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Advancements in Computing (ICAC), International Conference on

作者： P.S.D. Fernando H.P.D.E.R. Amarasekara M.D.B. Rathnakumara B.W. Amarakoon Samantha Rajapaksha Jenny Krishara Dept. of Information Technology Malabe Sri Lanka Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Proficiency in mathematics is essential for both academic and professional success, yet many students find the subject difficult, especially when preparing for important exams like Sri Lanka's General Certificate of Education (G.C.E.) Ordinary Level examination. In Sri Lanka, the students show poor pass rates in Ordinary Level mathematics examination. In order to improve mathematics education for O/L students, this research suggests an innovative mobile application that makes use of adaptive learning strategies. This application features components like a comprehensive knowledge bank that is curriculum-mapped, pre-assessment tools for identifying knowledge shortages, a weaknesses based lesson recommendation for personalized lesson suggestions, a custom developed chat assistant to interact with the knowledge bank, interactive 3D visualizations and justifications of complex concepts, and platforms for problem-solving, writing, and time management skill development. The suggested adaptive learning strategy makes use of multimedia tools and datadriven insights to tailor instruction to each student's unique needs, improving math comprehension and readiness for the O/L examination in Sri Lanka.

关键词： Adaptive learning Visualization Three-dimensional displays Writing Mathematics Natural language processing Mobile applications Problem-solving Stakeholders Research and development

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Predictions of Gender from the Analysis of the Images of Iris and Ocular Regions

Predictions of Gender from the Analysis of the Images of Iri...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Masoud Husayn Mohamed A.E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

the investigation of biological measurements has become a powerful for classifications. The analysis of eye images for gender classification is proven that it is one of the reliable form identification. This paper examines use of support vector machines and CNN for gender predictions based on analyzing images of the ocular and iris regions for determining which one provides more accurate results. Feature extracted by HOG and LBP methods are examined by the SVM classifier. Whilst, pertained CNN is examined with considering the architecture of AlexNet and ResNet18. The proposed models are then evaluated on the MMU2 dataset which contains 995 images. The results of CNN with AlexNet has shown that the eye region yields an accuracy rate of 94.19% higher than the iris with an accuracy of 90.46%. This indicates the feasibility for applications in security, forensics, marketing, customer service improvement.

关键词： Support vector machines Biometrics Iris Accuracy Image analysis Forensics Machine learning Feature extraction Security Reliability

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Digital Twin of Large River Section with Urbanized Floodplain

Digital Twin of Large River Section with Urbanized Floodplai...

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anna Yu. Klikunova Alexander V. Khoperskov Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

Some features of building a digital twin (DT) for analyzing the hydrological state of river systems and adjacent floodplain areas are discussed. The Northern part of the Volga-Akhtuba floodplain is used as the test site. Two main components determine the quality of the digital twin. The first includes a set of basic spatially distributed landscape characteristics that affect the hydrological regime. Geoinformation tools ensure the operation of such a module. The second component is a computing kernel for modeling the dynamics of surface water and groundwater together with the transport of sediments of different types. Validation of the digital twin is based on the agreement of the modeling results with instrumental measurements over long time intervals of about a year. Such a DT can be an effective tool, for example, for constructing cadastral maps of flooded areas.

关键词： Coastlines Computational modeling Soil Spatial databases Data models Digital twins Rivers Iterative methods Floods Water resources

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Detection of Upper Limbs Fractures from X-Ray Images Using Machine Learning Techniques

Detection of Upper Limbs Fractures from X-Ray Images Using M...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdalkreem Masaod Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Bone fractures are common injuries which might affect individuals at any age. The diagnosis should be accurate for proper treatment. Although conventional X-ray imaging is generally practiced, physicians might not be able to diagnose the fractures due several challenges such as infinitesimal fractures that could lead to the misdiagnosis. The misdiagnosis is probably happened especially in the case of the small size of bones. This research is carried out to improve detection of upper limbs fractures. In each upper limbs there are 32 bones. 27 bones are in balms which are quite small pieces. Therefore, an accurate diagnosis is really requires. Automatic diagnosis of bone fractures has already been exploited. However, there are several issues due to the accuracy rate. The proposed framework in this study is an experiment to obtain high level of results. It involves: data gathering, preparation, feature extraction, selection of the best features, and classification. It employs advanced methods such as Gray Level Co-occurrence Matrix, Local Binary Patterns, and deep learning models like VGG16 and DenseNet to improve the diagnosis accuracy. The findings have shown that the SVM classifier yields a rate accuracy of 96%, which outperforms other methods. These findings have been yielded from analyzing the dataset of 8942 samples. This result shows that the proposed framework has outperform other studies.

