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检索条件"任意字段=2nd International Conference on Software Engineering and Machine Learning, CONF-SEML 2024"

共 232 条记录，以下是81-90 订阅

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing machine learning Methods for Accurate Detection;monkeypox Detection with K-mer Using machine learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of machine learning Models for Improved Diabetes Prediction;image Processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest software;a Diagnosis Model Based on Federated learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces Images Using Transfer learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of Signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd international conference on Science, engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722868

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2nd international conference on the Frontiers of Robotics and software engineering, FRSE 2024

2nd International Conference on the Frontiers of Robotics an...

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2nd international conference on the Frontiers of Robotics and software engineering, FRSE 2024

ISBN: (纸本)9789819631605

The proceedings contain 33 papers. The special focus in this conference is on Frontiers of Robotics and software engineering. The topics include: Gait Optimization of Bioinspired Pole-Climbing Robot;an Obstacle Avoidance Path Planning Algorithm for Underwater Multi-jointed Robotic Arms;boosting Practicality of Natural Image Matting with Trimap Adaptation;diffusion Adversarial learning For Underwater Depth Estimation;CRFENet: Cross-Spectral Stereo Matching Network Exploiting Reciprocal Feature Evolution;optimization of Offensive and Defensive Strategies Based on Agent2D Underlying Team Code;A Novel MRI-Compatible Rotary Encoder: Design and Evaluation;progressive Feature Interaction and Enhancement Network for Visible-Infrared Person Re-Identification;a Visual Simulation Platform for Automatic Field-of-View Adjustment Algorithm Optimization of a Tendon-Driven Continuum Laparoscope;purely Visual Loading: Pose-Classification Network for Loading Service Robot onto Ego-Vehicle;unsupervised Person Re-Identification Based on Pixel-Level Fine-Grained Feature Attention;Application of TD3 Algorithm with Adaptive Parameter Noise Mechanism in Automatic Parking;QED-Former: A Query Enhanced Deformable DETR for Video Instance Lane Detection;analysis of the Climbing and Forward Flight Mechanism of Dragonflies;Coverage Path Planning for Heterogeneous UAVs Based on Water Wave Optimization;Research on Scheduling Algorithms for Multi-UAV Collaborative Search;Research on Adaptive Mobile Mechanism of Wheel-Shoe Complex Deformed Mobile Police EOD Robot;Functional Coverage-Driven UVM-Based ARINC429 Verification;reinforcement learning Based Soccer Kicking for Humanoid Robots;robot Path Optimization Method for Nuclear Reactor Decommissioning;Multi-AMR Obstacle Avoidance Algorithm Based on Improved MATD3;The NFTSM Based Platoon Tracking Control for a Group of Nonlinear and Underactuated Vehicles on Curved Roads;Application of SE Module Based YOLO v8 Network and machine learning in Laser Notch

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An Experimental Comparison Between Tree-Based and machine learning Approaches for Predicting an Oil Temperature in an Electricity Transformer Dataset 2nd

An Experimental Comparison Between Tree-Based and Machine Le...

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2nd international conference on From Smart City to Smart Factory for a Sustainable Future, SCFF 2024

作者： Do, Anh Kim Thi Nguyen, Vinh Dinh School of Computing and Information Technology Eastern International University Thủ Dầu Một Viet Nam Department of Software Engineering FPT University Can Tho City Viet Nam

ISBN: (纸本)9783031656552

Time series data forecasting remains a challenging problem within research communities. A plethora of machine learning and deep learning architectures have been devised to tackle the wide array of time series datasets across different domains. Recent advancements in machine learning algorithms have demonstrated impressive performance. However, there remains a limitation regarding the time-consuming nature of both training and testing phases. Hence, this study endeavors to address whether any methods can achieve comparable performance to recent machine learning algorithms without requiring extensive training time. Through our research, we have identified XGBoost as the optimal solution for addressing this issue. Through comprehensive experiments across three study cases encompassing multivariate to multivariate prediction, multivariate to univariate prediction, and univariate to univariate prediction scenarios, we discovered that XGBoost outperformed other state-of-the-art machine learning techniques in the multivariate-to-multivariate prediction case under ETD dataset. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： machine learning time series forecasting tree-based method

