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Neural computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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The effect of deep learning methods on deepfake audio detection for digital investigation

The effect of deep learning methods on deepfake audio detect...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： McUba, Mvelo Singh, Avinash Ikuesan, Richard Adeyemi Venter, Hein Department of Computer Science University of Pretoria Pretoria South Africa Computing and Applied Technology College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices. © 2023 The Authors. Published by Elsevier B.V.

关键词： Cloning

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University intelligent voice assistance: AI based digital academic assistance

University intelligent voice assistance: AI based digital ac...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Faizal Hajamohideen Nouf Mohammed Al Balushi Manar Ahmed Al Mamari Rihab Zaid Al Mamari College of Computing Information Sciences University of Technology and Applied Sciences (UTAS) Suhar Oman

ISBN: (数字)9781837243105

Using cutting-edge artificial intelligence, the university student voice assistant was created to adapt to the changing demands of contemporary learning. It provides seamless integration and accessibility via mobile devices by utilizing an extensive application kit. Instant access to customized information, such as email address, course details, and university identity, is provided upon login. Students are able to make well-informed judgments by using the interface's user-friendly iconography for things like topic selection, scheduling, and academic preparation. The method creates academic averages from the beginning of the semester and decides topic approval by examining the study schedule. In addition, it offers easy access to past exams and assignments, as well as customized study guidance and resource recommendations. Furthermore, the voice assistant uses artificial intelligence (AI) to identify each student's unique voice, facilitating smooth verbal communication and rapid information retrieval. This feature improves the learning process by enabling students to ask questions, get prompt responses, and access material effectively. The voice assistant uses artificial intelligence (AI) to adjust to the particular requirements of every student, which eventually promotes increased productivity and academic performance.

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Posture Monitoring and Classification for Health Care of Bedridden Patients Using IoT and AI Algorithms

Posture Monitoring and Classification for Health Care of Bed...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Sunitha Cheriyan Sudha Sakthivel Thirupathi Regula Kiran Kumar S. G. Said Al Riyami Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Posture monitoring has become a crucial area of research and application, particularly considering the increasing concerns about musculoskeletal disorders and their association with prolonged lying and poor ergonomic practices. To solve the crucial problem of bedridden patient care, this study investigates the categorization of lying postures using four different deep learning models: The models used are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), VGG16, and ResNe $t$ 50. Due to bedridden patients' difficulty in developing pressure ulcers, respiratory disorders, and other complications, the classification of laying positions which includes supine, prone, and lateral is essential. Some possible assistive devices that can inform caregivers or initiate some techniques to improve the patient's comfort and eliminate adverse effects of immobility can benefit from the identification of some postures in real-time. The models were trained using both balanced and unbalanced data sets from a set of labeled images covering different laying postures. Their performance was then determined by criteria including accuracy, RMSE, Kappa, recall, and precision. As for the performance, the proposed VGG model achieved better classification accuracy compared to the CNN, RNN, and ResNet models. In light of enhancing patient care as well as monitoring within the health care facilities, it is suggested from our result that these models could suggest potential solutions to posture identification for bedridden patients. By illustrating how AI posture classification systems can be implemented in IoT-based applications, the study also shows how future healthcare delivery can be improved and the problems caused by long periods of inactivity can be prevented.

关键词： Recurrent neural networks Accuracy Ergonomics Prevention and mitigation Refining Medical services Transforms Real-time systems Convolutional neural networks Monitoring

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Leveraging Digital Twins and AI for Enhanced Clinical Decision Support in Endometrial Cancer Treatment 5

Leveraging Digital Twins and AI for Enhanced Clinical Decisi...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Cibi, A. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Application Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Virtusa Consulting Service Private Limited Data & Analytics Visualizaation - Delivery Chennai India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