关键词： Support vector machines Deep learning Limbs Accuracy Medical services Bones Feature extraction X-ray imaging Injuries Computational intelligence

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SonicGlass: An Obstacle Detection and Navigation System Using Smartglass-Based Ultrasonic Sensors

SonicGlass: An Obstacle Detection and Navigation System Usin...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Hrishikesh Govindrao Kusneniwar Surjya Ghosh Sougata Sen Dept. of Computer Science and Information Systems BITS Pilani Goa campus India

Enabling continuous obstacle detection and providing real-time navigational assistance for people with visual impairment allows them to navigate in an indoor space without dependence on others. In this paper, we present the design, development, and evaluation of SonicGlass, a wearable obstacle detection and indoor navigation system. SonicGlass consists of a custom 3D printed wearable smartglass that can replace the individual's existing smartglasses. The smartglass is embedded with multiple sensors and microcontrollers. We performed a user study with 10 participants to determine the performance of SonicGlass and observed that SonicGlass could easily guide individuals in an indoor space while detecting obstacles with an F1-score of 84.7% (at 100% precision), and yielded a localization error of 1.35 meters when using a very simplistic regression model. Overall, SonicGlass can be a helpful tool for people with visual impairment to navigate in indoor spaces without assistance.

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Enhancing LoRaWAN Security: Protection Against Bit Flipping Attacks in IoT Networks

Enhancing LoRaWAN Security: Protection Against Bit Flipping ...

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Security of information and Networks (SIN), International Conference on

作者： Jay Dave Nikumani Choudhury Dantu Havishteja Katuri Revanth Dept. of Computer Science & Information Systems BITS Pilani Hyderabad Campus India

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

The prominence of the Internet of Things (IoT) has surged in recent years due to tech-nological advancements, increased connectivity, and the widespread adoption of smart devices. Currently, IoT solutions are pervasive across various sectors, including smart homes, wearable devices, health-care, transportation, and industrial automation. The Long Range Wide Area Network (LoRaWAN) is a wireless communication protocol designed to provide reliable connection over long distances, spanning many kilometers, using low-power and wide-area networks (LPWANs) often used in IoT applications. However, LoRaWAN is vulnerable to bit-flipping attacks, where transmitted data is intercepted and maliciously altered by flipping specific bits in the message payload. Such attacks have the potential to undermine the trustworthiness and dependability of the system. In this paper, we propose a novel security enhancement for LoRaWAN. Our scheme incorporates a hash digest along with the end device's payload, enabling the application server to detect any malicious alterations in the received content using the hash value. We analyze the security of the proposed scheme against bit-flipping attacks and measure its performance through experiments in a real testbed. Our observations indicate that the proposed mecha-nism not only secures communication against these attacks but also incurs minimal overhead in terms of power consumption, transmission overhead, and latency.

关键词： Network servers Power demand LoRaWAN Transportation Smart homes Security Servers Internet of Things Wearable devices Payloads

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19 53

Investigating Online Searching Behavior Based on Google Tren...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Belkacem, Abdelkader Nasreddine Jamil, Nuraini Alrabaee, Saed College of It Uae University Dept. of Computer and Network Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of It Uae University Dept. of Information Systems and Security Al Ain United Arab Emirates

ISBN: (纸本)9798350336429

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning. © 2023 IEEE.

