aim of our system is to help farmers to make appropriate decisions regarding the crop and fertilizer selection in order to increase the crop's pro ductivity. The proposed system performs analysis based on the geographical location and other factors such as weather, temperature, humidity, rainfall, phosphorus and nitrogen levels using algorithm viz. Random Forest, Decision Tree and Naive Bayes. By performing exploratory data analysis through these algorithms, it was found that the Random Forest algorithm yields the most accurate and unbiased prediction. In order to connect farmers directly with the retailers for trading of crops, the proposed system provides the B2B platform. This eliminates any third party involvement and thus any transaction fee. © 2023 IEEE.

关键词： Decision trees

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CO2 Emission Rating by Vehicles using Supervised algorithms 2

CO2 Emission Rating by Vehicles using Supervised Algorithms

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2nd IEEE International conference on Advances in computing, Communication and Applied Informatics, ACCAI 2023

作者： Ramesh, Sudarshni Shiny Shalynn, I.M. Justus, J. Jean St. Joseph's College of Engineering Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350315905

Personal vehicles play a significant role in contributing to the issue of global warming. Gasoline used in cars emits an estimated 24 pounds of carbon dioxide and other greenhouse gases per gallon, which accounts for roughly 20% of all emissions. Over five pounds of heat-trapping pollutants are produced throughout the fuel's extraction, manufacture, and delivery, while it's important to note that cars release over 19 pounds per gallon directly from their tailpipes. At present, gas-powered vehicles typically achieve a fuel efficiency of around 22.0 miles per gallon, covering an annual distance of 11,500 miles. As a consequence, the combustion of one gallon of gasoline results in approximately 8,887 grams of CO2 being released into the atmosphere. In 1998, the automobile industry pledged to reduce new car emissions by 25% by 2008. At that time, CO2 emissions from new cars were approximately 203g/km. Currently, the average emissions of vehicles stand at approximately 170g/km and it is projected that they will not decrease to 140g/km until after 2020. The amount of CO2 emitted by a standard passenger car is typically around 4.6 metric tons per year, but this can fluctuate depending on factors such as fuel type, fuel efficiency, and mileage. While predicting emissions becomes more challenging as more variables come into play, some experimental designs consider controllable variables and their interactions. To predict gas emissions, we've developed a model that utilizes a car's attributes to determine if it exceeds the CO2 threshold. If it does, the RTA will take action. One of the best techniques for predicting CO2 emissions is supervised machine learning. © 2023 IEEE.

关键词： Data visualization

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A machine learning Model for Augmenting the Media Accessibility for the Disabled People 2

A Machine Learning Model for Augmenting the Media Accessibil...

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2nd IEEE International conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Andi, Hari Krishnan Senbagam, S. Sharma, Dilip Kumar Shelke, Mayura Kumar, N K Senthil Poonia, Ramesh Chandra Asia Metropolitan University Centre for Postgraduate Studies Malaysia Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nandu Mamallapuram India Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India Aissms Ioit Department of Artificial Intelligence and Data Science Pune India Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science Delhi-NCR Uttar Pradesh Ghaziabad201003 India

ISBN: (纸本)9798350369960

In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern machine learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. machine learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. © 2023 IEEE.

关键词： captioning Digital data disabled individuals machine learning media augmentation similarity score tags vector words

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BGKnow-Medical Chatbot: A Hybrid Approach Based on Knowledge Graph and GPT-2 2nd

BGKnow-Medical Chatbot: A Hybrid Approach Based on Knowledg...

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2nd International conference on Advances in Data-driven computing and Intelligent Systems, ADCIS 2023

作者： Nikam, Disha Sunil Nisha Murthy, D. Pragada, Sreeramya Dharani Mamatha, H.R. PES University Karnataka Banashankari Bengaluru560085 India

ISBN: (纸本)9789819995202

Accurate and timely diagnosis is critical in ensuring patients receive care and treatment for their medical conditions. Traditional symptom checkers often lack accuracy and efficiency in diagnosing diseases, as they typically rely on preprogrammed decision trees or rule-based algorithms that may not account for the complexity and variability of symptoms. By using natural language processing (NLP) and machine learning techniques, such as knowledge graphs and Bio-Bidirectional Encoder Representations From Transformers (BioBERT), chatbots can provide a more accurate and personalized approach to disease diagnosis. This paper introduces a hybrid chatbot framework called "BGKnow." BGKnow represents the combination of a knowledge graph and Generative Pre-trained Transformer 2 (GPT-2) model that uses BioBERT embeddings for effective diagnosis based on symptoms entered by users. The proposed system shows promising potential for assisting healthcare professionals in accurately and efficiently addressing medical inquiries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Knowledge graph

