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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

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Analysis of the Cloudlet's Performance Using machine learning with Cloud Management System 4

Analysis of the Cloudlet's Performance Using Machine Learnin...

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4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Salve, Ritesh Paik, Sanket Kalaskar, Payal Urunkar, Rutuja Sable, Sahil William, P. Sanjivani College of Engineering Savitribai Phule Pune University Department of Electronics and Computer Engineering Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India

ISBN: (纸本)9798350393248

One of the most widely used methods for hosting and offering services over networks and the Internet is cloud computing, which also includes edge computing. the hardware architecture is built upon the advanced computer technology. Many businesses and academic organizations employ cloud computing. Cloud and edge computing are hampered in terms of security and privacy. Customers frequently have security concerns about the cloud. there are numerous risks that could endanger sensitive user data kept in the cloud, even with privacy regulations in place. In addition to giving a summary of the numerous study findings on cloud privacy issues and tabulating the advantages and disadvantages of recent studies, we classify the current approaches to privacy issues in cloud environments as designs, tactics, and methodology. We also discuss open research topics and offer ideas for new directions to pursue. the principal objective of this research is to elucidate the current gaps in privacy knowledge and offer a more comprehensive grasp of the privacy issues related to cloud computing. the development of the cloud has brought up a number of security concerns that affect both people and corporations. Using machine learning (ML) is one way to secure the cloud. machine learning methods can be used in a variety of contexts to identify and address cloud security risks. this study has improved IoT device network security detection by using machine learning more frequently. © 2023 IEEE.

关键词： Edge computing

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Extreme learning machine for Parking Slot Recognition in Smart Parking System 8

Extreme Learning Machine for Parking Slot Recognition in Sma...

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8th international conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Rahmat, Romi Fadillah Faza, Sharfina Syahputra, Ilham Nainggolan, Pauzi Ibrahim Sulaiman, Riza Rahman, Mohd Amiruddin Abd Universitas Sumatera Utara Department of Information Technology Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia Universitas Sumatera Utara Department of Computer Science Medan Indonesia Institute of IR 4.0 Universiti Kebangsaan Malaysia Bangi Malaysia Universiti Putra Malaysia Faculty of Science Selangor Malaysia

ISBN: (纸本)9798331531249

In the modern era, technology has become an integral part of human life, particularly in image processing. this advanced technology is now applied to parking areas to identify vacant parking spaces. the application is designed to detect empty parking slots in real time using image data captured by a camera as input. the system then automatically scans for available parking spaces, displaying the results on the interface with green indicators for empty slots and red for occupied ones. the process involves several key stages, such as image pre-processing, image segmentation, and object detection. the system employs the Extreme learning machine (ELM) method to enhance its object detection capabilities. this system achieves a high accuracy rate of 94%under well-lit conditions. this novel technology optimizes parking management, providing an effective and dependable method to monitor parking availability in real time. © 2024 IEEE.

关键词： Adversarial machine learning

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the application of Artificial Intelligence in Exam Evaluation 4

The Application of Artificial Intelligence in Exam Evaluatio...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Li, Yuwei Jilin Agricultural Science and Technology University Jilin132101 China

In order to achieve intelligent recognition and auxiliary scoring of subjective questions in paper-based exams, as well as timely detection of various abnormal test papers, assist manual grading and quality inspection, and improve the quality of grading, the application of artificial intelligence in education exam evaluation has been studied. the author focuses on the technological progress of artificial intelligence in educational exam evaluation, introduces the application results of artificial intelligence technology in assisting artificial intelligence in exam marking, and explains the effectiveness of using intelligent assisted scoring in various exam questions such as fill in the blank and essay. the experimental results showed that, compared with manual grading, the human-machine consistency rate for identifying multiple gaps in blank filling questions exceeded 94%, and the human-machine consistency rate calculated based on small questions also exceeded 97%. this result indicates that the machine evaluation results can serve as an auxiliary means for quality inspection and grading, and their effectiveness is reliable. In addition, in terms of subjective question evaluation, there is a high correlation between machine evaluation results and manually rated final report scores, both of which are greater than 0.7. the results indicate that their scores can be regarded as third-party manual scoring, which can be used for scoring quality checks and other aspects, playing a good role in improving scoring effectiveness. © 2024 the Authors.

