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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3307 条记录，以下是331-340 订阅

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Smart Energy Conservation in Data Centers Using Machine Learning Based software-Defined Networking 6

Smart Energy Conservation in Data Centers Using Machine Lear...

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6th international conference on Energy Conservation and Efficiency, ICECE 2023

作者： Aslam, Haseeb Munawar, Saima Virtual University of Pakistan Department of Computer Science and Information Technology Dera Ghazi Khan Pakistan

ISBN: (纸本)9798350332193

the rapid growth of Data Centers (DC) poses the problem of heavy energy consumption. the servers consume most of the energy in the DC, but the network infrastructure also uses a great deal of energy. It is therefore necessary to minimize the energy consumption of DC. DC Energy Conservation Strategy has various directorates, including energy-efficient methods for servers and networks, blended for servers and networks, cooling and renewable energy. As the literature revealed, many existing ways are available, but there are many problems with the current strategies, such as the issues include loss of packets, systems with rigid approaches with low modifications, obsolete methodologies for newer hardware devices, methods tested on small networks, and the amount of energy saved compared to the risk and financial cost involved is not worth it. this research eliminates the risk of packet loss up to some extent by securing as well improving the network efficiency. the study aims to construct a way to save energy in DC by employing the software-defined networking technique and modifying the existing Elastic Tree to maximize power savings, minimize the performance effect, and guarantee fault tolerance. It can consider improving the existing optimizer in elastic tree methodology and introducing new factors like power control based on traffic pattern analysis. A Mininet emulator is utilized to develop a virtual DC network. An optimizer is designed using different models and includes a floodlight controller that monitors traffic flow and creates a custom network topology by tracking the traffic pattern in DC. this custom network topology keeps the network's essential components active and power down the unneeded parts, including many links and switches, to meet the best performance. the custom topology suggested demonstrating in J-Graph. this exploration further leads to optimizer analysis, a step toward improving smart energy conservation. © 2023 IEEE.

关键词： software defined networking

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NLP Tools for Epileptic Seizure Prediction Using EEG Data: A Comparative Study of three ML models 6th

NLP Tools for Epileptic Seizure Prediction Using EEG Data: A...

引用

6th international conference on Nanotechnologies and Biomedical engineering, ICNBME 2023

作者： Iapascurta, Victor Fiodorov, Ion Department of Software Engineering and Automation Technical University of Moldova Chisinău Moldova Department of Anesthesia and Intensive Care N. Testemitanu State University of Medicine and Pharmacy Chisinău Moldova

ISBN: (纸本)9783031427817

Natural Language Processing (NLP) is an ever-evolving field of computer science that involves the development of algorithms that can process, analyze and understand human language. One of the most exciting areas of NLP is the creation of NLP language models with applications across almost every industry. However, most people only associate NLP with its traditional use in language translation, sentiment analysis, and chatbots. In reality, there are many less-common uses for NLP models that have the potential to transform businesses, improve customer experiences, and even save lives. In the healthcare industry, NLP models can be used to analyze unstructured medical data and help diagnose and treat patients more efficiently. For example, NLP can be used to analyze clinical notes, lab results, and other data combing through vast amounts of data to identify patterns and create targeted treatment plans. NLP-based medical diagnosis is still in its infancy, but it has the potential to revolutionize the healthcare industry in the coming years. this article explores a less common use of machine-learning language models built on transformed EEG data for epilepsy prediction using the Kolmogorov-Chaitin algorithmic complexity as the first step in generating text-like data, which are finally used for building machine learning models. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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Computer Vision-Based Personnel Management in Complex Scenarios of Industrial Enterprises 6

Computer Vision-Based Personnel Management in Complex Scenar...