关键词： COVID-19 Google Trends MENA Online learning Remote learning Web Behavior

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Leveraging the Machine Learning Techniques for Diagnosing Liver Diseases

Leveraging the Machine Learning Techniques for Diagnosing Li...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Warda A. A. Altawati Lubna A. B. Mohammed Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Liver diseases are known as one of the deadly diseases around the world. The diagnosis is crucial due to the fact of that the high incidence and death rates. Although, there are commonly used methods for diagnosing liver disorders, they are too expensive. People who are at risk of liver health issues would benefit from the early and low-cost diagnosis for effective treatment. Recently, machine learning plays obvious roles on healthcare to help medical experts to detect illnesses at early onset of the disease. The diagnosis of the liver diseases has been considered in several studies. This paper develops the accuracy rate of the diagnosis of liver diseases with less size of features. Decreasing the size of feature vectors minimizes the time cost of the machine learning model. The paper puts into practice feature selection techniques: Relief, PCA, PSO, and OS to gain its aim. The proposed method of this paper of the diagnosis is applied on two different datasets: ILPD and BUPA. Four different experiments have been carried out for acquiring high level of outcomes. Despite the promised results obtained from all feature selection techniques considered in this study, the selected features using Relief with the random forest classifier (RF) has yielded the best accuracy rates of 91.3% and 98.55% from ILPD and BUPA respectively.

关键词： Accuracy Liver diseases Mortality Feature extraction Vectors Particle swarm optimization Random forests Principal component analysis Diseases Classification tree analysis

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Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

arXiv

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arXiv 2025年

作者： Agheli, Pouya Pappas, Nikolaos Kountouris, Marios Communication Systems Dept. EURECOM France Dept. of Computer Science and Artificial Intelligence University of Granada Spain Dept. of Computer and Information Science Linköping University Sweden

This paper addresses query scheduling for goal-oriented semantic communication in pull-based status update systems. We consider a system where multiple sensing agents (SAs) observe a source characterized by various attributes and provide updates to multiple actuation agents (AAs), which act upon the received information to fulfill their heterogeneous goals at the endpoint. A hub serves as an intermediary, querying the SAs for updates on observed attributes and maintaining a knowledge base, which is then broadcast to the AAs. The AAs leverage the knowledge to perform their actions effectively. To quantify the semantic value of updates, we introduce a grade of effectiveness (GoE) metric. Furthermore, we integrate cumulative perspective theory (CPT) into the long-term effectiveness analysis to account for risk awareness and loss aversion in the system. Leveraging this framework, we compute effect-aware scheduling policies aimed at maximizing the expected discounted sum of CPT-based total GoE provided by the transmitted updates while complying with a given query cost constraint. To achieve this, we propose a model-based solution based on dynamic programming and model-free solutions employing state-of-the-art deep reinforcement learning (DRL) algorithms. Our findings demonstrate that effect-aware scheduling significantly enhances the effectiveness of communicated updates compared to benchmark scheduling methods, particularly in settings with stringent cost constraints where optimal query scheduling is vital for system performance and overall effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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3D Visualization of Femoral Shaft Fractures 4

3D Visualization of Femoral Shaft Fractures

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4th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2023

作者： Mi, Lam Xin Hoon, Saw Seow Wang, Khor Siak Hui, Saw Seow Utar Faculty of Information and Communication Technology Dept. of Information Systems Perak Malaysia Utar Dr. WLT-CRCD Utar Sg. Long Faculty of Science Dept. of Allied Health Sciences Perak Malaysia Utar Faculty of Information and Communication Technology Department of Computer Science Perak Malaysia

ISBN: (纸本)9798350318432

The absence of an independent learning platform to address femoral shaft fractures poses challenges in understanding and classifying these fractures. The existing anatomy platforms, such as 3D interactive anatomical atlases, are insufficient in providing comprehensive information on the fractures and the high cost of these interactive anatomical atlases can be a barrier for learning to proceed due to the needs of the actual bones and human models. This study aims to develop an accessible, cost-effective educational 3D mobile application that caters to the learning needs associated with human femoral shaft fractures. Although femoral fractures are relatively uncommon, the increased cases of fractures necessitate awareness and preparedness among individuals as well as medical professionals. The proposed mobile application will enhance users' understanding of anatomical structures in femoral shaft fractures and contribute to improved patient care as well as enhance medical education. © 2023 IEEE.

关键词： Fracture

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