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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是211-220 订阅

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A machine learning based Insect Bite Classification 3

A Machine Learning based Insect Bite Classification

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Akshaykrishnan, V. Sharanya, C. Abhinav, K. Aparna, C.K. Bindu, P.V. Government College of Engineering Department of Computer Science and Engineering Kerala Kannur India

ISBN: (纸本)9781665464871

Identifying insects by their bite marks can assist doctors in diagnosing victims and providing appropriate treatment. In recent years, researches using machine learning have been actively conducted and have produced excellent results in fields such as object detection, behaviour recognition, voice recognition, and cancer detection in medical field. This study has developed a classification application that can be used on mobile phones to solve the insect classification problems. Experiments were carried out on five insect species chosen for being the most common biting insects. Detailed study was conducted on different images with the help of Random Forest and Support Vector machine models. These models need different insect bite marks images to classify them. Random forests achieve a better performance and are usually much faster than Support Vector machines. © 2023 IEEE.

关键词： Support vector machines

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A Query-Based Approach to Mitigate the Shortcomings of Widely Used learning Methods Through E-learning 1

引用

3rd international conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Zaman, Khandoker Ashik Uz Islam, Yusuf Mahbubul Sayed, Md Abu Computer Science & Engineering Independent University Bangladesh Plot 16 Aftab Uddin Ahmed Rd Dhaka1229 Bangladesh Department of Computer Science & Engineering South-East University Dhaka Bangladesh

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

This paper begins with a review of existing learning methods, highlighting the limitations that hinder motivation, development of confidence, and memory retention. To address these gaps, we propose an e-learning strategy that uses technology to create a peer collaborative question development marketplace. The mobile application developed for this purpose is part of a progressive web application that functions as the peer collaborative questioning questions platform. The students’ exchanges of questions are ranked using Bloom’s Taxonomy, encouraging anonymous student questioning that should gradually lead to a deeper topic exploration. Our analysis, comparing an experimental group with a control, reveals that the proposed strategy creates a positive impact on students, enhancing their confidence, motivation, and querying skills. Furthermore, we found that enhanced questioning correlates with improved academic performance. Finally, we also discuss the potential challenges and how the dataset obtained from this study can be utilized for machine learning and artificial intelligence applications in e-learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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Artificial Intelligence in Software Engineering: Perspectives and Challenges 3

Artificial Intelligence in Software Engineering: Perspective...

引用

3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Bhandari, Kirti Kumar, Kuldeep Sangal, Amrit Lal Dr. B.R Ambedkar National Institute of Technology Computer Science & Engineering Jalandhar India National Institute of Technology Computer Engineering Kurukshetra India

ISBN: (纸本)9798350300710

Artificial Intelligence in Software Engineering is the hottest trend in the rapidly growing software world. AI techniques are powerful and easy to use as it can be easily deployed as key components of the system. The fusion of both these techniques emerges four new innovative research areas which require extensive research in the future. The application of AI techniques in the Software Engineering field poses many opportunities and risks for software organizations and handling these issues is essential before the selection of appropriate techniques. In this paper, we present an introduction, new emerging fields with the fusion of Artificial Intelligence with Software Engineering, challenges, and a conclusion. © 2023 IEEE.

关键词： machine learning

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Daily, Weekly and Monthly Rain Forecasting using Deep learning 3

Daily, Weekly and Monthly Rain Forecasting using Deep Learni...

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Gupta, Kunal Gupta, Rajeev Kumar Gupta, Aniket Pandit Deendayal Energy University Computer Science & Engineering Gandhinagar382007 India

ISBN: (纸本)9798350381931

This research paper focuses on the extensive exploration and forecasting of daily rainfall, a critical element in weather prediction and environmental planning. The study integrates two-time series datasets spanning from 2013 to 2022, encompassing essential meteorological parameters. Through Exploratory Data Analysis (EDA) visualizations, non-stationary patterns are identified, leading to the application of Seasonal Autoregressive Integrated Moving Average (SARIMA) for capturing temporal dynamics. The study then introduces deep learning, specifically Long Short-Term Memory (LSTM) networks, to enhance rainfall prediction by capturing intricate temporal dependencies. A comparative analysis combines SARIMA with Rolling Forecast, showcasing adaptability to evolving patterns. In this compares we find that SARIMA higher accuracy with a high R2 value. Both are outperformed by the hybrid Rolling Forecast with SARIMA, underscoring the necessity of balancing accuracy and flexibility. The paper highlights SARIMA's ability to capture temporal dynamics, acknowledges LSTM's collaboration with meteorological organizations for improved rain forecasting precision in the face of climate change. © 2024 IEEE.