引用

6th international conference on software engineering and Computer Science, CSECS 2023

作者： Chen, Yongqi Li, Boying Liu, Yanqi Shi, Weining Yang, Haoyu Jiang, Yang Lai, Yongxuan Xiamen Tobacco Industry Co. Ltd. Information Technology Department Xiamen China School of Informatics Xiamen University Xiamen China Shenzhen Research Institute School of Informatics Xiamen University Xiamen China School of Mathematics and Information Engineering Longyan University Longyan China

ISBN: (纸本)9798350306378

Some companies have extremely strict requirements for the safety management of personnel and equipment in the production plant, including real-time counting of the number of personnel. However, in practice, it may not be convenient to use traditional access control for real-time counting due to production efficiency. this study aims to design a video streaming-based visual counting method for personnel at critical entrances and exits. To achieve this goal, we extensively evaluated and compared the performance of traditional target detection algorithms to find the one that is suitable for our task scenario. Subsequently, we delved into the RCNN [7] family of algorithms and the YOLO [8], [9]family of algorithms for both two-phase target detection algorithms and single-phase target detection algorithms. We used manually annotated datasets to train the relevant models and tested these algorithms for performance evaluation in task scenarios. In the end, we chose the YOLOv7 and StrongSORT [9], [13] based algorithms to successfully implement the personnel counting task and achieved a small error through model optimization to make it suitable for complex scenarios. © 2023 IEEE.

关键词： Object detection

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Detection and Classification of Brain Tumor and 3D Reconstruction of MRI 5

Detection and Classification of Brain Tumor and 3D Reconstru...

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5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Kongalla, Maheshwari Desai, Rhutuja Vangepuram, Benson Padma, T. Gokaraju Rangaraju Institute of Engineering and Technology Department of ECE Hyderabad India

ISBN: (纸本)9798350367171

Brain tumors pose a significant global health concern, with millions affected worldwide, often resulting in death. In the United States, approximately 70,000 people have primary brain tumors, making brain cancer the 10th most common tumor in India. Accurate diagnosis is crucial for initiating appropriate treatment, and improving patient survival. Treatment varies based on tumor type, size, location, and aggressiveness. Deep learning methods, like transfer learning-based techniques using traditional classifiers, are increasingly used for tumor detection in MRI images. Our study explores VGG-16 and VGG-19 transfer learning methods, leveraging 3D Slicer's adaptable algorithm for reconstructing MRI scans of brain tumors. By utilizing robust software components like Tcl/Tk, VTK, and ITK, alongside an intuitive application layer, we enable advanced functionalities such as image-guided surgery and brain mapping. Our work highlights 3D Slicer's alignment with research goals, despite challenges like prolonged diagnosis times due to workload pressures, potentially leading to errors. this predictive models enhance patient outcomes in this critical medical domain, with improved ROC-AUC score >0.9 and classification accuracy of ∼99%. © 2024 IEEE.

关键词： Deep learning

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Phase Transition Material Based Wideband, Tunable, and Fabrication Friendly Metamaterial Absorber 6

Phase Transition Material Based Wideband, Tunable, and Fabri...

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6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Faruk, Md. Omar Khaleque, Abdul Arafat Hossain, A.B.M. Rahman, Md. Arafat Rajshahi University of Engineering & Technology Dept. of Electrical & Electronic Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350370836

this work studies three simpler models of meta-material absorber (MA) and proposes an optimum structure for achieving broad bandwidth and high tuneability within the terahertz frequency range, utilizing vanadium dioxide (V O2) as the phase transition material. the optimization, simulation, and analysis of the best among the three models were performed using CST Studio Suite with the Finite Integration Technique. Subsequently, the main absorption spectrum was verified using the finite element method-based COMSOL Multiphysics software. the simulation result demonstrates that the MA achieves absorption above 90% within the range of frequencies from 2.94 thz to 6.12 thz, providing an operating bandwidth of 3.18 thz. Importantly, the absorptivity of this suggested structure may be continuously tuned from 2.3% to 100% by adjusting the conductance of V O2, enabling the transition of V O2 from an insulating stage to a metallic stage. Additionally, the best model exhibits insensitivity to polarization and has significant absorption for incident angles up to 60°. the recommended structure is validated using the impedance matching theorem and interference cancellation condition. the physical mechanics of the MA is elaborated by analyzing the electromagnetic field distribution around the resonance frequency. Potential applications of this absorber include sensors, modulation, imaging, terahertz filters, optical switching, and more. © 2023 IEEE.

