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10th International Conference on Socio-Technical Perspectives in Information Systems, STPIS 2024

作者： Henriksson, Elin Uddstrom, Anna van Bree, Lotte Department of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden

In an era of growing digitization, technology is essential for communication and daily life. However, inaccessible websites, including in Sweden, create barriers for individuals with disabilities, often due to insufficient education and awareness among web developers. Our quantitative study aimed to assess the status of web accessibility education within Swedish web development and interface design programs. We analyzed curricula and surveyed 63 final-year students across 23 programs. Findings show that while most programs (18 out of 23) include web accessibility, integration levels vary. Only 2% of courses offered standalone accessibility objectives, and 7% incorporated it within other courses, leaving 91% without any. Student knowledge varied;69.8% were aware of accessibility guidelines and laws, but 30.2% were not. Additionally, only 26.2% of students reported acquiring knowledge about accessibility guidelines and laws from their current education. This underscores the need for better web accessibility education. © 2024 Copyright for this paper by its authors.

关键词： Accessibility accessibility guidelines education interface design WCAG web accessibility web development

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Cyber Threat Intelligence in Smart Cities: Bayesian Inference and Energy Optimization in LoRa Networks for Big Data Applications

Cyber Threat Intelligence in Smart Cities: Bayesian Inferenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Karras, Aristeidis Theodorakopoulos, Leonidas Karras, Christos Antonopoulou, Hera University of Patras Computer Engineering and Informatics Department Patras Greece University of Patras Department of Management Science and Technology Patras Greece

ISBN: (纸本)9798350362480

In the evolving landscape of smart cities, optimizing energy consumption and enhancing cybersecurity in Internet of Things (IoT) networks are crucial. This study leverages LoRa (Long Range) technology, Bayesian Inference, and Extreme Learning Machines (ELMs) to advance cyber threat intelligence and energy efficiency in large-scale IoT deployments. The proposed algorithms address key challenges within LoRa networks by implementing a novel energy consumption model, kernel-based ELM fine-tuning, Bayesian parameter tuning, and data fusion. Through Bayesian Inference, our approach dynamically adjusts network parameters to optimize packet transmission and collision rates, ultimately reducing power consumption across smart city applications. The Kernel-ELM algorithm fine-tunes LoRa network applications by adapting kernel parameters to the unique demands of IoT environments. Additionally, our Anomaly Detection and Fusion Algorithm (ADFA) integrates data from multiple sources to detect potential cyber threats, enhancing network security. Experimental results validate these algorithms on various datasets, demonstrating improvements in both energy optimization and threat detection. Ultimately, this study provides actionable insights into the deployment of scalable, energy-efficient, and secure IoT networks for smart city infrastructure. © 2024 IEEE.

关键词： Cyber attacks

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Performance Evaluation of Wearable Internet of Things Devices for Indoor Positioning 10

Performance Evaluation of Wearable Internet of Things Device...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Papadopoulos, George Spachos, Petros University of Thessaly Department of Computer Science and Biomedical Informatics Greece University of Guelph School of Engineering GuelphON Canada

ISBN: (纸本)9798350373011

Indoor localization and tracking services are necessary for several popular applications from asset monitoring to location-based marketing. The use of Internet of Things (IoT) devices in those services has increased due to their low cost and ease of deployment. However, indoor services pose significant challenges due to the lack of GPS signals as well as the complexity of the indoor environment. Alternative approaches, mainly including wireless technologies, should be considered. In this work, we examine the feasibility of using wearable IoT devices for indoor tracking. Specifically, we use smartwatches and we examine the performance of Wi-Fi and Bluetooth Low Energy (BLE) for indoor tracking. According to experimental results, BLE outperformed Wi-Fi in terms of energy efficiency while Wi-Fi, on the other hand, demonstrated a slightly better accuracy in Line of Sight conditions. © 2024 IEEE.

