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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques

Comparative analysis of Start-up Success Rate Prediction Usi...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Mohammad Riyaz Belgaum Ediga Kasi Viswanath Pavukolla Chakradhar Ediga Chenna Kesava Goud Kuruva Chandu Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success.

关键词： Analytical models Accuracy Social networking (online) Machine learning Companies Predictive models Programming

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Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds

arXiv

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arXiv 2024年

作者： Fu, Liqun Tong, Jingwen Lin, Tongtong Zhang, Jun The School of Informatics Xiamen University Xiamen361005 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple challenges. This paper studies quality of experience (QoE)oriented resource allocation in MEC while considering user diversity, limited resources, and the complex relationship between allocated resources and user experience. We introduce a closed-loop online resource allocation (CORA) framework to tackle this problem. It learns the objective function of resource allocation from the historical dataset and updates the learned model using the online testing results. Due to the learned objective model is typically non-convex and challenging to solve in real-time, we leverage the Lyapunov optimization to decouple the long-term average constraint and apply the prime-dual method to solve this decoupled resource allocation problem. Thereafter, we put forth a data-driven optimal online queue resource allocation (OOQRA) algorithm and a data-driven robust OQRA (ROQRA) algorithm for homogenous and heterogeneous user cases, respectively. Moreover, we provide a rigorous convergence analysis for the OOQRA algorithm. We conduct extensive experiments to evaluate the proposed algorithms using the synthesis and YouTube datasets. Numerical results validate the theoretical analysis and demonstrate that the user complaint rate is reduced by up to 100% and 18% in the synthesis and YouTube datasets, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DNN for Advanced Image Analysis

MobileDNN: Leveraging the Potential of Hybrid MobileNetV2-DN...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Roseline Oluwaseun Ogundokun Rytis Maskeliūnas Robertas Damaševičius Marion Olubunmi Adebiyi Department of Computer Science Landmark University Omu Aran Nigeria Department of Multimedia Engineering Kaunas University of Technology Lithuania Faculty of Informatics Kaunas University of Technology Kaunas Lithuania

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

computer vision systems designed for posture recognition could offer valuable solutions in the healthcare sector, improving healthy ageing and supporting elderly individuals in their daily routines. One area of interest is the automatic detection of falls, which has garnered attention from computer vision and pattern recognition communities. While many existing methods rely on wearable sensors, which demonstrate high accuracy in fall detection, some individuals may be reluctant to wear such devices. Consequently, alternative approaches, such as vision-based techniques, have been developed. The proposed method utilizes a vision-based system that employs a convolutional neural network (CNN) to recognize and categorize various postures (e.g., sitting, lying, standing) in each frame, if available. This approach begins by detecting the person in the frame, applying background subtraction for human body shape extraction, and associating each daily activity with the extracted shape. The proposed model achieved an accuracy of 93.75%, which shows an outstanding performance over existing models. Subsequently, it estimates bounding boxes encompassing the predicted person's body shape. Overall, the proposed technique shows promising results for indoor monitoring systems.

关键词： computer vision Accuracy Shape computer architecture Feature extraction Convolutional neural networks Sustainable development

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Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

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computers, Materials & Continua 2022年第9期72卷 5117-5134页

作者： Hiam Alquran Wan Azani Mustafa Isam Abu Qasmieh Yasmeen Mohd Yacob Mohammed Alsalatie Yazan Al-Issa Ali Mohammad Alqudah Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Faculty of Electrical Engineering Technology Campus Pauh PutraUniversiti Malaysia Perlis02000ArauPerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia Perlis(UniMAP)02000ArauPerlisMalaysia The Institute of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Department of Computer Engineering Yarmouk UniversityIrbid21163Jordan

Cervical cancer is screened by pap smear methodology for detection and classification *** smear images of the cervical region are employed to detect and classify the abnormality of cervical *** this paper,we proposed the first system that it ables to classify the pap smear images into a seven classes *** smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images *** features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine(SVM)*** success of this proposed system in distinguishing between the levels of normal cases with 100%accuracy and 100%*** top of that,it can distinguish between normal and abnormal cases with an accuracy of 100%.The high level of abnormality is then studied and classified with a high *** the other hand,the low level of abnormality is studied separately and classified into two classes,mild and moderate dysplasia,with∼92%*** proposed system is a built-in cascading manner with five models of polynomial(SVM)*** overall accuracy in training for all cases is 100%,while the overall test for all seven classes is around 92%in the test phase and overall accuracy reaches 97.3%.The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer,which may lead to an increase in the survival rate in women.

