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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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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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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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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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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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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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Implementation of Deep Learning Using Convolutional Neural Network for Skin Disease Classification with DenseNet-201 Architecture

Implementation of Deep Learning Using Convolutional Neural N...

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International Conference on informatics and Computing (ICIC)

作者： Evita Pramesti Achmad Benny Mutiara Rina Refianti Informatics Engineering Gunadarma University Depok Indonesia Computer Science and Information Technology Gunadarma University Depok Indonesia

The skin is the outermost organ of the human body, playing a crucial role in maintaining internal balance and protecting vital organs from factors that could disrupt health. As the outermost part of the body, the skin is also vulnerable to various environmental exposures, such as sunlight radiation, air pollution, viruses, bacteria, and parasitic agents that cause skin diseases. Skin diseases can vary from mild conditions like rashes to more serious skin issues such as skin cancer. This diversity often poses challenges for medical professionals in accurately identifying and classifying skin diseases. However, the process of diagnosing skin diseases is still predominantly carried out manually, resulting in suboptimal accuracy and effectiveness. Thus, alternative methods are needed to address these limitations. Notably, Convolutional Neural Network (CNN) techniques have led to remarkable advancements in computers’ ability to recognize patterns in images, including medical images like skin images. This research aims to build a CNN model and compare the performance of DenseNet-201 with other CNN architectures. The dataset consists of 1,789 dermoscopy images of skin diseases. The training process involves a series of preprocessing techniques and the use of optimization algorithms. Evaluation results utilizing a confusion matrix demonstrate that the DenseNet-201 architecture achieves an accuracy of 99%. The model with the best accuracy is implemented into a web application using the Flask framework. This application allows users to upload images of skin diseases and promptly receive accurate prediction results.

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Feasible Regions Identification based on Historical Solutions for Constrained Optimization Problems

Feasible Regions Identification based on Historical Solution...

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Congress on Evolutionary Computation

作者： Mengli Shan Changhe Li Mai Peng Xiaobo Liu Michalis Mavrovouniotis Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Artificial Intelligence Anhui University of Science & Technology Hefei China State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science & Technology Huainan China ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus School of Computer Science and Informatics De Montfort University Leicester United Kingdom

ISBN: (数字)9798331534318

ISBN: (纸本)9798331534325

The presence of constraints often leads to the formation of narrow and fragmented feasible regions within the search region, presenting significant challenges for optimization problem-solving. This paper introduces a novel approach, Feasible Regions Identification based on Historical Solutions (FRIHS), designed to address these challenges. FRIHS leverages previously evaluated solutions to partition the search region into ε-feasible and ε-infeasible regions. Additionally, by analyzing the correlations among constraints, they are reformulated as auxiliary objectives, effectively transforming the constrained optimization problem into a constrained multi-objective optimization problem. The method employs the classical evolutionary algorithm Differential Evolution and the multi-objective method NSGA-III to search the most promising feasible regions. The effectiveness of FRIHS is evaluated through a comparative analysis with five advanced constraint-handling algorithms across a benchmark test suite. Experimental results indicate that the proposed approach demonstrates competitive performance on the test problems.

关键词： Correlation Evolutionary computation Benchmark testing Search problems Rendering (computer graphics) Problem-solving Optimization Faces

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Development of novel Urdu language part of speech tagging system for improved Urdu text classification

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Social Network Analysis and Mining 2025年第1期15卷 1-26页

作者： Ishaq, Muhammad Asim, Muhammad Ahadullah Cengiz, Korhan Konecki, Mario Ivković, Nikola Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan Department of Electrical Engineering Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Faculty of Organization and Informatics University of Zagreb Varaždin Croatia

Humans immediately understand any long text by processing or searching the Part-of-Speech (POS). Optimal AI classifier and a unique POS tagging approach guarantee the success of natural language processing. Improved Urdu text classification depends upon the choice of algorithm and an optimal tagging system. Support Vector Machine (SVM) and its latest hyper-tuned variants are effective classifiers because the decision function or support vectors have training points. For more improved results and novelty different variants and hyperparameters will be used. SVM variants, Random Forest, and an ensemble model are tested on the same corpus dataset with and without the proposed unique POS tagger. The POS tagging is a kind of feature engineering for better results. The corpus dataset is divided into four categories: sports, entertainment, science and technology, and business and economics. The results are dramatically improved by using customized POS taggers. The average accuracy, precision, recall, and F1-score of the SVM variants using POS tagging are above 92%. Random Forest classifiers give over 95% results for all matrices. It gives an average of above 98% results for all four matrices. POS tagging significantly improves the performance of models. There is a special focus on improved POS based text categorization for Quick and lightweight models deployment. Recent emerging discriminative and generative AI algorithms with improved architecture may better utilize POS taggers for more complex natural language text classification. Rule-Based POS Taggers Uses handcrafted linguistic rules and Machine learning based taggers Learns patterns from annotated corpora.

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