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International Journal of Interactive Mobile Technologies 2022年第14期16卷 123-135页

作者： Maabreh, Majdi Obeidat, Ibrahim Elsoud, Esraa Abu Alnajjai, Asma Alzyoud, Rahaf Darwish, Omar Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University P.O. Box 330127 Zarqa13133 Jordan Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Information Security and Applied Computing Department Eastern Michigan University MI United States

Cyber attacks have increased in tandem with the exponential expansion of computer networks and network applications throughout the world. Fortunately, various machine/deep learning models have demonstrated excellent accuracy in predicting network attacks in the literature;nonetheless, having simple and understandable models might be a big benefit in network monitoring systems. In this study, we evaluate four feature selection algorithms to find the minimal set of predictive features of network attacks, seven classical machine learning algorithms, and the deep learning algorithm on one million random instances of the CSE-CIC-IDS2018 big data set for network intrusions. The feature selection algorithms highlighted the importance of features related to forwarding direction (FWD) and two flow measures (FLOW) in predicting the binary traffic type;benign or attack. Furthermore, the results revealed that not all features are required to build efficient ML/DL in detecting network attacks, four features unanimously selected by the feature selection algorithms were enough to build comparable ML models to those trained on all features. This might lead to models that are more suitable for deployment in terms of complexity, explainability, and scalability. Moreover, by selecting four unanimity features instead of all traffic features, the training time may be decreased by 10% to 50%. © 2022. All Rights Reserved.

关键词： Network security

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Lessons Learned in Transferring a Lecture on Virtual Reality into Immersive Virtual Reality 20

Lessons Learned in Transferring a Lecture on Virtual Reality...

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Die 20. Fachtagung Bildungstechnologien der Gesellschaft fur Informatik e.V., DELFI 2022 - 20th Conference on Educational Technologies of the German Informatics Society, DELFI 2022

作者： Deuchler, Jonas Wölfel, Matthias Karlsruhe University of Applied Sciences Faculty of Computer Science and Business Information Systems Moltkestraße 30 Karlsruhe76133 Germany University of Hohenheim Schloß Hohenheim 1 Stuttgart70599 Germany

ISBN: (纸本)9783885797166

Immersive virtual reality offers novel approaches on live-teaching by challenging the idea of the physical classroom. Here we present our experiences and learnings from our work-inprogress on transferring a real-life lecture about immersive virtual reality to the medium itself, employing a custom-implemented social virtual reality application. We highlight how students benefit from virtual lectures through a higher form of student participation and individual customisation, employing learning frameworks in accordance with the specificities of virtual reality like experiential learning and game-based learning. In addition, we show how the custom virtual environment allows to exploit the pedagogic potential of virtual reality to a great extent by enabling to intervene and control a virtual classroom at a deeper level and to respond to evaluations quickly and in an iterative incremental way, especially in comparison to currently available public social virtual reality platforms. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Virtual reality

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IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmentation Network

IAMS-Net: An Illumination-Adaptive Multi-Scale Lesion Segmen...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zheng, Yisen Huang, Guoheng Zhang, Feng Cheng, Lianglun Yuan, Xiaochen Zhong, Guo Luo, Shenghong School of Computer Science and Technology Guangdong University of Technology Guangzhou China The First Affiliated Hospital of Sun Yat-Sen University Department of Otorhinolaryngology Guangzhou China Macao Polytechnic University Faculty of Applied Sciences China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China University of Macau Faculty of Science and Technology China

ISBN: (纸本)9781665410205

In recent years, many Lesion segmentation (LS) models based on UNet have been proposed. However, existing researches rarely consider the influence of illumination change leads to the weak boundary area. Such as melanomas and polyps, the demarcation of the boundary between the diseased area and the surrounding tissue remains particularly challenging. To overcome these challenges, we propose an IlluminationAdaptive Multi-scale Lesion Segmentation Network (IAMS-Net). In IAMS-Net, we integrate Illumination-Adaptive MultiStream Attention (IAMA) and Contour Perception Module (CPM). In the decoding stage, the IAMA is used as a bridge between the encoder and the decoder to solve the adverse effects of illumination changes on the segmentation of weak boundary lesions. In order to further enhance the boundary features lost due to illumination change in the low-contrast lesion area, we introduce the CPM to improve the perception of the integrity of the lesion area. Subsequently, we performed comparison and ablation experiments using the publicly available ISIC2018 dataset and the individually collected data set BoreIllumination(BI). © 2024 IEEE.