关键词： Classification deep learning machine learning pap smear images resnet101 support vector machines

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Assessing the Maturity of Blockchain-based Supply Chain Management and Logistics: Comprehensive Examination of the Current Situation

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Procedia computer Science 2025年 253卷 2458-2467页

作者： Umut Şener Murat Tahir Çaldağ Ebru Gökalp Graduate School of Informatics Middle East Technical University Ankara Türkiye Department of Technology and Knowledge Management Baskent University Ankara Türkiye Department of Computer Engineering Hacettepe University Ankara Türkiye

Blockchain technology, a transformative force in digital transactions, has emerged as a significant enabler of secure, transparent, and traceable operations within supply chain management and logistics (SCML). Its immutable nature facilitates enhanced management of SCML activities by seamlessly integrating information flow throughout the value chain, and substantially increasing data integrity and operational reliability. While organizations are attracted to these capabilities, they still lack comprehensive guidelines to fully benefit from blockchain in SCML. There is a fundamental need to assist organizations in assessing and enhancing their blockchain-based SCML capabilities. Maturity models (MMs) are tools that help organizations assess and improve their processes, providing step-by-step guidelines from basic to advanced levels of maturity. Therefore, this study aims to identify and analyze MMs focused on blockchain-based SCML. An extensive systematic literature review (SLR) was conducted, and thirteen relevant studies were identified and evaluated against a comprehensive set of quality criteria. The analysis revealed that none of the existing MMs fully satisfied all criteria, highlighting a significant gap in the field. This research provides a thorough systematic analysis of MMs for blockchain-based SCML, offering an in-depth view of their levels, dimensions, research focuses, domains, industry targets, and their quality evaluation. The significance of this study lies in identifying the necessity for an MM specifically designed for blockchain-based SCML, addressing a critical gap in the current literature. These findings contribute to the growing body of knowledge on blockchain integration in SCML and provide a foundation for future research.

关键词： Blockchain Supply Chain Management Logistics Logistics 4.0 Maturity Model Assessment

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Advanced Automated Vulnerability Scoring: Improving Performance with a Fine-Tuned BERT-CNN Model

Advanced Automated Vulnerability Scoring: Improving Performa...

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International Symposium on Telecommunications, IST

作者： Seyedeh Leili Mirtaheri Andrea Pugliese Narges Movahedkor AmirHossein Majd Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Rende Italy Department of Computer Engineering Amirkabir University of Technology (Tehran Polytechnique) Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

The ever-growing prevalence of software systems necessitates robust and efficient vulnerability assessment methods. An automated vulnerability assessment model can significantly streamline this process, providing accurate identification of vulnerability severity levels based on the given textual descriptions. This paper addresses this critical need by proposing a novel vulnerability score predictor model. Our model uses Bidirectional Encoder Representations from Transformers (BERT) and deep learning to classify vulnerability metrics accurately. We have fine-tuned the BERT tokenizer on NVD’s descriptions and used TextCNN for better performance. This approach represents a significant advancement in the field of automated vulnerability assessment, offering substantial benefits for security professionals by streamlining vulnerability identification and prioritization. The carried out experimental evaluations in comparison to various deep learning models, including LSTM, CNN, and LSTM-Attention, as well as two state-of-the-art automated vulnerability assessment baselines indicate an accuracy score of over $\mathbf{9 7 \%}$ across all vulnerability metrics.

关键词： Measurement Deep learning Accuracy Bidirectional control Predictive models Transformers Software systems Encoding Telecommunications Security

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Risk-Aware Reinforcement Learning Based Federated Learning Framework for Io V

Risk-Aware Reinforcement Learning Based Federated Learning F...

