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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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Multi-Agent Deep Reinforcement learning Offloading Algorithm Based on Resource Reservation and Task Adjustment 4

Multi-Agent Deep Reinforcement Learning Offloading Algorithm...

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4th international conference on computer Engineering and application, ICCEA 2023

作者： Qu, Hua Gao, Qian Zhao, Jihong Xue, Yucheng School of Software Xi'an Jiaotong University Shaanxi Xi'an China School of Information and Communication Engineering Xi 'An Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9798350347548

With the popularization of the Internet of things and mobile Internet, the data shows explosive growth. Adopting cloud computing mode will lead to network congestion and high energy consumption, and can't meet the low latency requirements of applications. Computing offloading technology offloads tasks to the edge of the network for processing, which can effectively make up for the shortcomings of the cloud computing model. However, in the multi-user and multi-server edge system, it is challenging to formulate the task offloading strategy. In this paper, we consider divisible and latency sensitive tasks and edge server load dynamics. then we model the edge computing scenario, and formulates a task offloading problem to minimize the overall task processing latency while meeting the application latency requirements. We propose a resource reservation strategy to expand the candidate destination nodes for task offloading and propose task adjustment strategy to adjust the resource allocation of elastic tasks which can improve the task carrying capacity of MEC system and then improve the average quality of experience. We use a multi-agent deep reinforcement learning method to solve optimization problems. the simulation results show that our method can significantly reduce the application latency rate and improve the average quality of experience and resource utilization of MEC system. © 2023 IEEE.

关键词： Reinforcement learning

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DeepTestDroid A Platform for Automated application Testing Using Deep learning 4

DeepTestDroid A Platform for Automated Application Testing U...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Huzaifa, Md. Fabin, Fabiha Bushra Tabassum Natasha, Nazia Fakir, Imran Falguni, Sabiha Afsana Hasan, Mahamudul Ahmmad Rashid, Mohammad Rifat East West University Dept. Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350300192

Black and white box testing is used to measure an application. Testing can be complex and costly. Various testing techniques can measure multiple parts of an application. DeepTestDroid performs UI performance testing, which is black box testing. It can predict the density of an application's page from its wireframe picture. Higher density means a longer load time. UI performance testing can only be performed with human interaction. However, DeepTestDroid makes it automatic. To detect density, DeepTestDroid uses mobilenetV3Large. MobileNetV3Large was selected after an experimental analysis alongside other image processing models. this project can reduce the cost and step of UI performance testing. It will also make it easier to complete the testing phase of SDLC. © 2023 IEEE.

关键词： Image processing

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Exploring Amino Acid Indices for the Classification of Parkinson's Proteins with the aid of Deep learning 4

Exploring Amino Acid Indices for the Classification of Parki...

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4th international conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Baruah, Bornali Halder, Anindya Kumar, Ansuman North-Eastern Hill University Department of Computer Application Meghalaya Tura Campus India

ISBN: (纸本)9798350389128

Parkinson's disease (PD) is a progressive neurolog-ical condition characterized by a range of motor and non-motor symptoms, typically becoming noticeable during later stages of its progression. Detecting the disease early often relies on studying Parkinson's disease-related amino acid protein sequences. However, evaluating these sequences is challenging due to the complex nature of protein structure and the diverse physico-chemical properties involved. therefore, this study presents a novel approach using various commonly employed amino acid indices (AAIndex) extracted from protein sequences alongside a Gated Recurrent Unit (GRU)-based Deep learning (DL) model to differentiate between Parkinson's and healthy proteins. We investigate the efficacy of different AAIndex in transforming protein sequences into numeric information and evaluate their influence on classifying 1,719 PD proteins and 6,330 healthy proteins. the AAIndex polarizability parameter achieves the highest accuracy value of 92.90%. Additionally, other evaluation metrics (viz., precision, recall, and the F1 score) also exhibits similar performance on the polarizability parameter index. the outcomes of our investigation highlight that the polarizability parameter index turns out to be a promising amino acid feature index for detecting PD from the protein sequences. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Research on Quantum Phase Diagram of t-J Model Based on machine learning Tensor Network Algorithm 4

Research on Quantum Phase Diagram of t-J Model Based on Mach...