关键词： Online systems

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Trip Saathi: Travel Planning System based on Sentiment Analysis of Reviews 4

Trip Saathi: Travel Planning System based on Sentiment Analy...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kanakia, Harshil T. Eranjikal, Shivani Sardar Patel Institute of Technology Computer Application Department Mumbai India

ISBN: (纸本)9798350300093

In India there are lot of places, some less explored and some places have been the epicentre of tourism. Many travel agencies and some enthusiastic people plan the journey with travel and stay. the tourists have a wide array of experiences ranging from positive, neutral to negative. Using the feedback from these people, many others plan their journey. this paper proposes a system to accumulate all feedback at one place and provide sentimental analysis. the result will help any person to have a well-informed decision. © 2023 IEEE.

关键词： Sentiment analysis

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A Deep learning-Based Smart Helmet Detection and Notification with e-Challan Issuance for Traffic Fines 4

A Deep Learning-Based Smart Helmet Detection and Notificatio...

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4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Ghosh, Rajdeep Kotal, Akash Karmakar, Raja Das, Krittika Techno International Department of Computer Science and Engineering New Town Kolkata India Heritage Institute of Technology Department of Computer Science and Engineering Kolkata700107 India Techno International Department of Computer Science and Engineering New Town India

ISBN: (纸本)9798350333558

the Ministry of Road Transport and Highways estimates that 158,964 two-wheeler road accidents occurred in India in 2020, resulting in 56,873 fatalities, some of which were brought on by the lack of a helmet. this is still an issue on Indian roads and puts the lives of the country's young inhabitants in peril, despite numerous campaigns that have been undertaken by various state governments and the imposition of numerous punishments. the usage of computer vision is a suitable remedy for this issue. An automated system is suggested that employs a state-of-the-art single shot detector (SSD) for detection, image upscaling and Optical Character Recognition (OCR) to identify the vehicle's licence plate, as well as a database for e-challan records. Together with the payment link to clear the challan online, other Short Message Service (SMS) and mail application Programming Interface (API) is also utilised to notify about the challan's issuance. this paper explores a deep-learning-based idea that assures traffic safety and order on Indian roads and is a workable and useful solution to the highlighted issue. © 2023 IEEE.

关键词： computer vision

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Development and Implementation of a Web application for Monitoring Social Media Use in Adolescents using Predictive Models Based on machine learning 4

Development and Implementation of a Web Application for Moni...

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4th IEEE international conference on Advanced learning Technologies for Education and Research, ICALTER 2024

作者： Jose Sebastian, Lorenzo Q. Christopher Jean Pierre, Arias P. Esther, Aliaga C. Universidad Peruana de Ciencias Aplicadas Information Systems Engineering Lima Peru

ISBN: (纸本)9798331510244

the rising use of social media among adolescents has led to a concerning increase in mental health issues, such as depression and anxiety, highlighting the need for tools that can identify and address these risks early on. this paper presents a web application that employs Random Forest models, combining machine learning with cognitive-behavioral approaches to diagnose social media addiction risks in real-time among adolescents, facilitating personalized interventions for parents and health professionals. Additionally, social media addiction, characterized by Network Obsession (OR), Lack of Personal Control (FCP), and Excessive Use (UE), has shown negative impacts on adolescents' mental health. To address this issue, the dataset, comprising over 10,000 records, was gathered through an ARS questionnaire and real-time monitoring data on social media behavior, including connection hours and types of content consumed. Experiments conducted on a sample of 120 students from Colegio Trini de Carabayllo, out of a population of 450, demonstrated outstanding results: the FCP model achieved 100% accuracy and an F1-score of 100%, effectively detecting lack of personal control;the OR model attained 98.29% accuracy and an F1-score of 99.04%, highly effectively identifying cases of network obsession;and the UE model reached 93.93% accuracy and an F1-score of 96.10%, detecting excessive use patterns. Finally, the recommendations included setting time limits on social media and cognitive-behavioral therapy through the Mentalu application. © 2024 IEEE.

关键词： Electronic health record

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Comparative Analysis of Federated learning, Deep learning, and Traditional machine learning Techniques for IoT Malware Detection 4

Comparative Analysis of Federated Learning, Deep Learning, a...

