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International Conference on Emerging Technologies, ICET

作者： Omar Kashif Majeed Zain ul Abideen Usama Arshad Raja Hashim Ali Abdullah Habib Rafay Mustafa Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty.

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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International Conference on Emerging Technologies, ICET

作者： Muhammad Haseeb Ishaq Rafay Mustafa Usama Arshad Zain ul Abideen Raja Hashim Ali Abdullah Habib Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies.

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Fused Fuzzy Deep Learning and Information Steganography for Privacy Preservation in Medical Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Chen, Jingxue Zhao, Pengbiao Deng, Erqiang Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Xiong, Hu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China the School of Computer Science Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315104 China the KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India the Division of Research and Development Lovely Professional University Phagwara India the University Centre of Research and Development Chandigarh University Mohali India the Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia the Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia

—Medical Consumer Electronics (MCE) have greatly facilitated people’s lives, allowing patients to obtain personal medical information including medical images. These medical images obtained from MCE need to be processed using segmentation, diagnosis, recognition to obtain useful information. Deep learning plays an important role in medical image segmentation and recognition. However, other personal information about the patient, such as previous cases, is often required to determine the final diagnosis. However, sending personal information and medical images generated through MCE to the clinic directly exposes patient privacy. Therefore, in this paper, we propose a digital medical image privacy protection framework based on information steganography with fuzzy deep learning. The proposed framework transforms patient privacy information into invisible noise and hides it in the deep covert space of the image. This method plays a crucial role in ensuring patient privacy and data security. Furthermore, this method can ensure a high level of image segmentation accuracy that supports the development of personalized medical services. The experimental results show that the proposed framework can embed patient information in medical images without affecting the segmentation task, and effectively protect privacy. Finally, the proposed framework provides new possibilities and impetus for the popularization of MCE. © 2025 IEEE.

关键词： Medical imaging

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

arXiv

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arXiv 2025年

作者： Goyal, Harshika Wajid, Mohammad Saif Wajid, Mohd Anas Khanday, Akib Mohi Ud Din Neshat, Mehdi Gandomi, Amir Indian Institute of Technology Kharagpur India Department of Computer Science and Intelligent Systems Tecnológico de Monterrey Monterrey Mexico Institute for the Future of Education TEC de Monterrey Monterrey Mexico Department of Computer Science and Software Engineering United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates Department of Computer Science Samarkand International University of Technology Samarkand Uzbekistan Faculty of Engineering and Information Technology University of Technology Sydney Ultimo SydneyNSW2007 Australia Obuda University Budapest1034 Hungary School of Computer and Mathematical Sciences University of Adelaide Adelaide5005 Australia

The rapid expansion and advancement of deepfake technology, which is specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across a multitude of fields, including but not limited to the realms of the entertainment industry, forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methodologies, especially through the implementation of techniques such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformer Neural Networks, the outcomes achieved in the generation of deepfakes have been nothing short of astounding and transformative. Furthermore, the recent advancements in diffusion models have served to invigorate and catalyze ongoing research efforts in this fascinating domain. At the same time, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in both deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations that are shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Besides, task definitions are standardized to ensure clarity, while datasets and metrics are diligently introduced, paving the way for a

关键词： Generative adversarial networks

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Part-based pyramid loss for person re-identification

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International Journal of Information and Communication technology 2019年第4期15卷 344-356页

作者： Wang, Yuanyuan Wang, Zhijian Jiang, Mingxin College of Computer and Software Engineering Huaiyin Institute of Technology Huaian China College of Computer and Information Hohai University Nanjing China College of Electronic Information Engineering Huaiyin Institute of Technology Huaian China

