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检索条件"机构=Computing and Applied Technology"

共 4296 条记录，以下是1-10 订阅

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Digital Forensic Tools: Comparison of Autopsy TSK and Forensic Explorer

Digital Forensic Tools: Comparison of Autopsy TSK and Forens...

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Farnan, Mitchell Pratt, James David, Andrew Shakiba, Masoud School of Computing Electrical and Applied Technology Unitec Institute of Technology Auckland New Zealand

ISBN: (纸本)9798350382693

This research aims to compare the effectiveness of two prominent digital forensics tools, Autopsy TSK and Forensic Explorer, in analysing a well-known forensic image, specifically the Lone Wolf image from Digital Corpora. The aim is to assess their capabilities in detecting and handling forensic artefacts and their responses to selected anti-forensic techniques. Autopsy TSK and Forensic Explorer exhibited robust performance in identifying and analysing forensic artefacts within the Lone Wolf image. They successfully retrieved most of the available digital evidence. Data was concealed using encryption techniques, both tools failed to detect the hidden information, highlighting a limitation in their ability to uncover encrypted steganographic content. In cases of altered Exif and metadata, both tools could retrieve the original information from the hex data of the files. However, it is noteworthy that neither tool provided an alert or warning to guide the investigator regarding potential data manipulation. Based on the findings of this research, it is advisable for digital forensic investigators to exercise caution and employ additional techniques or tools when handling cases which may involve encrypted steganography. © 2024 IEEE.

关键词： Steganography

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computing the TCCD-Number for the Strong Product of Graphs and Its Application in Urban Water Treatment: Addressing Water Scarcity Through Drainage Purification 1

Computing the TCCD-Number for the Strong Product of Graphs a...

引用

1st International Conference for Women in computing, InCoWoCo 2024

作者： Kaviya, S. Mahadevan, G. Sivagnanam, C. The Gandhigram Rural Institute Deemed University Gandhigram Tamilnadu India Mathematics and Computing Skills Unit University of Technology and Applied Sciences Sur Oman

ISBN: (纸本)9798331518943

A dominating set $S$ of a graph $G$ is termed as a triple connected certified dominating set (TCCD-set) if, for every vertex $v\in S$, the condition $\vert N(v)\cap(V-S)\vert \neq 1$ holds, and the subgraph $\langle S \rangle$ is triple connected. The minimum size of a TCCD-set is known as the triple connected certified domination number (TCCD-number), denoted by $\gamma_{TCC}(G)$. This study investigates the behavior of the strong product of graphs with respect to the TCCD-number. The TCCD-set concept offers valuable insights into graph connectivity and its applications in enhancing network reliability. In addition to graph theory, this study examines practical applications, such as the establishment of advanced water treatment plants in strategically chosen locations within cities. These plants enable companies to efficiently purify water while utilizing sufficient resources. This model facilitates a reduction in the installation and maintenance costs of drainage water systems, with a primary focus on transforming drainage water into purified water. © 2024 IEEE.

关键词： Drainage

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Classification Framework for Autoimmune Liver Disease using Machine Learning and Deep Learning Techniques 5

Classification Framework for Autoimmune Liver Disease using ...

引用

5th technology Innovation Management and Engineering Science International Conference, TIMES-iCON 2024

作者： George, E. Ben Rosline, G. Jeba Varghese, Abraham Rajesh, Gnana D. Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798350362602

—Autoimmune diseases affect people of all ages, genders, and ethnic backgrounds, making it one of the most widespread health issues we face today. These disorders occur when the immune system mistakenly targets healthy cells within the body and causing a wide range of potentially life-threatening diseases. There are over 80 recognized autoimmune disorders, including type-1 diabetes, rheumatoid arthritis, Graves' disease, alopecia, celiac disease, and autoimmune liver disease(AiLD). Autoimmune liver disease is one such chronic disorder characterized by liver inflammation and damage. If not detected on time and untreated within the given time, it might lead to liver cirrhosis or liver cancer. Identifying and treating autoimmune liver disease is a complex and time consuming process which involves symptom analysis, laboratory tests, and clinical assessments. Detecting the disease can be more challenging because many symptoms of autoimmune liver diseases overlap with those of other autoimmune conditions. As a result, there is a need for an optimized, accurate, efficient, and cost-effective methodology to identify the disease without extensive and invasive procedures such as organ transplants. This paper introduces an innovative approach that employs the power of supervised machine learning and deep learning techniques to predict the onset of autoimmune liver diseases in advance. The machine learning model is constructed using a comprehensive dataset comprising the complete medical history of patients, physical examination records, histopathology and genetic data. Along with the machine learning model, the deep learning model is also developed for liver MRI images of various patients to make accurate prediction. This system provides a second opinion for medical professionals that may avoid the need for extensive and invasive medical procedures, including biopsies, and ultimately improve patient care and outcomes. ©2024 IEEE.

关键词： Autoimmune Liver Disease Classification Deep Learning Machine Learning

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A Dyad-Based Mobile System to Support Older Adults and Informal Caregivers 14

A Dyad-Based Mobile System to Support Older Adults and Infor...