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IEEE Conference on Wireless Communications and Networking

作者： Yuhan Chen Zhibo Liu Xiaozhen Lu Liang Xiao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Informatics and Communication Engineering Xiamen University Xiamen China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Federated learning helps protect data privacy for Internet of vehicles (Io V) by selecting a number of participated nodes but suffers from performance degradation such as low model training accuracy in the highly dynamic and large-scale Io V systems under selfish attacks. In this paper, we propose a risk-aware reinforcement learning based federated learning framework against selfish attacks for Io V,which jointly optimizes the training policy (i.e., the selection of participated vehicles and the corresponding local training data size) based on the state including the global model training accuracy, local model quality, training latency, data rate, and participation rate. By designing a punishment function to evaluate the immediate risk of each choosing training policy, this scheme avoids risky policies that result in extremely low training accuracy and high training latency to satisfy the requirements of local tasks such as the quality of service requirements. An evaluated neural network involved fully connected layers is designed to fast extract the global and local training features and thus accelerate the convergence speed. Experimental results based on both the MNIST and CIFAR-10 datasets verify that our scheme outperforms the benchmarks with higher training accuracy and less training latency.

关键词： Training Accuracy Federated learning Training data Reinforcement learning Benchmark testing Feature extraction

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Tree Data Structures and Efficient Indexing Techniques for Big Data Management: A Comprehensive Study 22

Tree Data Structures and Efficient Indexing Techniques for B...

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Proceedings of the 26th Pan-Hellenic Conference on informatics

作者： Dimitrios Samoladas Christos Karras Aristeidis Karras Leonidas Theodorakopoulos Spyros Sioutas Department of Computer Engineering and Informatics University of Patras Greece Department of Management Science and Technology University of Patras Greece

ISBN: (纸本)9781450398541

In the modern era where data is produced from multivariate sources, there is an urge to handle such data in an efficient yet effective manner. Therefore, applications that necessitate such capabilities shall make use of data structures and indexing mechanisms that can perform fast index operations along with low complexity as per insertion, deletion, and search. In this work, we survey B+ Tree, QuadTree, kD Tree, R Tree, and others along with efficient indexing techniques for big data management in order to provide a generic overview of the field to readers. Ultimately, we provide some indexing experiments as per insert operations and response times.

关键词： Data Structures Indexes Big Data Management QuadTree B+ Tree kD Tree

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Machine Learning Regression Approach for Estimating Energy Consumption of Appliances in Smart Home

Machine Learning Regression Approach for Estimating Energy C...

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2023 Al-Sadiq International Conference on Communication and Information technology, AICCIT 2023

作者： Husin, Nor Syafiqah I.M. Mostafa, Salama A. Jaber, Mustafa Musa Gunasekaran, Saraswathy Shamini Al-Shakarchi, Ali H. Abdulsattar, Nejood Faisal Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Al-turath University Department of Medical Instruments Engineering Techniques Baghdad Iraq Al-farahidi University Department of Medical Instruments Engineering Techniques Baghdad Iraq Universiti Tenaga Nasional College of Computing & Informatics Department of Computing Selangor Malaysia Ninevah University College of Information Technology Mosul Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

This paper attempts to use machine learning algorithms to estimate the energy consumption of appliances in a smart home environment. This work aims to promote awareness among smart home systems and users about their appliances' energy consumption and guide them toward energy-saving practices. To achieve this, three machine learning algorithms, namely Decision Forest (DF), Boosted Decision Tree (BDT), and Linear Regression (LR), were chosen for regression tasks to estimate the energy consumption of several appliances accurately. The time-series datasets, namely appliance energy prediction datasets, are used for training and testing the algorithms. The Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which comprises six processing phases, was employed in this work. The test is performed by utilizing 10-fold cross-validation. The results obtained assess the models' performance in predicting the appliances' energy consumption. The experimental results indicate that the three models exhibit varying degrees of accuracy in predicting energy consumption, as measured by their respective R-squared values. Among the three models, the random forest model exhibited superior performance by achieving the highest R2 values of 0.62 and 0.54 during the training and testing phases, respectively. © 2023 IEEE.

关键词： Energy utilization

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