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Enhancing Hand Script Digitization using Deep learning 2

Enhancing Hand Script Digitization using Deep Learning

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2nd IEEE international conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Siranjeevi, P. Ruthran, R. Ramchandru Kaladevi, R. Panimalar Engineering College Department of IT Chennai India

ISBN: (纸本)9798350317558

The growth of digital technology in society plays an important role of storing, securing, and availability. For all of this to be possible the language is a critical point which makes people understand it. Many historical and legal documents in Tamil Nadu and other Tamil speaking regions are still in handwritten text. This Research converting handwritten script into machine-readable format. The practical use of Convolutional Neural Networks and Recurrent Neural Networks to effectively learn and recognize the intricate patterns of Tamil characters can allow people to understand and use these applicable methods in making a digitally Recognized Language. The Research also examines the potential environment this software could shine its excellence of Tamil handwritten recognition, including digitizing historical documents, enabling efficient data entry on touch-enabled devices. This software's goal is to make every user able to develop their own digital library available at their fingertips. The compatibility and flawless performance with various operating software is ensured. This Technology would be a contribution to the society which acts in various fields such as helping in evaluating exam papers, written documents and for better information fraud control. The result of this project has shown a promising result of 95.7% Accuracy on Tamil Handwritten Character Recognition with low error rate of 0.0728% for every character, which also exceeded the previous technological standard. © 2024 IEEE.

关键词： CNN Convolutional Neural Network document scanner Feature extraction Handwritten Tamil Recognition Image scanner Preprocessing Recurrent Neural Network RNN Segmentation Text extract

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2nd international conference on the Frontiers of Robotics and software engineering, FRSE 2024

2nd International Conference on the Frontiers of Robotics an...

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2nd international conference on the Frontiers of Robotics and software engineering, FRSE 2024

ISBN: (纸本)9789819630127

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Revolutionizing Business Sentiment Analysis: Unleashing the Power of machine learning for Enhanced Sentiment Classification 2nd

Revolutionizing Business Sentiment Analysis: Unleashing the ...

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2nd international conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Mandal, Swagata Singh, Bhuvnendra Pratap Mohla, Ankur Bhatt, Aditya Nath Department of Electronics and Communication Engineering SRM Institute of Science & Technology Tamil Nadu Chennai603203 India

ISBN: (纸本)9789819977109

This research proposes a feedback system for modern businesses that utilizes text sentiment analysis to analyze customer reviews. Many customers have reported that businesses fail to understand their needs and often sell them products of poor quality or no use. To solve this problem, a smart feedback system is necessary to successfully classify reviews as positive, negative, or neutral. The proposed system consists of an AI/ML algorithm that extracts reviews from Amazon. The algorithm with the highest accuracy may be used to classify customer reviews. The proposed feedback system is advantageous over existing systems in that it is developed using mainstream technologies, easy to use, utilizes artificial intelligence, and can be automated. If deployed as a software as a Service, the software subscription cost may be affordable and attract many business owners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Ransomware Detection Utilizing Ensemble Based Interpretable Deep learning Model 2

Ransomware Detection Utilizing Ensemble Based Interpretable ...

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2nd international conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Chisty, Tanjir Alam Rahman Rahman, Md. Mahbubur University of Professionals Department of Information and Communication Technology Bangladesh Dhaka Bangladesh Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