Digital twins are a relatively new concept that have lately garnered interest in the industrial sector. Digital twins are virtual models that mirror actual products. In this research, we suggest using the technology of digital twins to the field of healthcare in order to enhance clinical decision support and to provide more individualized treatment for patients. When artificial intelligence (AI) is combined with digital twins, healthcare professionals are able to more effectively analyze large volumes of varied data and increase their ability to make diagnostic and therapeutic decisions. In this research, we provide a conceptual framework for leveraging digital twins and AI to solve existing limits in cancer care, notably endometrial cancer therapy. This research study mainly focuses on cancer care, and more specifically on endometrial cancer treatment. In addition, we analyze the possible challenges and opportunities that may arise throughout the process of integrating this technology in healthcare settings. Our overarching objective is to improve the standard of treatment as well as the clinical results for people who have cancer. © 2024 IEEE.

关键词： Lung cancer

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Tourism Enhancement Through Hybrid Mobile Application with Web Scraping and Recommendation System Using KNN and MF

Tourism Enhancement Through Hybrid Mobile Application with W...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Thirupathi Regula Anshar Ali Dhanasekar N Department of IT College of Computing and Information Sciences University of Technology and Applied Science Muscat Oman

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Mobile applications have become an essential part of daily life. The tourists develop their tourist attractions and other facilities, which were being struggled after making plans within constraints. However, it is still incomplete and calls for some fine-tuning mechanisms with a view to creating the ideal tourist itinerary. This research has proposed a hybrid mobile app that integrates web scraping with machine learning technology to enhance the trip experience of visitors. The software does web scraping to scrape data in real time from several sites for bookings of hotels, flights, cabs, and food services with one interface. In solving the challenge of personalized tour planning, this app makes use of powerful machine learning techniques: K-Nearest Neighbors and matrix factorization-based recommendation algorithms. These algorithms consider user preferences for budget, length of travel, ratings, and other constraints while making personalized recommendations for tourism sites and activities. Software is constantly reacting to user feedback and behavior to enhance the relevance of recommendations over time. Unique in its method, it enables trip planning not only by unifying services but also makes sure travelers make informed and cost-effective decisions. It represents a huge step forward in intelligent, user-centric travel solutions by embedding the powers of web scraping for extensive data gathering and machine learning for personalization.

关键词： Machine learning algorithms Software algorithms Machine learning Nearest neighbor methods Software Real-time systems Mobile handsets Mobile applications Planning Recommender systems

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Empirical Analysis of Machine Learning Methods for Predicting Student Performance

Empirical Analysis of Machine Learning Methods for Predictin...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Prashanth Kumar Bolisetty Satyanarayana Bora Department of Information Technology College of Computing and Information Sciences University of Technology And Applied Sciences Shinas Sultanate of Oman Preparatory Studies Center University of Technology and Applied Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Performance prediction for students is relatively a new topic of research that aims to apply quantitative and qualitative approaches to evaluate student performance. This paper reviews existing approaches in predicting student performance across various learning environments. It explores fundamental statistical models, Machine Learning (ML) algorithms, and Deep Learning (DL) models, highlighting their pertinent characteristics and suitability for analyzing academic, behavioral and demographic data. The paper focuses on analyzing the existing methodologies for student performance prediction. The datasets used consist of student grades, attendance records, socioeconomic status, and interaction rates, along with an assessment of their efficiency in feature selection and data preprocessing. Lastly, the explainability and interpretability of the proposed predictive models are explored to enable reliability for educational stakeholders on these predictions for timely interventions. This survey concludes with a discussion of future scopes, focusing on enhancing prediction accuracy, ensuring fairness in models, and integrating multiple data modalities into performance analysis.

关键词： Wireless communication Surveys Analytical models Reviews Predictive models Prediction algorithms Data models Stakeholders Reliability Socioeconomics

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The Role of Universities in AI-Driven Innovation for Sustainable Development

The Role of Universities in AI-Driven Innovation for Sustain...

