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检索条件"任意字段=Proceedings of the 9th International Conference on Predictive Models in Software Engineering"

共 2518 条记录，以下是121-130 订阅

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A Novel Ant Colony Based Genetic Approach Neural Learning for Defect Prediction in software Products 4

A Novel Ant Colony Based Genetic Approach Neural Learning fo...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Gurram, Rajendra Babu Reddy, M. Tummala, Bindu Madhavi Phaneendra, K. Ashok Reddy, P. Durga Prasad, D. Lakireddy Bali Reddy College of Engineering Department of It Mylavaram India Krishna University Department of Computer Science Machilipatnam India Vr Siddhartha Engineering College Department of Cse Vijayawada India Lakireddy Bali Reddy College of Engineering Department of Cse Mylavaram India Pscmr College of Engineering and Technolony Department of Cse Vijayawada India

ISBN: (纸本)9798350349856

software defects prediction is a process that involves using various techniques and models to anticipate and estimate the occurrence of defects or bugs in software during its development or maintenance lifecycle. the main goal of software defects prediction is to identify areas of the software that are more likely to have defects, so developers and testers can focus their efforts on those specific parts to improve the software's overall quality. By predicting potential defects early in the software development lifecycle, developers and testers can focus on addressing these issues before they become critical problems. this leads to reduced rework and lowers the cost of fixing defects. Predicting defect-prone areas helps in allocating testing and review resources more effectively. Instead of evenly distributing efforts, teams can concentrate on the parts of the software that are more likely to have defects, leading to better testing coverage. In most of the existing approaches, the prediction of defects are carried out using statistical models and machine learning models. Imbalanced datasets, where the number of defect-free instances significantly outweighs the number of defective instances, can lead to biased predictions. Statistical models may struggle to capture the patterns of defect-prone instances adequately, leading to lower recall or sensitivity. the proposed model implements genetic neural network for prediction of defects at early stages. ANNs are capable of capturing complex non-linear relationships between software metrics and defect occurrences. this is particularly advantageous when dealing with software defects, as the relationships between metrics and defects may not always be linear. Genetic algorithms can be used to automatically search for the optimal subset of features and fine-tune hyper parameters of the ANN. this helps in finding the most relevant features and achieving better predictive performance (Abstract). © 2024 IEEE.

关键词： Defects

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Using Model predictive Control in Renewable Energy Sources 9

Using Model Predictive Control in Renewable Energy Sources

引用

9th IEEE international conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Rosli, Anis Najwa Yusoff, Siti Hajar Mansor, Hasmah Sapihie, Siti Nadiah Mohd Fahmi, Muhammad Aqil Muqri Muhamad International Islamic University Department of Electrical and Computer Engineering Malaysia Malaysia Petronas Research Sdn. Bhd. Malaysia

ISBN: (纸本)9798350343380

the integration of renewable energy sources (RES) poses challenges due to their intermittent nature and the requirement for grid stability and reliability. this study explores the application of Model predictive Control (MPC) as an advanced control strategy for effective RES integration. MPC utilizes mathematical models and prediction algorithms to optimize power flow, manage system dynamics, and mitigate the intermittent nature of RES. Simulations conducted using Simulink and MATLAB platforms demonstrate the effectiveness of the MPC controller in improving grid stability, optimizing power generation, and accommodating fluctuating RES output. the results highlight the potential of MPC to enhance the integration of RES into the power grid, paving the way for a sustainable and resilient energy infrastructure. Additionally, a comparison between the MPC controller and the Proportional Integral (PI) controller was performed, indicating that MPC outperforms the PI controller. © 2023 IEEE.

关键词： Model predictive control

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predictive Data Analysis Platform, for Optimizing and Automating the Distribution of Car Insurance Products, Based on Telematic Data 24th

Predictive Data Analysis Platform, for Optimizing and Automa...

引用

24th international TRIZ Future conference

作者： Barna, Erik Barcau, Emanuel Ravaru, Raul Life Is Hard Software Co Floresti Romania

ISBN: (纸本)9783031759253;9783031759239;9783031759222

the purpose of this article is to present an AI technology based innovative approach, involved in a platform for digitizing processes in the Car Insurance Business field, which allows the end user (the broker of insurance or the insurer) to base his decisions on robust information to help him make a robust business forecast. predictive analytics for insurance entails the use of special technology to sift through and analyze historical telematics data and consumer trends in effort to project future behavior. Obviously combining the AI based IT technologies with mathematical and statistical models, the integrated digitalized platform presented in this article involve also both Data Modeling and Deep Learning. Practically, the software platform presented in this article represents the backbone of any insurance brokerage business, because without such an application it is impossible to manage business processes that have hundreds or even thousands of sales agents. From structural point of view, this platform has a layered structure, the first layer being the basic brokerage application, this being extended with innovative predictive computational modules as upper layers. the technical implications mainly refers to the innovative way of involving in the digitalized system a massive amount of telematics data. the expected business implications consists in offering, by an innovative digitalized solution, the possibility for the final client (insurance broker or insurer) to receive information that will help him make a forecast of business.