Ransomware, a form of malicious software, encrypts victim data and renders it inaccessible until a ransom is paid. Different ransomware variants employ various tactics to avoid detection during their attacks. Understanding the techniques used in ransomware development and deployment is crucial to counter these threats. This study presents a stacked ensemble-based machine learning (ML) approach for ransomware detection. The ensemble model consists of Naive Bayes (NB), Random Forest (RF), and K-Nearest Neibors (KNN) as base classifiers and Artificial Neural Network (ANN) as meta-classifiers. Additionally, Explainable AI (XAI) utilizing LIME and SHAP has been applied to gain insights into prediction outcomes and validate model correctness. The use of LIME and SHAP ensures the inter-pretability of the model, which is a crucial factor in real-world implications. The research employs a publicly available dataset and evaluates the model using multiple metrics. Experimental results reveal that the proposed model achieves an impressive accuracy of 98.57%. The model is benchmarked against existing ML techniques, demonstrating its superior performance. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Power Consumption in Wireless Sensor Networks for Prolonged Sustainability 2

Optimizing Power Consumption in Wireless Sensor Networks for...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Sivasankar, C. Subramanian, E.K. Sarala, V. Moorthy, A. Purushothaman, N. Chennai India Department of Computer Science and Engineering Saveetha school of Engineering Simats Chennai India Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Thandalam Chennai India Department of CSE SKP Engineering College Tiruvannamalai India

ISBN: (纸本)9798350383867

As Wireless Sensor Networks (WSNs) grow rapidly, power optimization for sustainability and efficiency is needed. This study offers a complete energy-saving and WSN-extending method. Hardware optimization, software algorithms, network management, adaptive network design, AI-based optimization, security, and reliability are advised. Hardware optimization creates energy-efficient low-power sensor nodes. Battery power and solar cells and vibration energy harvesters form a hybrid energy system. It conserves node energy and prolongs network life. The method uses algorithms and software. To save energy, intelligent duty cycle and power management algorithms optimize sensor activity and sleep. Aggregating and compressing data saves transmission and processing energy. These technologies reduce data handling power to improve network efficiency. Optimizing energy use involves network management. Energy-aware routing techniques and multi-hop routing optimize data transmission paths and reduce network layer energy use. To save energy, cross-layer optimization merges programs across network protocol stack levels. Scalable, adaptive network architecture efficiently handles more sensor nodes. Network configuration adapts to energy and environment. Network infrastructures must be scalable and adaptable for energy efficiency. Advanced machine learning and AI-based optimization are used. machine learning algorithms forecast energy use and needs in predictive energy management. AI optimises energy use in self-optimizing networks, enhancing efficiency. Security and reliability impact energy efficiency. Energy-efficient secure connection protects data. Fault-tolerant networks handle node failures and communication disruptions without affecting energy utilization. It ensures energy economy and network reliability. This method could work. We anticipate 40% reduced node energy use and 75% longer network longevity. Data transmission efficiency should rise by 28.6% and system dependability by

关键词： Digital storage

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Land use and land cover classification using GEE and machine learning algorithms: a case study of Vaijapur Tehsil 2

Land use and land cover classification using GEE and machine...

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2nd international conference on Current Trends in Physics and Photonics, ICCTPP 2024

作者： Gaikwad, Sandeep V. Shah, Suraj V. Vibhute, Amol D. Saini, Jatinderkumar R. Kale, Karbhari V. Pune411016 MH India Dr. Babasaheb Ambedkar Technological University Lonere402103 MH India

ISBN: (纸本)9781510682948

The monitoring and analysis of land surface changes are very challenging for researchers in terms of time and the cost of software tool licenses. Now, it has become easier to accomplish with the help of modern tools like Google Earth Engine (GEE) and built-in machine learning (ML) algorithms. GEE is a platform for analysing and interpreting satellite datasets in the cloud, which saves time and storage space for researchers. In the present research study, we have analysed the Land Use Land Cover (LULC) of Vaijapur taluka, which is undergoing a rapid shift from rural to urban. The study compares the LULC classification by three supervised ML algorithms, Random Forest (RF), k- Nearest-Neighbour (k-NN), and Gradient Tree Boost (GTB). We have obtained the overall accuracy of RF (92%) is better than that of GTB (88 %) and k-NN (86.67%). The research study observed that the kappa accuracy of RF (0.89) is improved than GTB (0.83) and k-NN (0.81). © 2024 SPIE.

关键词： Random forests

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