关键词： Rain

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Pneumonia Detection and Classification using Hybrid Convolution Neural Network and machine learning Classifiers 3

Pneumonia Detection and Classification using Hybrid Convolut...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Madhuri, A. Akhila, N. Rashidunnisa, Sk Yamini Priya, V. Shahid, Sk Lokesh Sai Kumar, D. Prasad v Potluri Siddhartha Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Vijayawada India

ISBN: (纸本)9781665462167

Viruses, bacteria, and fungus are the main causes of pneumonia, an infectious disease that affects the lungs in people. It is mostly seen individuals older than sixty years of age and youngsters younger than five. Pneumonia causes fever, shortness of breath, nausea, diarrhea, etc. So, in this project Hybrid Convolution Neural Network (CNN) is used with machine learning Classifiers to detect Pneumonia disease from the Chest X-rays, which are used in the real world by medical professionals to find pneumonia. computer-Aided Diagnosis (CAD) systems may be improved with the application of deep learning and machine learning based technologies, which can be of assistance to radiologists and doctors when making medical decisions. CNN have shown a significant amount of promise in terms of image classification and segmentation, and they are often used for the development of DL-based CAD systems. The findings indicate that the suggested ensemble classifier, which combines Radial Basis Function (RBF), classifiers using support vector machines (SVM) and logistic regression (LR), performs best, with an accuracy rate of 97%. Ultimately, a web-based CAD system is made using this concept that will greatly enhance radiologists' capacity to recognize pneumonia. © 2023 IEEE.

关键词： Support vector machines

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Prediction of Peak Shear Strength of Joints Based on machine learning Algorithms 3

Prediction of Peak Shear Strength of Joints Based on Machine...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Shen, Hui Liu, YaQun Li, HaiBo Xia, Xiang Liu, Bo Yu, Chong Institute of Rock and Soil Mechanics Chinese Academy of Sciences State Key Laboratory of Geomechanics and Geotechnical Engineering Hubei Wuhan China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665459112

The peak shear strength of rock joints is closely related to the normal stress, the roughness of the joint surface, and the mechanical properties of the material. Based on the published data, three machine learning (ML) algorithms, including Support Vector machine (SVM), BP Neural Network (BPNN), and Random Forest (RF) are used to established regression models for predicting the peak shear strength of joints. 10-fold cross-validation is adopted to evaluate the performance of three ML models. In addition, three empirical peak shear strength models are selected for comparison with the ML models. Compared to the empirical models, we find that the ML models predict the peak shear strength with higher accuracy and lower error, indicating that the ML models perform better. Finally, the parameter analysis is performed to evaluate the influence of input parameters and the robustness of the ML models. © 2022 IEEE.

关键词： Support vector machines

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Research on Power Line Carrier Communication Channel Modeling for Power Distribution and Consumption IoT 3

Research on Power Line Carrier Communication Channel Modelin...

引用

3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Shi, Zhan Lu, Jiangang Li, Bo Guangdong Power Grid Co. Ltd. Power Dispatching Control Center Communication Management Department Guangdong China

ISBN: (纸本)9781665459112

The information interaction in power distribution and consumption internet of things (IoT) requires high-speed, reliable and secure communication support. Power line carrier (PLC) communication provides an effective solution due to the low construction cost, wide coverage, and high transmission rate. However, the performance of PLC is effected by multipath fading, noise, and electromagnetic interference. Therefore, it is necessary to research on the PLC channel modeling. In this paper, three PLC channel modeling methods based on transmission line theory, graph theory, and machine learning are researched, which can achieve relatively accurate channel estimation and low error values between the estimated and the measured values. © 2022 IEEE.

关键词： machine learning

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

3rd International Conference on Machine Learning, Cloud Comp...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

ISBN: (纸本)9789819616978

The proceedings contain 27 papers. The special focus in this conference is on machine learning, Cloud Computing and Intelligent Mining. The topics include: Decentralized Optimal Tracking Control of MRMs Under Health Indicator-Based Event-Triggered Mechanism;iterative learning Control for Nonlinear Switched Continuous-Time Systems with Time-Varying Delay;Model Migration and Joint Communication and Computing Resource Allocation in Native AI Wireless Networks;FTB-RRP Control for 2-D Continuous-Discrete Systems in Roesser Model;Optimal UAVs Placement for TDOA Localization Method Based On An Improved Grey Wolf Optimization Algorithm;A Bat-BP Optimization Algorithm Based System of Dry Ice Decontamination for UAV;Optimal Resource Allocation Algorithm for Multi-user AI Tasks in Native AI Networks;study on Non-intrusive Load Monitoring Method Based on K-means Clustering and Dual Convolutional Neural Networks;research and application of computer Vision and Predictive Maintenance in Health Management of Conveying Equipment;attitude Stabilization Control for Unmanned Aerial Vehicle with Robotic Arm Based on Fixed-Time Observer Active Disturbance Rejection Control;state Estimation and Control of Switched Neural Networks with Mode-Dependent Hybrid Dwell Time;Distributed Consensus Control Research of Unmanned Aerial Vehicle (UAV) Swarms Based on Lennard-Jones Potential;lifetime Prediction of Fuel Cell Composite Operating Conditions Based on Genetic Algorithm Optimized Elman Neural Network;a Tiered Native Digital Twin for Telco Networks;defender: The Possibility of Repairing Jailbreak Defects;research on Special Vision Recognition and Fault Alarm System for Vertical Roller;malicious Traffic Classification Algorithm Based on Multimodal Fusion;Enhanced Affine Formation Control in Multi-agent Systems with Disturbance Rejection Using RISE Controller;model Predictive Control for Contour Control in Biaxial System.

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3rd international conference on machine learning, Cloud Computing and Intelligent Mining, MLCCIM 2024