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4th international conference on Advances in Electrical Engineering and computer applications, AEECA 2023

作者： Li, Shenghao Wu, Yuanyuan Chongqing Vocational Institute of Engineering School of Big Data and Internet of Things Chongqing China

ISBN: (纸本)9798350308082

By introducing the concepts and methods of machine learning, the traditional tensor network algorithm is upgraded to the machine learning tensor network algorithm in order to obtain more accurate results in complex systems. Under the context of the virtual time evolution tensor network algorithm established in the graded projection entangled pair state (gPEPS) representation of the two-dimensional strongly correlated electron infinite fermion system based on machine learning, this paper mainly investigates the quantum phase diagram, quantum phase transition and quantum entanglement of the two-dimensional t-J model related to high temperature superconductivity through the ground state energy. the research results showed that the t-J model has a phase separation line between the Heisenberg antiferromagnetic state with no vacancies and the state rich in vacancies, with the upper critical point at Jc=0.95t and the lower critical point at Jc=3.45t. It was found that at J=0.4t, in addition to the potential long-range ferromagnetic state, four different phases were composed in the different doping uniform regions: one wasis the superconducting phase with coexistence of charge and spin density waves and px(py) wave symmetry, one wasis the mixed superconducting phase with d+s wave superconducting pairing and spin triplet px(py) wave superconducting symmetry in the antiferromagnetic background, one wasis the superconducting phase with extended s-wave symmetry, and the other one iswas the superconducting phase with px(py) wave symmetry in the ferromagnetic background. © 2023 IEEE.

关键词： machine learning

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Review on Depression Detection Using machine learning Techniques 4th

Review on Depression Detection Using Machine Learning Techni...

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4th international conference on Recent Trends in machine learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Vidya, S. Raju, G.T. Vinayaka Murthy, M. REVA University Bengaluru560064 India Department of MCA Bangalore Institute of Technology Bengaluru560004 India Department of Computer Science and Engineering Sri Jagadguru Chandrashekaranatha Swamiji Institute of Technology Karnataka Chickballapur562101 India School of Computer Science and Applications REVA University Bengaluru Kattigenahalli Yelahanka 560064 India

ISBN: (纸本)9789819994410

machine learning has achieved notable advancements in the medical field by improving the accuracy, precision, and analysis of diagnostics while also reducing labour-intensive tasks. With strong evidence, machine learning has exhibited the capability of detecting mental distress including depression. It is fact that a large proportion of the population is currently experiencing depression, which is considered to be a global burden of disease. Hence there is a critical need to develop an early depression detection model. this article aims to improve our understanding the present state of depression prediction models by exploring issues and identifying difficulties. this review relies on the examination of audio, video, images, PHQ questions, Social media (Twitter and Facebook), EEG signal depression detection models using different machine learning approaches. this paper reviews indicators of depression and provides a summary evaluation of diverse research studies encompassing their performance parameters. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Car Dent Detection Using Watershed Segmentation and Deep learning 4

Car Dent Detection Using Watershed Segmentation and Deep Lea...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Bhatlawande, Shripad Shilaskar, Swati Kumar, Saket Salunkhe, Parag Jaybhay, Sarthak VIT Dept. of e and Tc Pune India

ISBN: (纸本)9798350300888

Deep learning-based car dent detection is a new topic with enormous promise for a variety of industries, including insurance and resale businesses. Human examination is used in traditional approaches to find automotive dents, although this can be time-consuming and error-prone. Deep learning improvements have made it possible to use computer vision algorithms to automatically detect dents. the system has the potential to be employed by insurance companies, car dealerships, and car repair shops to identify car dents quickly and accurately. the proposed method consists of two stages: object detection and segmentation. In the first stage, the yolov5 model is trained on a dataset of car images to detect the location of dents in the image. In the second stage, watershed segmentation is applied to the detected dent regions to segment the dent area accurately. the proposed method is evaluated on a dataset of car images containing different types of dents. © 2023 IEEE.

关键词： Watersheds

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Exploring a Novel Methodologies for Beetroot Leaf Disease Severity Prediction: Federated learning and CNN 4

Exploring a Novel Methodologies for Beetroot Leaf Disease Se...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mehta, Shiva Kukreja, Vinay Sharma, Vikrant Manwal, Manika Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300888

this study explains how a federated learning Convolutional Neural Network (CNN) model is used to diagnose the various degrees of beetroot leaf diseases. Beetroot crops are essential to the world's food systems, but several illnesses often affect their quality and productivity. To suggest a CNN model using federated learning to analyse data from several clients while preserving privacy for a quick and precise diagnosis. the method analyses the severity of the five stages of beetroot leaf diseases using a federated learning technique across several local clients to use precision, recall, F1-score, and accuracy as performance measurements. the review showed that performance had improved consistently for all customers. As an example, Client X had a prediction accuracy of 0.97, which increased gradually to 0.98 for both Client P and Client R. Additionally, as shown by the macro-average (94.15-94.17%), weighted average (94.67%), and micro-average (94.62%) scores for top-performing customers, the model's performance remained stable and resilient throughout all severity levels. the study's findings support using a CNN model built on federated learning for scalable, effective, and privacy-preserving disease control in agriculture. the promising results suggest the potential for extending such approaches to other agricultural products, considerably advancing efforts to ensure global food security. the research also showed that the CNN model could learn and develop continuously in a federated learning scenario, highlighting its usefulness as a dynamic tool that adapts to new illness patterns. A paradigm shift in the approach to combating agricultural illnesses and increasing food supply is heralded by this study, which reveals a potential junction between machine learning and agricultural disease management. the findings motivate more research into the model's adaptability to other crops and pests, enhancing its contribution to resilient and sustainable farming systems. © 2023

关键词： Crops

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Health Disease Prediction Using machine learning and Internet of things 4

Health Disease Prediction Using Machine Learning and Interne...