关键词： Metamaterials

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Mental Health Diagnosis Survey Using Question Reduction,Advanced Generation algorithms and Contactless UI system 6

Mental Health Diagnosis Survey Using Question Reduction,Adva...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Jaybhaye, Sangita Hirve, Tejas Chavan, Suraj Talele, Ajay Kolekar, Yash Bambal, Sumedh Tadkase, Anuj Vishwakarma Institute of Technology Maharashtra Pune411037 India Department of Information Technology Vishwakarma Institute of Technology Maharashtra Pune411037 India Department of Electronic and Telecommunication Engineering Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9798350369748

In this fast-moving world, prioritizing mental health has become very important for overall wellbeing, this paper present a software-based survey to assess and diagnose mental health symptoms effectively. the system has user friendly UI and tries to maximize the user engagement through various machine learning models. the system effectively addresses various issues of data storage in present systems ensuring that the critical mental health cases are addressed properly. In addition to this it also incorporates a question reduction algorithm which reduces the time taken to give the survey, which enhances user engagement and thereby enhances validity and user completion rates of survey. Considering future needs, the system integrates natural language processing and web scraping to dynamically generate questions related to current news affairs through which future mental health symptoms can be incorporated into the survey. Such systems ensure that the models stay relevant and responsive to upcoming mental health challenges. © 2024 IEEE.

关键词： Electronic health record

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Harnessing feature engineering to improve machine learning: A review of different data processing techniques 6

Harnessing feature engineering to improve machine learning: ...

引用

6th international conference on Intelligent Systems and Computer Vision, ISCV 2024

作者： Abdelouahed, Sabri My Abla, Rahmouni Asmae, Ennaji Abdellah, Aarab University Sidi Mohamed Ben Abdallah Faculty of Sciences Computer Science Department Fez Morocco University Sidi Mohamed Ben Abdallah Faculty of Sciences Physics Department Fez Morocco

ISBN: (纸本)9798350350180

this study delves into the critical realm of feature engineering, examining its influence on the performance of various machine learning classifiers under different data preprocessing conditions. Our research encompasses an array of algorithms, including Support Vector Machine (SVM), Random Forest, Decision Trees, Artificial Neural Networks (ANN), and K-Nearest Neighbors (KNN). A key aspect of our methodology is the employment of Grid Search to systematically explore a wide range of hyperparameter configurations for each classifier. Focusing on handling missing values, outliers, and data scaling, we assess the impact of these feature engineering techniques on model performance. the evaluation is based on metrics such as accuracy, recall, precision, and F1-score, providing a comprehensive comparative analysis. the results offer insightful revelations about the differential impacts of feature engineering strategies on each algorithm's predictive accuracy and overall effectiveness. In particular, the study highlights how specific algorithms, notably Random Forest demonstrate robust performance across varied data scenarios. these findings contribute significantly to the understanding of feature engineering in machine learning, offering practical guidance for selecting and optimizing data preprocessing methods. this research underscores the paramount importance of fine-tuning feature engineering approaches and lays a foundation for future advancements in optimizing machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Image Classification Using Optimized Convolution Neural Network 6

Image Classification Using Optimized Convolution Neural Netw...