关键词： Computational models Computational Science and engineering Telematics Data Mining Clustering Methods

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Deep Learning based Frailty Detection in Conjuction with Telemedicine 9

Deep Learning based Frailty Detection in Conjuction with Tel...

引用

9th IEEE international conference for Convergence in Technology, I2CT 2024

作者： Badola, Ayush Chauhan, Rahul Bhatt, Chandradeep Devliyal, Swati Kumar, Gotte Ranjith Pillai, Rudresh Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India Chitkara University Institute of Engineering And Technology Chitkara University Punjab India

ISBN: (纸本)9798350394474

Telemedicine is a service provided by healthcare providers to patients seeking medical attention remotely. this paper explores the role of Artificial Intelligence (AI) and Deep Learning in enhancing the quality of telemedicine services. AI which includes deep learning, data science, and machine learning, enables the development of smart applications that can assist both patients and medical professionals in a more efficient way. Deep Learning is a subset of machine learning. this paper explores the related works, and smart AI models to help physicians to improve telemedicine services. this paper explores the implementation of AI in healthcare services. In this research paper, we propose the implementation of our Heart Attack Risk predictive model to overcome the challenges in telemedicine services, achieving 98% accuracy. the traditional healthcare process involves multiple steps, scheduling an appointment with the cardiologist undergoing prescribed tests, collecting the test results, and finally receiving a recommendation for the treatment. A theoretical approach of deploying a model within a web portal to eliminate the frictions is discussed in this paper which has the potential to revolutionize remote medical treatment. © 2024 IEEE.

关键词： Telemedicine

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Internet Financial Risk Assessment Based on Hybrid Neural Network Algorithm 9

Internet Financial Risk Assessment Based on Hybrid Neural Ne...

引用

9th international conference on Systems and Informatics, ICSAI 2023

作者： Fu, Menghan Jiang, Zhuo Su, Jie Zhou, Ling Cufe School of Management Science and Engineering Beijing China Bnu School of Social Development and Public Policy Beijing China

ISBN: (纸本)9798350383706

High occurrence rate, complex types, powerful contagion, and considerable risk repercussions are characteristics of internet financial risks. One of the most important aspects of financial risk control is building an online system for assessing financial risk. the current body of assessment research only offers a restricted mix of quantitative evaluation models and a broad and varied indicator system, which is unable to satisfy both the objective standards of the models and the comprehensiveness of the indicators at the same time. through empirical analysis, this study shows that the particle swarm optimization convolutional neural network model has the highest predictive accuracy and can effectively capture the dynamics of Internet financial risks. It also completely considers the risk manifestations of both Internet characteristics and financial nature. © 2023 IEEE.

关键词： Risk assessment

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Effective Feature Extraction Framework to Improve Network Intrusion Detection System 9

Effective Feature Extraction Framework to Improve Network In...

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9th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering, WIECON-ECE 2023

作者： Mv, Hari Vinayak Jarin, T. Raja, Kiruba thangam Jyothi Engineering College APJ Abdul Kalam Technological University Department of ECE Thrissur India Jyothi Engineering College APJ Abdul Kalam Technological University Department of EEE Thrissur India School of CSE and Information Systems Vellore Institute of Technology Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350319651

A network intrusion detection system (NIDS) is a crucial component of a robust cybersecurity strategy. Its primary purpose is to continuously monitor network traffic and detect suspicious or malicious activity that could be indicative of a cyberattack or unauthorized network access. the effectiveness of NIDS depends heavily on the techniques we use to boost the classification accuracy of intrusion and minimize the computational difficulty while performing training and testing. the high volume of network traffic combined with its large number of features will increase the classification time. With the recent emergence of deep learning techniques, scientists have shown interest in learning dataset features, followed by the classification of intrusions. this study offers a novel method for extracting high-dimensional features from input data by employing a stacked sparse autoencoder. Simple machine learning models are then built using the remaining low-dimensionality features. Simulations were conducted, and the efficacy of binary and multiclass classifications was verified. the proposed method exceeds most of the other existing approaches in terms of performance. © 2023 IEEE.

关键词： Feature extraction

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A Novel Approach to Fall Detection System using Computer Vision 9

A Novel Approach to Fall Detection System using Computer Vis...

