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Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

computers, Materials & Continua 2023年第2期74卷 3263-3278页

作者： Sagheer Abbas Syed Ali Raza MAKhan Muhammad Adnan Khan Atta-ur-Rahman Kiran Sultan Amir Mosavi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science GC University LahorePakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Pattern Recognition and Machine Learning LabGachon UniversitySeongnam13120Korea Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah31261Saudi Arabia John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

File labeling techniques have a long history in analyzing the anthological trends in computational *** situation becomes worse in the case of files downloaded into systems from the ***,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user ***,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic ***,one major drawback of current topic modeling approaches is better *** rely on specific language types and domain similarity of the *** this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the *** results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

关键词： Automated file labeling file organization machine learning topic modeling

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Development of an Automated Service Level Agreement Negotiation Framework for SaaS Cloud E-Marketplace

Development of an Automated Service Level Agreement Negotiat...

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

作者： Uche Nnaji Ezenw Oke Azubuike Emmanuel Adetiba Damola Akinola Department of Computer & Information Sciences Covenant Applied Informatics and Communications Africa Centre of Excellence (CApIC-ACE) Covenant University Ota Nigeria Department of Computer & Information Sciences Covenant University Ota Nigeria Department of Electrical & Information Engineering Covenant Applied Informatics and Communications Africa Centre of Excellence (CApIC-ACE) Covenant University Ota Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

With the increasing number of Software-as-a-Service (SaaS) users, catering to their diverse needs while ensuring Quality of Service (QoS) has become imperative. Although SaaS providers regularly introduce new applications with varying offerings and QoS parameters, the current Cloud e-marketplace may overlook the individual QoS requirements of each user. To tackle this challenge, a Service Level Agreement (SLA) negotiation framework is employed for cloud service selection, ensuring SaaS user satisfaction, often facilitated by brokers. These brokers secure optimal offers from providers, taking into account the user's preferences. To initiate an agreement, brokers rank cloud providers, proceeding to negotiations only if the user rejects the top-rated provider's offer. Given the multitude of users and providers in today's cloud e-marketplace, selecting the most suitable provider and negotiating QoS parameters can be a complex task for brokers. This paper introduces a negotiation framework designed to enhance the customer satisfaction value for SaaS users through a service broker. Our proposed framework employs multi-agent systems (MAS) as the methodology, incorporating the level of competition, current negotiation time, and opportunities to formulate an improved negotiation model for SaaS users in the cloud marketplace. By adopting this proposed negotiation framework, SaaS users can acquire services from providers that precisely meet their requirements. Experimental results from simulations demonstrate that the proposed framework outperforms previous studies in terms of satisfaction level, response time, and negotiation success rate.

关键词： Software as a service Quality of service Time factors Task analysis Sustainable development Service level agreements Multi-agent systems

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Mammogram Mass Classification: A CNN-Based Technique applied to Different Age Groups 4th

Mammogram Mass Classification: A CNN-Based Technique Applied...

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4th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2021

作者： Obaidullah, Sk Md Mukherjee, Himadri Dhar, Ankita Goncalves, Teresa Santosh, Kc Roy, Kaushik Department of Computer Science and Engineering Aliah University Kolkata India Department of Computer Science West Bengal State University Kolkata India Department of Informatics University of Evora Evora Portugal 2AI: Applied AI Research Lab Department of Computer Science University of South Dakota VermillionSD United States

ISBN: (纸本)9783031070044

The World Health Organization (WHO) reports that breast cancer is one of the most frequent cancers among women, affecting almost 2.1 million women/year. As a consequence, fatality rate is high: 627,000 women died from breast cancer in 2018, which is approximately 15% [1] of all cancer deaths among women. Early detection (using AI-driven tools) can prevent from being worse. In this paper, we propose a Convolutional Neural Network (CNN) based approach for mammogram mass classification. The proposed method outperforms our existing feature-learning based model [2] on the same dataset. We experimentally achieved the best average recognition accuracy of 95.25% in separating malignant from benign masses. In our study on different age groups for mammogram mass classification, better results were observed from the age group: 61–75 years. © 2022, Springer Nature Switzerland AG.