关键词： Bluetooth

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Assisting Neurosurgeons: Advancing Brain Tumor Surgery with Smartphone Augmented Reality Navigation

Assisting Neurosurgeons: Advancing Brain Tumor Surgery with ...

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2024 International Conference on Innovation and Intelligence for informatics, Computing, and Technologies, 3ICT 2024

作者： Java, Muhammad Iskandar Hidayati, Shintami Chusnul Sarno, Riyanarto Anistyasari, Yeni Ali, Khurshed Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Engineering Universitas Negeri Surabaya Surabaya Indonesia Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331533137

Integrating Augmented Reality (AR) into neurosurgical procedures has shown substantial promise in enhancing surgical precision and educational outcomes, building upon previous applications primarily focused on simple visualization tasks. This study extends the current scope by using smartphone-based AR navigation systems tailored to assist neurosurgeons performing brain tumor surgery. Unlike prior efforts, our research developed and validated an AR system that superimposes precise virtual representations of critical anatomical structures, such as the skull and brain, onto a 3D-printed patient model. These models are rendered in transparent materials that improve the visibility and understanding of intricate internal structures, facilitating more intuitive surgical planning and training. Our findings demonstrate that while AR significantly enhances the visualization of anatomical details and the alignment of virtual to physical models, its effectiveness depends on the viewing angle and facial feature distinctiveness, achieving the highest accuracy in frontal views. These results underscore improvements over traditional AR systems, which often struggle with accurate depth perception and spatial orientation, and highlight the challenges of broad integration. ©2024 IEEE.

关键词： Smartphones

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Method of Automated Dataset Collection for Microwave Filters Synthesis

Method of Automated Dataset Collection for Microwave Filters...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Arinin, O.V. Bakhmach, D.M. Katsnelson, A.I. Kurbeteva, A.S. Yelizarov, A.A. Moscow Technical University of Communications and Informatics Moscow Russia Hse University Electronic Engineering Department Moscow Russia Hse University Computer Engineering Department Moscow Russia

ISBN: (纸本)9798331532635

This research discusses the method of dataset collection automatization for microwave filter synthesis by integrating machine learning techniques, thus reducing development time. Utilizing the 3D electromagnetic analysis software package, the study involves simulation and collecting geometric parameters and amplitude-frequency characteristics from three variants of passband highly selective microstrip tworesonator combined filters with stepped impedance resonators. In this paper, specific items and restrictions on filter topology creation in the simulation program are discussed, as well the review of the script used for dataset collection and its requirements is given. In addition, a dataset structure and format are described. © 2025 IEEE.

关键词： Software packages

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Digital Twin System for Smart Buildings Integrated with Blockchain and Mixed Reality Technology 1

Digital Twin System for Smart Buildings Integrated with Bloc...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Hidayat, Wahyu Nur Hakim, Oktafian Sultan Sukaridhoto, Sritrusta Zainuddin, Muhammad Agus Prayudi, Agus Arissabarno, Cahyo Achmad, Zacky Maulana Budiarti, Rizqi Putri Nourma Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Indonesia Universitas Nahdlatul Ulama Surabaya Indonesia

ISBN: (纸本)9798350365191

The integration of 3D innovation, Mixed Reality (MR), and blockchain in smart buildings has revolutionized the IoT sector. Visualization using 3D technology and immersive interfaces allows users to control and interact with devices within smart building environments. Users can use hand gestures or controllers in a 3D environment to adjust lighting, monitor energy consumption, or manage other smart devices. When combined with MR, the capabilities of 3D technology are enhanced with virtual elements overlaying onto the real-world environment. Within smart buildings, MR can visualize real-time sensor data, display device statuses, and provide intuitive visual feedback. The integration of blockchain in this ecosystem adds layers of security, transparency, and trust by securely recording and verifying device data, control commands, and user interactions. Blockchain technology enables secure peer-to-peer communication and smart contracts for automated device interactions and control. User testing results for controlling smart buildings with Mixed Reality (MR) technology showed high user satisfaction with an average score of 4.48. Users appreciated the performance and functionality of the system, as well as the new experience provided by the natural and immersive MR interface for interacting with smart building controls. © 2024 IEEE.