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Federated Transfer learning for Vision-Based Fall Detection 2nd

Federated Transfer Learning for Vision-Based Fall Detection

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2nd International conference on Big Data, IoT and machine learning, BIM 2023

作者： Mistry, Durjoy Tonmoy, Moshiur Rahman Anower, Md. Shahib Hasan, A S M Touhidul Department of CSE University of Asia Pacific 74/A Green Rd Dhaka1215 Bangladesh Division of Computing Analytics and Mathematics School of Science and Engineering University of Missouri-Kansas City Kansas CityMO64110 United States

ISBN: (纸本)9789819989362

The incidence of human falls has emerged as a growing public health concern, particularly among the elderly and individuals with disabilities. Fall detection has assumed a vital role in healthcare research, aiming to mitigate the adverse consequences of falls, such as severe medical complications, prolonged treatment, hospitalization, and potential permanent disabilities. Fall detection methods encompass auxiliary equipment-based and computer vision-based approaches, which have gained prominence due to the increasing effectiveness and resilience of the Internet of Things (IoT). Nevertheless, computer vision-based methods give rise to significant privacy concerns, as monitoring human movements may entail capturing sensitive personnel images. To address this concern, this paper proposes a privacy-preserving approach for fall detection, utilizing Federated learning in conjunction with Transfer learning to train models. This study implemented various well-known pre-trained models, including VGG16, VGG19, InceptionV3, InceptionResNetV2, and Classic CNN, in a federated environment using a dataset comprising 30 videos. Among these models, InceptionResNetV2 achieved the highest test accuracy of 97.38%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International conference, ETBS 2022

Emergent Converging Technologies and Biomedical Systems Sele...

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International conference, ETBS 2022

ISBN: (纸本)9789819922703

The proceedings contain 55 papers. The special focus in this conference is on Emergent Converging Technologies and Biomedical Systems. The topics include: Development of a Coconut De-Husking machine;health Sector: An Overview of Various Smartphone Apps;survey on IoT Based Secure Health Care Framework Using Block Chain Technology;a Methodology for Increasing Precision for the Efficient Implementation of Sigmoid Function;fog-Based Smart Healthcare Architecture in IoT Environment;COVID-19 and SDG 3 in Bangladesh: A Statistical and machine learning Approach;Analysis of Power Consumption and IoT Devices Using SPSS Tool;analyzing Optimal Environment for the Text Classification in Deep learning;estimating Factors of Agile Software Development Using Fuzzy Logic: A Survey;hyperspectral Image and Deep learning Methodology for Water Evaporation Prediction and Control System;computational System Based on machine learning with Hybrid Security Technique to Classify Crime Offenses;Analysis of Robotically Controlled Percutaneous Needle Insertion into Ex Vivo Kidney Tissue for Minimally Invasive Percutaneous Nephrolithotomy (PCNL) Surgery;location-Based Attendance Monitoring System with Facial Authentication;load Balancing in Cloud computing Using Multi-agent-Based algorithms;Viable Methods Adopted for Reducing SAR Value in Mobile Phone Antenna: A Review;numerical Modelling of Cu2O-Based Gas Sensor for Detection of Volatile Organic Compounds in Human Breath;significance of Convolutional Neural Network in Fake Content Detection: A Systematic Survey;wavelet and Savitzky–Golay Filter-Based Denoising of Electrocardiogram Signal: An Improved Approach;Heart Disease Detection Using Phonocardiogram (PCG) Signals;enigmas of Various Techniques to Implementing Authentication and Integrity in Blockchain-Based Wireless Sensor Networks;data Integration in IoT Using Edge Gateway.

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2nd International conference on Deep Sciences for computing and Communications, IcondeepCom 2023

2nd International Conference on Deep Sciences for Computing ...