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Circuit, Systems and Communication (ICCSC), International Conference on

作者： Nasim Salim Ziyad Al Balushi College of Computing and Information Sciences Unversity of Technology and Applied Science Ibri Sultanate of Oman

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

The development of sustainability of any country depends not only on a strong government. Academic institutions also have critical role in this. A theoretical analysis approach to investigate the potential role of universities in sustainability especially in AI-driven innovation field is used. It is found that University of technology and applied Science, Sultanate of Oman has some noticeable points in this field. The objective of this reserach paper is to indicate the role of academic institutions in sustainable development especially in the AI-driven innovation field.

关键词： Technological innovation Government Education Teamwork Sustainable development Artificial intelligence Investment

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Virtual and Augmented Reality in Interactive Entertainment: Unlocking New Dimensions of Immersive Interaction

Virtual and Augmented Reality in Interactive Entertainment: ...

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computing and Data Analytics (ICCDA), International Conference on

作者： Jasim Sharki Ghulam Al Balushi Malak Ibrahim Ahmed Al Jabri Suresh Palarimath Chithik Raja Mohamed Venkateswaran Radhakrishnan Muni Balaji Thumu College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

Virtual Reality (VR) offers an immersive experience with position tracking and 3D near-eye displays, enabling users to enter virtual surroundings. Conversely, Augmented Reality (AR) superimposes computer-generated content onto the real world, integrating digital and physical environments through visual, aural, tactile, and other sensory modalities. Both technologies are transforming user engagement with content, providing the opportunity to explore remote locations, communicate with digital characters, or assume leading roles in interactive entertainment events. While VR completely immerses users in simulated surroundings, AR enriches real-world contexts by incorporating interactive elements and contextual data. This study examines the transformative effects of VR and AR in interactive entertainment, emphasizing its various uses, benefits, and constraints. It emphasizes how new technologies have transformed narrative, facilitating enhanced user involvement and a more dynamic feeling of presence. The review analyzes the present and prospective capabilities of VR and AR in transforming social interactions, narrative-centric experiences, and cooperative gameplay. In addition to entertainment, the article examines the ramifications of AR and VR in education, training, and healthcare, where immersive environments demonstrate significant efficacy. The report highlights the crucial impact these technologies have in enhancing human-computer interaction across various industries.

关键词： Headphones Video games Visualization Three-dimensional displays Entertainment industry Medical services Immersive experience Transforms Software Hardware

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Enhancing Tourism Recommendation System Using KNN-Based Hybrid Mobile Application

Enhancing Tourism Recommendation System Using KNN-Based Hybr...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Thirupathi Regula Anshar Ali Dhanasekar N Department of IT College of Computing and Information Sciences University of Technology and Applied Science Muscat Oman

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Tourism development in today's digital era much counts on technology in order to achieve convenience and easiness of travel. The design of a hybrid mobile application recommendation system using K-Nearest Neighbors algorithm, the KNN, improves the development of the tourists. This application is designed to monitor information on locations of tourists, hotel room reservations, booking a flight, booking of a taxi, and interests before use. It allows for real-time personal behavioral choices based on budget, time, and location information for the Recommendation system. KNN algorithm in identifying similar users and locations more properly and therefore leads to more appropriate and correct choices. A hybrid mobile application will provide the advantages of both a native and an online application, ensuring consistency and responsiveness irrespective of devices and operating systems. The mobile application is supposed to run on both the Android and iOS systems. Tests and evaluation results reveal that the system is effective in enhancing user engagement with destinations, satisfaction, and intentionality of revisiting, thereby underpinning its contribution to the sustainable development of tourism. This work contributes to the creation of a scalable user-centered strategy driven by insights and machine learning. This paper achieved the accuracy of 82.1 % on Kaggle dataset of restaurants available in Amsterdam. This approach encourages tourism, that is one of the main pillars of economy development of many countries.

关键词： Reviews Operating systems Scalability Random access memory Nearest neighbor methods Real-time systems Mobile applications Sustainable development Recommender systems Public transportation

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