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4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Darwish, Rami Roy, Kaushik North Carolina A&T State University Department of Computer Science GreensboroNC United States

ISBN: (纸本)9798331518882

this paper addresses the challenge of classifying Internet of things (IoT) binary files as either malware or benign using graph-theoretical features derived from Control Flow Graphs (CFGs). We use a dataset of 248,276 IoT binary files, each represented by 23 features. Our analysis presents traditional machine learning (ML) classifiers, including Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF), with deep learning (DL) techniques-a Deep Neural Network (DNN) and a Convolutional Neural Network (CNN). Further developing these techniques, we propose the first application of Federated learning (FL) for IoT binary file classification based on CFG features, which provides a decentralized and privacy-preserving solution to malware detection. FL is simulated with two models, FL-DNN and FL-CNN, under IID (Independent and Identically Distributed) and Non-IID scenarios. IID divides the data equally among clients, while Non-IID causes an imbalance to reflect real-world scenarios where data distribution is significantly varied among the clients. In order to handle the challenges in imbalanced data scenarios, threshold tuning and class weighting have been applied to improve FL performance. Our results showed that, among the machine learning techniques, RF achieved the highest accuracy of 96.06%, followed by KNN at 92.78%, and LR at 74.25%. For DL models, DNN and CNN are competitive, achieving accuracies of 93.55% and 93.24%, respectively. Within the FL framework, FL-CNN achieved an accuracy of 95.27% under the IID scenario, while FL-CNN and FL-DNN achieved 95.09% and 94.76% under the Non-IID scenario. Our findings illustrate the feasibility of applying FL for IoT malware detection based on the analysis of IoT binary files with regard to CFG-based features, which can be extended to broader IoT domains such as health care, industrial IoT, and autonomous vehicles. © 2025 IEEE.

关键词： Federated learning

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A Multi-label Classification Method for Positive and Unlabeled Dataset. 4

A Multi-label Classification Method for Positive and Unlabel...

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4th Asia Service Sciences and Software Engineering conference, ASSE 2023

作者： Takahashi, Hikaru Qiangfu, Zhao University of Aizu Computer Science and Engineering Department Major in Computer Information System Japan University of Aizu Computer Science and Engineering Department Japan

ISBN: (纸本)9798400708534

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning. © 2023 ACM.

关键词： machine learning

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Mobile App for Kidney Patients to Provide a Comprehensive Solution to Manage Disease 4

Mobile App for Kidney Patients to Provide a Comprehensive So...

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4th international Informatics and Software Engineering conference, IISEC 2023

作者： Madubashana, C.P. Rupasinghe, M.R.S.N. De Silva, W.P.S. Lokesha, Weerasinghe thamaraka, G.I. thamali, Kelegama Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350318036

Chronic Kidney Disease (CKD) is a common and critical health problem that calls for effective disease treatment and careful kidney donor selection. this research introduces a mobile application made to help medical professionals manage CKD patients successfully and effectively. the app consists of four main parts: A module based on medical report scanning using OCR and NLP for patient summary generation, patient classification based on their health state using machine learning algorithms, a personalized chatbot specific for CKD patients to provide health references and meal plans and a donor selection module based on a tailored point schema. Python is used to create the backend of the software, Android is used for the front end, and MongoDB is used for the database. machine learning models improve several of the app's features. Results show that the components are functional, and noted shortcomings are being fixed in anticipation of future improvements. the software has the potential to revolutionize the management of CKD and the choice of kidney donors, improving patient outcomes and treatment effectiveness. © 2023 IEEE.

关键词： machine learning

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Early Prediction of Student Performance with LSTM-Based Deep Neural Network 47

Early Prediction of Student Performance with LSTM-Based Deep...

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47th IEEE-computer-Society Annual international conference on computers, Software, and applications (COMPSAC)

作者： Wan, Han Li, Mengying Zhong, Zihao Luo, Xiaoyan Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Beihang Univ Image Proc Ctr Sch Astronaut Beijing Peoples R China

ISBN: (纸本)9798350326970

the shift to hybrid teaching during the COVID-19 pandemic brought about a real challenge to predict student performance and conduct timely interventions on at-risk students. this study proposed a deep neural network supporting the early prediction of student performance. Bidirectional LSTM, Global Average Pooling, and TIME MASK structure were utilized in the improved GritNet model. Subsequently, this study optimized the hyperparameters with the aid of the hill-climbing algorithm. Finally, on-campus data sets were used in experiments to evaluate the model's performance. Data were collected from a course that carried out multiple iterations from Fall 2021 to Fall 2022. In Fall 2022, the proposed model achieved a ROC-AUC value of 95.47% in the 8th week, while the baseline model only achieved 91.44% in the same week. Besides, the proposed model achieved a ROC-AUC value of 89.67% in the 4th week, which meant it had acceptable prediction performance in the early stage. the experimental findings demonstrated that the model was capable of predicting the academic performance of students in hybrid courses early on.

关键词： learning analytics At-risk student prediction Early prediction Hill-climbing Deep learning

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