关键词： X ray screens

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Groundbreaking taxonomy of metaverse characteristics

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Artificial Intelligence Review 2025年第8期58卷 1-50页

作者： Sadeghi-Niaraki, Abolghasem Rahimi, Fatema Azlan, Nur Alya Emanuelle Binti Song, Houbing Ali, Farman Choi, Soo-Mi Department of Computer Science and Engineering and Convergence Engineering for Intelligent Drone XR Research Center Sejong University Seoul Korea Republic of BaltimoreMD21250 United States Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

The Metaverse, a dynamic and immersive virtual realm, has captured the imagination of researchers and enthusiasts worldwide. This survey paper aims to introduce a groundbreaking taxonomy for the characteristics of the Metaverse offering a structured and adaptable framework that extends beyond existing categorizations by incorporating dynamic transformations. Unlike prior taxonomies, which often focus on fixed attributes, our approach emphasizes the dynamic evolution of Metaverse characteristics. Through an extensive review of published literature, this study explores key technological, social, economic, and ethical dimensions of the Metaverse. It introduces a process-oriented classification based on 23 distinct characteristics, including immersification, spatiotemporalification, interactification, persistentification, presentification, personification, unification, imaginification, economification, uncertaintification, and credification. By mapping these evolving aspects, we provide a structured and future-proof foundation for understanding the Metaverse’s continuous development. This survey establishes a new standard for comprehensiveness and innovation, shedding light on the diverse facets that have been explored in literature. Through this novel taxonomy, we provide a detailed map of the current landscape and offer insights that pave the way for future research and development in this burgeoning digital frontier. © The Author(s) 2025.

关键词： Comprehensive analysis Futuristic exploration Metaverse characteristics Paradigm-shifting taxonomy Revolutionary categorization

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Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Urban Safety Improvement

Comparative Analysis of YOLOv9 and YOLOv10 Algorithms for Ur...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Matko Glučina Nikola Žigulic Domagoj Frank Ivan Lorencin Dario Matika Department of Engineering Istrian University of Applied Sciences Pula Croatia Faculty of Engineering University of Rijeka Rijeka Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varazdin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

Individual safety in urban and city centers is one of the fundamental rights of every person. Today's innovations open up various possibilities for improving the quality of life and safety, but if they are not applied, they carry the danger of an additional increase in crime or greater insecurity in urban centers. This investigation aims to apply insights into the anticipation of illegal actions by various criminal groups that use illegal means, such as weapons, to get property that does not belong to them. In this research, computer vision and advanced strategies such as YOLOv10 and YOLOv9 are used to compare their properties and performance for improving security in urban and populated areas. The best results were achieved by YOLOv9m with a high percentage of mean average precision in the amount of 0.923, while the worst performance was shown by YOLOv10n with a mean average precision of 0.902. Precision and recall for the YOLOv9 and YOLOv10 models were also taken into account and the results are as follows: 0.984.. 0.993.. 0.979.. and 0.996.

关键词： Technological innovation computer vision Weapons Urban areas Artificial neural networks Safety Security Intelligent systems informatics

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TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow with Distributed Generators Using a Machine Learning-Based Approach

TSO-DSO Interaction: Privacy-Preserving Optimal Power Flow w...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Burak Dindar Can Berk Saner Hüseyin Kemal Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350318098

ISBN: (纸本)9798350318104

The integration of distributed generators (DGs) into the electrical grid has introduced new dimensions in power system management: Enhancing efficiency and operational flexibility of the grid requires taking into account the DGs located at the distribution level in dispatch decisions, while also considering the operational constraints of both distribution and transmission levels. However, the centralization of cooperation between transmission system operators (TSOs) and distribution system operators (DSOs) is troublesome due to privacy concerns of both parties. In present paper, we introduce a Machine Learning-Based Privacy-Preserving Optimal Power Flow (MLB-PP-OPF) framework. This method enables the seamless inclusion of DGs at the distribution level in optimal power flow calculations, all while adhering to the operational limits of both systems and maintaining data privacy. The proposed approach leverages three machine learning models to map the distribution system variables to active and reactive power flows at the point of common coupling, and to determine the feasible operating region of the distribution system in a privacy-preserving manner. Comparative case studies, performed on the coupled IEEE 30-bus transmission and 33-bus distribution test systems, demonstrate that the results achieved using MLB-PP-OPF align almost perfectly with those obtained through classical OPF, while maintaining data privacy.