引用

2024 IEEE 14th Annual computing and Communication Workshop and Conference, CCWC 2024

作者： El Salti, Tarek Sykes, Edward R. Scrivo, Jordan Plaza, Brandon Mun, Vladislav Faculty of Applied Science and Technology Sheridan College School of Applied Computing OakvilleON Canada OakvilleON Canada

ISBN: (纸本)9798350360134

Despite a gradual rise in health care expenditures due to population aging between the Year 2020 and 2060 in several countries, the overall increase will contribute to additional GDP share (e.g., 1.3 percentage points in the EU). To potentially reduce costs, ongoing developments of mobile Health (mHealth) applications is essential which provide digital health care using mobile devices. However, there is no dyadic mHealth architecture (i.e., between caregivers and their older adults) exclusively utilizing smartphones, aimed at supporting older adults' well-being through regular follow-ups for their caregivers. In response, we developed a novel dyad-based architecture, including mobile applications for caregivers to check on their older adults' physical activities (e.g., walking). Our system is not only considered mobile but also highly accessible due to the pervasiveness of smartphones. This work in progress also included pilot usability study analysis for the user interface and experience design. Among the findings, participants mentioned that the proposed system strongly supports them in their caregiving for their older adults. This study will serve as a precursor for future usability studies that will include wearables. © 2024 IEEE.

关键词： Smartphones

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Avian Hazard Mitigation: A Classification Approach to Bird Strike Analysis 5

Avian Hazard Mitigation: A Classification Approach to Bird S...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Al-Zadjali, Najiba Said Hamed Savarimuthu, Charles Sudaravadivazhagan, B. Rances, Emanuel O. College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Oman University of Technology and Applied Sciences-Al Musanah College of Computing and Information Sciences Al-Musannah Oman College of Engineering and Technology University of Technology and Applied Sciences-Al Musanah Al-Musannah Oman

ISBN: (纸本)9798350356236

Bird strikes during landing or takeoff can result in fatalities and catastrophic accidents, raising concerns about loss of life for the aviation sector. This study explored the potential of employing machine-learning algorithms to mitigate bird strikes in aviation. Bird strike data were collected, and the Boruta algorithm feature-selection technique was applied to identify relevant features for an experiment. Irrelevant features were omitted, and relevant features related to bird strikes were identified for analysis. The multinomial Naive Bayes algorithm is then used to segregate objects based on their features. The experimental results illustrate the power of using machine learning techniques to segregate and analyze bird strike data, thereby aiding in determining the causes and effects of damage. © 2025 IEEE.

关键词： Aviation

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Predicting Student's Final Examination Grades in Statistics Courses Using a Regression Model Based on Internal Assessment Scores 2

Predicting Student's Final Examination Grades in Statistics ...

引用

2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Sreedhar, Jagath Prasad Nair, Ranjini S. Varghese, Abraham College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (纸本)9798331542573

Learning analytics is the process of collecting data on learners and their environment, analyzing it, and using those insights to improve learning experiences and outcomes. Analysis will help teachers identify students who might need extra help early on. Also, courses can be tailored to individual learning styles and the entire learning environment can be fine-tuned for better results. A multiple linear regression model is presented here in this work to predict students' Final Exam grades from internal assessment scores. In this case, the response variable is the student's score in the Final Exam, while the assessment components, such as Quiz, Midterm, and SCL, are predictor variables. The data were collected from a group of students who studied Probability Statistics course at the advanced diploma level program. A regression model was created using R software in this study, accompanied by graphical representations and tables to demonstrate the model. © 2024 IEEE.

关键词： Contrastive Learning

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Virtual and Augmented Reality in Interactive Entertainment: Unlocking New Dimensions of Immersive Interaction 2

Virtual and Augmented Reality in Interactive Entertainment: ...

引用

2nd International Conference on computing and Data Analytics, ICCDA 2024

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

ISBN: (纸本)9798331542573

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. © 2024 IEEE.

关键词： Virtual environments

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Empirical Analysis of Machine Learning Methods for Predicting Student Performance 4

Empirical Analysis of Machine Learning Methods for Predictin...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

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

ISBN: (纸本)9798350352931

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. © 2024 IEEE.

关键词： Prediction models

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Exploring Drug Recommendations Through Sentiment Analysis: A Machine Learning Perspective

Exploring Drug Recommendations Through Sentiment Analysis: A...

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2024 International Conference on Electrical and Computer Engineering Researches, ICECER 2024

作者： Qureshi, Imran Hashil Alsiyabi, Zahra Nasser Mohammad, Burhanuddin Shadab, S.G.M. Irfan, Mohammed University of Technology and Applied Science College of Computing and Information Sciences AlMusanna Oman

ISBN: (纸本)9798331539733

Current drug recommendation systems often rely on clinical trials and general guidelines, which may not fully account for individual patient experiences. This study addresses the gap by leveraging sentiment analysis of patient reviews to enhance drug recommendations. The primary objective is to develop a machine learning-based drug recommendation system that incorporates sentiment from drug reviews, improving the personalization of drug recommendations. Using a dataset from UCI ML repository with over 215,000 instances, we apply sentiment analysis and various feature extraction techniques, including TF-IDF and Word2Vec, alongside machine learning classifiers to predict drug efficacy for conditions such as acne, hypertension, and depression. By integrating sentiment-based insights, this approach aims to provide more tailored recommendations for individual patients. Our results show that models like Linear SVC (TF-IDF) and Random Forest (manual features) outperform traditional methods, achieving up to 93% accuracy. These findings demonstrate the potential of sentiment analysis to enhance the relevance and accuracy of drug recommendations. This research contributes to the growing field of personalized healthcare by introducing a data-driven framework that captures patient sentiment, offering more accurate and individualized medication guidance. © 2024 IEEE.

关键词： BoW Classifier Drug Recommender ML

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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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2024 International Conference on Circuit, Systems and Communication, ICCSC 2024

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

ISBN: (纸本)9798350365306

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. © 2024 IEEE.

关键词： AI-driven innovation Artificial Intelligence(AI) sustainable development universities

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