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4th international conference on Intelligent Engineering and Management, ICIEM 2023

作者： Ashokrao, Patil Rahul Radhakrishna, Derle Deepak Sharma, Mukesh Bhad, Sandeep KTHM College Department of Computer Science MS Nashik India Rungta College of Pharmaceutical Sciences and Research CG Bhilai India Rungta College of Engg. and Technology Department of Electronics and Telecommunication Engg. CG Bhilai India

ISBN: (纸本)9798350341126

the Analysis of Disease Forecast using machine learning may be a framework that makes an exact prediction based on the data or side effects that the user enters into the framework and the infection it will cause. If the understanding isn't very accurate and the client really needs to know the type of illness he or she has experienced, it could be a framework that offers the client the tips and tricks to maintain the client's wellbeing framework and it provides a way to discover the illness using this forecast. the health industry plays a crucial role in treating patients' infections, so there are typically a few types of offer assistance for the health industry. By just getting the client's indications and using the framework, the health business can also profit from it. they will be able to tell in a reasonable amount of time. With the help of machine learning and the Python programming language with Tkinter interface, this Malady Expectation Utilizing machine learning has been completed in full. It also uses the dataset that the medical facilities already have access to and uses that to predict the spread of infection. IoT, cloud, and fog computing, which are popular issues in the field of illness detection and prediction, are, on the other hand, attracting the attention of the academic community. the Internet of things (IoT), cloud computing, and fog computing have all been used to develop a variety of healthcare solutions. © 2023 IEEE.

关键词： Forecasting

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Design of a Full Process License Plate Detection System Based on Deep learning and Cloud Platform System 4

Design of a Full Process License Plate Detection System Base...

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4th international conference on computer, Big Data and Artificial Intelligence, ICCBD+AI 2023

作者： Ren, Fei Zhang, Tao Zhang, ChunMei Yao, MengHan Wang, Jian Doma, Bonifacio T. Nanjing Intelligent Transportation Information Co. Ltd Intelligent Networking Engineering Technology Research Center NanJing China Mapua University School of Graduate Studies/School of Information Technology Manila Philippines Nanjing Intelligent Transportation Information Co. Ltd NanJing China Nanjing Intelligent Transportation Information Co. Ltd Technology Engineering Department NanJing China School of Graduate Studies School of Information Technology Mapua University Manila Philippines

ISBN: (纸本)9798350373233

As digital technology continued to evolve, license plate detection systems became crucial auxiliary systems for daily parking management. However, research on non-fixed license plate detection systems had long been a blind spot in related technological exploration. To address this issue, this paper employed deep learning technology to detect license plates at angles, in blurred conditions, and under low light, leveraging cloud technology for data processing. To meet data security and practical production requirements, the system stored and pushed detection results to a WeChat public account (mobile platform) through a cloud platform. Pushing results to the mobile platform via the cloud platform yielded excellent performance, demonstrating significant engineering application value. © 2023 IEEE.

关键词： Deep learning

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Identifying Coincidental Correct Test Cases with Multiple Features Extraction for Fault Localization 47

Identifying Coincidental Correct Test Cases with Multiple Fe...

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

作者： Wu, Yonghao Tian, Shuaihua Yang, Zezhong Li, Zheng Liu, Yong Chen, Xiang Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong Peoples R China

ISBN: (纸本)9798350326970

Spectrum-Based Fault Localization (SBFL) technique is widely applied for fault localization, identifying faulty statements potentially resulting in unexpected faulty programs' behavior. However, researchers have approved that Coincidental Correct (CC) test cases contained in test suites can negatively affect the accuracy of SBFL. Previous researchers sought to identify CC test cases through machine learning algorithms, but the feature representation is insufficient, leading to limited accuracy. To address this challenge, we propose the machine learning-based CC test cases Identification approach (MLCCI), which leverages multiple features extracted from the program under test to identify CC test cases and map the CC identification task to a learning problem. To evaluate the performance of MLCCI, we conduct experiments in the well-known dataset Defects4J. the experimental results compared with state-of-the-art baselines indicate that: (1) MLCCI achieves higher CC identifying accuracy, with the average Recall, Precision, and F-measure values of MLCCI are 65.93%, 71.69%, and 53.74%, respectively;(2) the fault localization accuracy of MLCCI with the Jaccard formula outperforms baselines, where the values of Accuracy@ 1, 3, and 5 are 347, 369, and 393, achieving the maximum 137.67%, 67.73%, and 47.74% improvement against baselines, respectively. Besides, we perform ablation analysis to reveal the effectiveness of features utilized in this study.

关键词： software debugging spectrum-based fault localization coincidental correct test case features extraction machine learning

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