引用

9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Hegde, Adwait Patil, Ojas Belurgikar, Soham Jani, Preetida Vinayakray Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798350397376

the fall causes minor to major injuries in human beings due to accidents, weakness in muscles, loss of balance etc. therefore, the researchers have given the solutions for various fall detection systems using sensors, actuators, accelerometers along with integration of software modules. these proposed solutions are effective within the specific scope of their usage, but they require tight integration between hardware and software modules. Considering this, in this paper, an attempt has been made to provide a software solution by using computer vision approaches. the proposed system determines the fall by detecting and measuring the human body's change in the center of gravity, inclination angle and velocity of fall. New features have been highlighted for the development of further fall detection models. © 2023 IEEE.

关键词： Computer vision

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Innovative Sensor-Based Approach for Predicting Foot Ulcers in Diabetic Patients Using Machine Learning models 5

Innovative Sensor-Based Approach for Predicting Foot Ulcers ...

引用

5th international conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Begum, S. Ariffa Bhavya, K. Sree, B. Biindhu Lavanya, M. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798331530013

Foot ulcers, particularly in diabetic patients, are a significant health concern that can lead to severe complications if left untreated. Early detection of foot ulcers is critical for preventing infections, reducing hospitalizations, and improving the quality of life for at-risk individuals. this project aims to develop an automatic detection system for foot ulcers using an integrated approach that combines hardware sensors and predictive analytics software. the hardware consists of a foot temperature sensor and a pressure sensor to monitor the conditions that contribute to ulcer formation. the software, powered by machine learning algorithms, collects, preprocesses, and analyzes the data from these sensors to build a predictive model capable of detecting early signs of foot ulcers. By leveraging this integrated system, the solution provides a non-invasive, real-time detection method that can be deployed in clinical settings or as part of home monitoring systems for high-risk patients. this technology has the potential to enhance early diagnosis, reduce complications, and lower healthcare costs by enabling timely interventions. In this various predictive model like support vector machine, naive bayes, decision tree, and random forest has been implemented successfully and the results are compared. this technology has the potential to enhance early diagnosis, reduce complications, and lower healthcare costs by enabling timely interventions. © 2024 IEEE.

关键词： Support vector machines

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Digital Twin System for River Network Based on Graph Neural Network 36

Digital Twin System for River Network Based on Graph Neural ...

引用

36th international conference on software engineering and Knowledge engineering, SEKE 2024

作者： Jin, Zishuo Ye, Feng Zhang, Peng Hohai University NanJing China Jiangsu water Conservancy Engineering Planning Office NanJing China

ISBN: (纸本)1891706594

River network management presents a significant challenge in the field of water conservancy, and the subsequent advancements in digital technology offer a promising avenue for addressing this challenge. However, the utilization of cutting-edge technologies like digital twin and deep learning within the water conservancy domain remains largely experimental, hampered by the absence of applicable models, authentic scenarios, and viable solutions. To address this, we propose a river network prediction and response system based on digital twin. this system leverages an improved graph attention network to discern spatial correlations within the detection data of individual sites, integrating bidirectional long short-term memory neural network for predictive analysis. Concurrently, we have developed a suite of dynamic visualization tools designed to streamline real-time situation analysis for users. through the establishment of an interactive digital twin river network system, we furnish a fresh strategic framework for addressing water conservancy challenges. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Rivers

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Development of distributed vibration monitoring system with high synchronous acquisition accuracy 9

Development of distributed vibration monitoring system with ...

引用

9th Annual international conference on Network and Information Systems for Computers, ICNISC 2023

作者： Liu, Song Hu, ZeChao Shi, Liang He, MengXuan Naval University of Engineering Wuhan China National Key Laboratory on Ship Vibration and Noise Wuhan China

ISBN: (纸本)9798350306682

Vibration monitoring technology is an important means to recognize the state of machines and equipments, and has been widely used in industrial applications, such as predictive maintenance. With the rapid development of industry 4.0, the scale of industrial system increases, which requires large-scale vibration monitoring system for data acquisition. However, how to implement large-scale vibration monitoring remains a challenging problem. In this paper, a distributed vibration monitoring system is designed to solve the scalability problem of large-scale vibration monitoring. In order to realize high-speed signal acquisition, we design a hybrid architecture of FPGA+MCU, in which FPGA is used for concurrent acquisition and MCU is used for distributed control and communication. To provide real-time transmission of collected data, industrial Ethernet EtherCAT is used for transmission. In order to ensure the synchronization of different distributed vibration monitoring node, a high accuracy synchronization scheme is proposed, in which the software and hardware are jointly optimized. the experimental results show that the designed system can achieve high synchronization accuracy, and the average synchronization error is less than 20ns. © 2023 IEEE.

关键词： Synchronization

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