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6th Parul international conference on engineering and Technology, PICET 2024

作者： Chegoni, Ramesh SriLakshmi, V. Ramana, R. Sai Venkata Rajasekhar, Veeramreddy Changala, Ravindra thatha, Venkata Nagaraju Department of Information Technology CMR Technical Campus Hyderabad India B V Raju Institute of Technology Narsapur India Department Madanapalle Institute of Technology & Science India Hyderabad India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798350369748

Conventional artificial neural networks and machine learning algorithms encountered difficulties in fulfilling the processing needs for extracting features from large-scale images and training models. Furthermore, they showed poor efficiency and accuracy in classifying images. In order to lay a firm groundwork and offer aid for the categorization and recognition of large datasets, this study introduces a deep learning model for classification of images. the feature extraction technique of CNNs is refined by mitigating noise and adjusting model parameters, resulting in the development of an advanced CNN architecture for deep learning in image classification. the efficacy of the proposed model is assessed through tests that scrutinize the correlation between iteration numbers and accuracy of various network models are commonly employed in image classification. Notably, the research model surpasses its counterparts in terms of classification accuracy, indicating its superiority in the realm of image classification. the study contributes valuable insights by enhancing the understanding of CNNs and offering a more effective model for image categorization, supported by rigorous evaluations. © 2024 IEEE.

关键词： Adversarial machine learning

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LOGI: An Empirical Model of Heat-Induced Disk Drive Data Loss and Its Implications for Data Recovery 18

LOGI: An Empirical Model of Heat-Induced Disk Drive Data Los...

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18th ACM international conference on predictive models and Data Analytics in software engineering (PROMISE)

作者： Ahmad, Hammad Holoday, Colton Bertram, Ian Angstadt, Kevin Sharafi, Zohreh Weimer, Westley Univ Michigan Ann Arbor MI 48109 USA MathWorks Natick MA USA St Lawrence Univ Canton NY USA Polytech Montreal Montreal PQ Canada

ISBN: (纸本)9781450398602

Disk storage continues to be an important medium for data recording in software engineering, and recovering data from a failed storage disk can be expensive and time-consuming. Unfortunately, while physical damage instances are well documented, existing studies of data loss are limited, often only predicting times between failures. We present an empirical measurement of patterns of heat damage on indicative, low-cost commodity hard drives. Because damaged hard drives require many hours to read, we propose an efficient, accurate sampling algorithm. Using our empirical measurements, we develop LOGI, a formal mathematical model that, on average, predicts sector damage with precision, recall, F-measure, and accuracy values of over 0.95. We also present a case study on the usage of LOGI and discuss its implications for file carver software. We hope that this model is used by other researchers to simulate damage and bootstrap further study of disk failures, helping engineers make informed decisions about data storage for software systems.

关键词： n-gram model disk drive damage data recovery software

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Joint Simulation Study of Glass Greenhouse Roof Cleaning Machine Based on Adams and Matlab 6

Joint Simulation Study of Glass Greenhouse Roof Cleaning Mac...

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2023 6th international conference on Advanced Algorithms and Control engineering, ICAACE 2023

作者： Xu, Rui Wu, Mingge Shen, Yunde Chen, Zhenmu Yin, Haiyan Zou, Hantao College of Mechanical and Electrical Engineering Wenzhou University Zhejiang Province China Institute of Laser and Optoelectronics Intelligent Manufacturing Wenzhou University Zhejiang Province China

the glass greenhouse roof cleaning machine is driven by dual motors, and there are problems with machine deflection and inconsistent speed of the two wheels during operation, to solve these problems, a joint simulation method based on Adams and Matlab is used in this project. Firstly, build detailed 3D models using SolidWorks software and Adams software, and set the necessary constraints and material properties for the models;then, the control system is modeled with Fuzzy-Proportion Integration Differentiation (PID) control algorithm in Matlab/Simulink module;finally, a joint simulation was performed using Adams software and Matlab/Simulink software. through the analysis of the results of the joint simulation, the problem of inconsistent deflection and the two-wheel speed of the machine is optimized after the introduction of the Fuzzy-PID controller. © Published under licence by IOP Publishing Ltd.

关键词： Glass

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