Person re-identification (ReID) is a challenging problem in computer vision, meanwhile attracted the attention of industry. Person ReID focuses on identifying person among multiple different cameras. A key under-addressed problem is to learn a good metric for measuring the similarity among images. Recently, deep learning networks with metric learning loss has become a common framework for person ReID, such as triplet loss and its variants. However, the previous method mainly uses the distance to measure the similarity and the distance measure is more sensitive when the scale changes. In this paper, we propose part-based pyramid loss to learn better similarity metric for the person ReID, in which batches of quadruplet samples as the input. Specifically, we simultaneously use the relationship of distance and angle among samples learn the local body-parts features of person images. Our approach uses the pyramid relationship in triangles as a measure of similarity, minimising the angle at the negative point of the triangle. Pyramid loss can learn better similarity metric and achieve a higher performance on the person ReID benchmark datasets. The experimental results show that, our method yields competitive accuracy with the state-of-the-art methods. © 2019 Inderscience Enterprises Ltd.

关键词： Benchmarking

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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International Conference on Emerging Technologies, ICET

作者： Tafreed Ahmed Ahmad Maaz Danyaal Mahmood Zain ul Abideen Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.

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Study on Potability Water Quality Classification Based on Integrated Learning

Study on Potability Water Quality Classification Based on In...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： FenFen Li Lei Zhou Ting Chen Faculty of Computer and Software Engineering Research Center for logic and Intelligent Computation National-Local Joint Engineering Lab of System Credibility Automatic Verification Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781665405546

Aiming at the problem of low accuracy of simple classification model, based on the drinking water data set of kaggle official website, integrated learning models is proposed, which improves the precision and accuracy of binary classification of drinking water quality. K-fold cross validation was performed to measure model consistency. The effect of set classifier is higher than that of simple classifier.

关键词： Training Knowledge engineering Machine learning algorithms Fitting Water quality Machine learning Predictive models

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Games for Scrum Team Collaboration in the Global software Development Environment: A Literature Review

Games for Scrum Team Collaboration in the Global Software De...

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International Conference on Informatics and Computing (ICIC)

作者： Anita Hidayati Iklima Ermis Ismail Ade Rahma Yuly Henry Edison Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia Faculty of Computer Science Universitas Dian Nuswantoro Semarang Indonesia Department of Software Engineering Blekinge Institute of Technology Sweden Swedia

ISBN: (纸本)9798350345728

software development companies commonly use Global software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies.

关键词： Industries Systematics Bibliographies Games software Teamwork Object recognition

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ML Techniques for Employee Performance Prediction

ML Techniques for Employee Performance Prediction

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Brajesh Kumar Prateek Agrawal Dipesh Uike Melanie Lourens Indira Bhati M. Chandrakumar Peter Department of Computer Science & Engineering Dr. APJ Abdul Kalam Women's Institute of Technology Darbhanga Bihar Gurukula Kangri (Deemed to be University) Dr. Ambedkar Institute of management studies and research Nagpur Maharashtra Faculty of Management Sciences Durban University of Technology South Africa Department of Management MMIM Maharishi Markandeshwar (Deemed to be University) Department of Software Engineering PeriyarManiammai Institute of Science and Technology Thanjavur Tamilnadu

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

When it comes to maximizing the effectiveness of a business and promoting professional growth, employee performance prediction is an extremely important factor. This research article investigates the use of machine learning (ML) approaches to forecast employee performance, with a particular emphasis on the incorporation of different algorithms from different sources to improve accuracy and dependability. Methods that have been used for a long time, such as linear regression and decision trees, are evaluated alongside more recent approaches, such as ensemble methods and deep learning. To refine the prediction models, our research makes use of approaches such as feature selection and dimensionality reduction. These techniques are utilized by utilizing previous performance data, demographic information, and behavioral indicators. It has been demonstrated through the analysis that sophisticated machine learning techniques, in particular ensemble and deep learning models, are superior to traditional methods when it comes to anticipating employee performance. Our findings offer HR departments concrete insights that can be used to adopt data-driven methods for performance management, which will eventually contribute to improved organizational outcomes and increased employee satisfaction. This paper offers opportunities for future development in predictive analytics within the field of human resources and emphasizes the potential of machine learning to alter performance evaluation systems.

关键词： Deep learning Performance evaluation Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Feature extraction Ensemble learning Predictive analytics

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