关键词： Smart homes

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An intrusion detection system using ranked feature bagging

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1213-1219页

作者： Azhagiri, M. Rajesh, A. Karthik, S. Raja, K. Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Jain University Bangalore India Department of IT College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

An Intrusion Detection System monitors the network for any malicious attacks. It is an ideal tool for protecting extensive business networks from any kind of attack. In this paper, an Intrusion Detection System using Bagging and ranked features has been proposed. The sample datasets for training the ensemble of classifiers in the bagging approach are obtained by generating subsets of the features ranked according to their importance in the classification process. OneR algorithm has been used to rank the features. The decision tree has been used as the base classifier in the bagging approach. The proposed system was able to predict attacks with an accuracy of 99.71% with a minimal number of features and outperformed other similar approaches discussed in the literature. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bagging CIC-IDS 2017 dataset Decision tree Ensemble Feature selection Intrusion detection system OneR feature selection

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Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

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computers, Materials & Continua 2024年第12期81卷 4787-4832页

作者： Israa Ibraheem Al Barazanchi Wahidah Hashim Reema Thabit Mashary Nawwaf Alrasheedy Abeer Aljohan Jongwoon Park Byoungchol Chang College of Computing and Informatics Universiti Tenaga Nasional(UNITEN)Kajang43000Malaysia College of Engineering University ofWarith Al-AnbiyaaKarbala56001Iraq Faculty of Information Science&Technology Universiti Kebangsaan MalaysiaBangi43600Malaysia Department of Computer Science Applied CollegeUniversity of HailHail55424Saudi Arabia Department of Computer Science and Informatics Taibah UniversityMedina42353Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Republic of Korea

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning ***,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state *** models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal *** diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed *** goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic *** propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies ***,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN *** further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and *** validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’***,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model *** findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

关键词： computer science clinical decision support system(CDSS) medical queries healthcare deep learning recurrent neural network(RNN) long short-term memory(LSTM)

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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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Smart Road Damage Survey System Using Machine Vision and IoT Technology 3

Smart Road Damage Survey System Using Machine Vision and IoT...

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3rd International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2023

作者： Mutijarsa, Kusprasapta Alunjati, Figo Agil Hidayat, Fadhil Kurnia, Ranna Arsyad, Zulkifli School of Electrical Engineering and Informatics Bandung Indonesia National Institute of Technology Department of Civil Engineering Bandung Indonesia Bandung State Polytechnic Department of Computer and Informatics Engineering Bandung Indonesia

ISBN: (纸本)9798350394559

Road is an essential transportation infrastructure to move people and goods to support economic prosperity. The efficiency, safety, security, and comfort of people have significant consequences by damage and defects on the road surface. A good-condition road ensures the land traffic runs well. This research is a collaboration between the Bandung Institute of Technology and the department of Public Works and Spatial Planning of West Java Province to develop a smart system for road damage surveys. The system was built by leveraging machine vision and IoT technologies to improve the performance of the road survey activities. The previous road damage detection, classification method, and pavement condition index (IKP) computation were performed semi-automatically. The proposed survey system developed in this work uses the Canny edge Detection algorithm and MobileNet V2 architecture to detect road defects automatically. The pavement condition index computation also improved by using the interpolation reduction factor. The system is designed into a four-layer architecture that consists of several functions and modules, i.e., the sensor system that includes a vision sensor, GPS and odometry, a hardware embedded system using a microcontroller, and machine learning to process the road condition image automatically, and a dashboard application to reveal the road condition on map. The performance resulted in accuracy, precision, recall, and F-score of the road damage detection are 54.5%, 24.2%, 78.9%, and 37%, respectively. © 2023 IEEE.

关键词： computer vision

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