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2nd International conference on Deep Sciences for computing and Communications, IcondeepCom 2023

ISBN: (纸本)9783031689048

The proceedings contain 82 papers. The special focus in this conference is on Deep Sciences for computing and Communications. The topics include: Digital Identity System for Real World Asset Using Blockchain;blockchain-Driven Framework for Fake Product Detection;blockchain Powered Real-Estate Management System;a Block-Chain Based Decentralized Mechanism to Ensure the Security of Electronic Voting System Using Solidity Language;a Secure Persistent Health Care System Using Blockchain Smart Contract;multimodal Fake News Detection Using Deep learning Techniques;maritime Human Drowning Detection Using Intelligent Deep learning Algorithm;development of a Pothole Detection System Using Deep learning Techniques and Depth Estimation;machine learning and Deep learning algorithms for Breast Cancer Prediction;leaf Disease Detection Using Deep learning Approach;Robust Traffic Sign Recognition Using CNN YOLOv5 Model;Fusion Emotion Prediction Using the CEFER Algorithm;performance Analysis of Various machine learning Techniques for Mental Health Tracking;Chronic Kidney Disease (CKD) Detection Analysis Using machine learning;Revolutionizing MS Rehabilitation with Digital Twins and machine learning: A Promising Path to Precision Medicine;empirical Evaluation of machine learning Techniques for Car Price Prediction;water Quality Analysis Using machine learning Techniques;design and Development of Human Identification and Obstacle Detection System for Blind Using machine learning;effective Parkinson Disease Detection and Prediction Using Voting Classifier in machine learning;inflow and Infiltration Water Problem Detection Using machine learning;crop Recommendation and Production Prediction;a Comparison of Cox Model and machine learning Techniques in the High-Dimensional Survival Data;Smart Farming: Using IoT and AI to Improve Crop Yield in Aeroponics System.

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2nd International conference on Business Data Analytics, ICBDA 2023

2nd International Conference on Business Data Analytics, ICB...

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2nd International conference on Business Data Analytics, ICBDA 2023

ISBN: (纸本)9783031807770

The proceedings contain 28 papers. The special focus in this conference is on Business Data Analytics. The topics include: A Novel Approach for Diabetic Prediction Using Attribute Subset Selection, K-Means and Logistic Regression;diabetic Retinopathy Detection Approach Using Convolution Neural Networks;voting Regression Model for the Air Quality Prediction;Quantitative Assessment on Investigation on the Impact of Artificial Intelligence on HR Practices and Organizational Efficiency for Industry 4.0;NIR Spectroscopy for Freshness Detection and Classification of Chicken Eggs;Human Action Recognition in Infrared Domain: A Study on IITR-IAR Dataset;Software Requirements Prioritization Using Fuzzy Based TOPSIS Methods;predictive Analytics for Inventory Management in Multi-Vendor E-Commerce;Data Analytics Augmented by AI in the Realm of 6G Wireless Communication;big Data-Powered Analytics for Fortifying Virtualized Infrastructure Security in the Cloud;orthogonal Polynomials and Their Engineering Applications;Explainable AI for Drone Data Analytics in Aerial computing;correlational Analysis of Risk-Taking Propensity in Adolescents;Analytical Insight into Cutting-Edge Image Captioning for Advanced ChatGPT Functionality;an Expert System for Talent Prediction and Enhancement of Non-Talented Cricket Performers;Predicting Early Dropouts in SWAYAM MOOCs Using machine learning Techniques: A Comparative Analysis;data Analysis of Social Media’s Impact on Promotion of Organic Products in Uttarakhand;enhancing Academic Performance Prediction Through K-Means Clustering and Comparative Evaluation of machine learning algorithms: A Case Study on Student Dataset;cognitive Data Underwriting with Automation: A Survey;Multi-Level Thresholding Segmentation of Chest X-ray Images of COVID-19 Patients Using Chaos Game Optimizer and Utilizing Kapur's Entropy;concrete Strength Prediction Using machine learning.

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Classic and Modern Methods of Automatic Parking Control of Self-driving Cars 13th

Classic and Modern Methods of Automatic Parking Control of S...

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13th International conference on High-Performance computing Systems and Technologies in Scientific Research, Automation of Control and Production, HPCST 2023

作者： Tyulenev, Ilya D. Filimonov, Nikolay B. Lomonosov Moscow State University Leninskie Gory 1 Moscow119991 Russia Bauman University 2nd Baumanskaya Street 5/1 Moscow105005 Russia

ISBN: (纸本)9783031510564

The problem of automatic parking control of an unmanned car is considered. The formulation and formalization of the problem of the car parking control taking into account the restrictions that ensure the safety of the parking maneuver are given. Classical and modern methods of the automatic parking control of unmanned cars are analyzed. Based on the Dubins and Reeds-Shepp motion models, optimal algorithms developed for the car parking control are synthesized. A fast-growing random tree algorithm RRT is used to construct a path between two points. Based on the method of machine learning involving reinforcement, a car parking control algorithm is synthesized. The algorithm convergence is investigated, and the optimal values of the training parameters are determined. The results of the computer testing of synthesized parking algorithms implemented in Python using mathematical libraries Matplotlib and NumPy are presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Reinforcement learning

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