关键词： Couplings Data privacy Reactive power Privacy Machine learning Generators Data models

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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Advanced Persistent Threat Identification with Boosting and Explainable AI

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SN computer Science 2023年第3期4卷 271页

作者： Hasan, Md. Mahadi Islam, Muhammad Usama Uddin, Jasim Department of Computer Science and Engineering Asian University of Bangladesh Ashulia Dhaka 1341 Bangladesh School of Computing and Informatics University of Louisiana at Lafayette 104 E University Ave Lafayette 70503 LA United States Department of Applied Computing and Engineering Cardiff School of Technologies Cardiff Metropolitan University Western Avenue Wales Cardiff CF5 2YB United Kingdom

Advanced persistent threat (APT) is a serious concern in cyber-security that has matured and grown over the years with the advent of technology. The main aim of this study is to establish an effective identification model for APT attacks to prevent and reduce their influence. Machine learning has the potential as well as substantial background to detect and predict cyber-security threats including APT. This study utilized several boosting-based machine learning methods to predict various types of APTs that are consistent in cyber-security domain. Furthermore, Explainable Artificial Intelligence (XAI) was coupled with the predictions to provide actionable insights to the domain stakeholders as well as practitioners in this domain. The results, particularly XGBoost with weighted F1 score of 0.97 and SHapley Additive exPlanations (SHAP)-based explanation, prove that boosting methods as well as machine learning models paired with XAI are indeed promising in handling cyber-security-related dataset problems which can be extrapolated towards new avenues of challenging research by effectively deploying boosting-based XAI models. © 2023, The Author(s).

关键词： APT Boosting Cyber-security Explainability Machine learning XAI

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Deploying YOLOV10 for Affordable Real-Time Handgun Detection

Deploying YOLOV10 for Affordable Real-Time Handgun Detection

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Nikola Žigulić Matko Glučina Domagoj Frank Ivan Lorencin Zoran Šverko Dario Matika Faculty of Engineering University of Rijeka Rijeka Croatia Department of Engineering Istrian University of Applied Sciences Pula Croatia Department of Computer Science and Informatics University North Koprivnica Croatia Faculty of Informatics Juraj Dobrila University of Pula Pula Croatia Department of Electrical engineering University North Varaždin Croatia

ISBN: (数字)9798350385601

ISBN: (纸本)9798350385618

The development of artificial intelligence is one of the most significant technological innovations that contributes to humanity with its characteristics and facilitates, secures, and improves everyday life. However the challenge arises when the programmer-engineer learns the algorithm of artificial intelligence to perform the proposed task, that is, the challenge lies in the issue of available hardware resources. Artificial intelligence algorithms perform many human-impossible tasks, such as detection and counting, i.e. calculating the interrelationships of individual objects, segmentation of tumors and other malignant diseases, i.e. tissues, classification of specific states of classes of a scene, and many other similar technologies. This research paper examines the possibility of implementing the You Only Look Once algorithm of the tenth generation on certain devices such as an affordable Raspberry Pi, and will discuss the advantages and disadvantages of changes in detection parameters, i.e. inferences to the applied model. In addition, a mock-up of the device will be shown, which will serve to provide timely information about criminals and suspicious persons who possess firearms in different situations, such as normal weather conditions in a populated place or in shops where petty robberies are frequent. The testing will be done using recorded videos in real-time scenarios. Finally, real-time inference or detection in real-time will be tested and the actions that Raspberry will perform will be simulated. The results indicate that the optimal model achieved a precision of 0.938, recall of 9.863, mAP50 of 0.91, and mAP50-95 of 0.739. This was achieved using an image size of 640, IOU threshold of 0.7, confidence threshold of 0.6, and training for 600 iterations with the Stochastic Gradient Descent optimizer, without augmentations, and employing the ONNX inference format.

关键词： YOLO Training Technological innovation Real-time systems Hardware Classification algorithms Delays Videos Tumors Testing

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