关键词： Signal encoding

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain systems

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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OntoMG: a unique and ontological-based intelligent framework for early identification of myasthenia gravis (MG)

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International Journal of information Technology (Singapore) 2024年第6期16卷 3847-3853页

作者： Mahajan, Prerna Agarwal, Trapty Vekariya, Daxa Gupta, Rajesh Malviya, Ashwini Anandaraj, S.P. Jain, Garima Anand, D. Department of Computer Science and Information Technology Jain (Deemed to Be University) Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara India Department of Management Sanskriti University UP Mathura India Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science Engineering Presidency University Bengaluru India Department of Computer Science and Business Systems (CBS) Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Myasthenia Gravis (MG) is a neuromuscular disease causing extreme muscular fatigue, triggering problems with vision, swallowing, speech, mobility, dexterity, and breathing. However, early detection and prediction of MG is a crucial and quite challenging task for medical practitioners. So, in our proposed work, we are focused on introducing an ontology-based intelligent system for the prognosis of patients affected by this neuromuscular disease. The given paper presents a novel framework that employs the concepts of ontology, semantic web rules, and reasoner for deriving whether the patient is tested positive or negative. The suggested framework's effectiveness is assessed by comparing it to machine learning classifiers using parameters from the NIH repository for Myasthenia Gravis. The experimental results show that the ontological-based framework is able to achieve higher accuracy (80.85%), higher precision (78.37%), and higher recall (76.55%) than the existing classifiers used in the recent studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Machine learning (ML) Myasthenia gravis disease Ontology Reasoners Semantic web rules language (SWRL)

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Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning Model for Intrusion Detection System

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computer systems science & Engineering 2023年第11期47卷 2497-2512页

作者： Mahmoud Ragab Sultanah M.Alshammari Abdullah S.Al-Malaise Al-Ghamdi Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

The Internet of Things(IoT)system has confronted dramatic growth in high dimensionality and data *** system named intrusion detection systems(IDS)is broadly utilized for the enhancement of security posture in an IT *** IDS is a practical and suitable method for assuring network security and identifying attacks by protecting it from intrusive ***,machine learning(ML)-related techniques were used for detecting intrusion in IoTs ***,the IoT IDS mechanism faces significant challenges because of physical and functional *** IoT features use every attribute and feature for IDS self-protection unrealistic and *** study develops a Modified Metaheuristics with Weighted Majority Voting Ensemble Deep Learning(MM-WMVEDL)model for *** proposed MM-WMVEDL technique aims to discriminate distinct kinds of attacks in the IoT *** attain this,the presented MM-WMVEDL technique implements min-max normalization to scale the input *** feature selection purposes,the MM-WMVEDL technique exploits the Harris hawk optimization-based elite fractional derivative mutation(HHO-EFDM)*** the presented MM-WMVEDL technique,a Bi-directional long short-term memory(BiLSTM),extreme learning machine(ELM)and an ensemble of gated recurrent unit(GRU)models take place.A wide range of simulation analyses was performed on CICIDS-2017 dataset to exhibit the promising performance of the MM-WMVEDL *** comparison study pointed out the supremacy of the MM-WMVEDL method over other recent methods with accuracy of 99.67%.

关键词： Internet of Things intrusion detection system machine learning ensemble deep learning metaheuristics

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data 3

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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3rd International Conference on Communication, Control, and Intelligent systems, CCIS 2024

作者： Prasanna, V. Suresh Kumar, N. Divyaprabha, P. Sundaram, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Data Science and Business Systems Chennai India Sri Ramakrishna Engineering College Department of Information Technology Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Ganesh College of Engineering Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798331528201

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN-GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors' information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python. © 2024 IEEE.

关键词： Recurrent neural networks

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Comparative Analysis of Machine Learning Algorithms in Vehicle Image Classification 22nd

Comparative Analysis of Machine Learning Algorithms in Veh...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Asry, Nur Izzaty Muhammad Mustapha, Aida Mostafa, Salama A. Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia KM 1 Jalan Panchor Johor Pagoh84600 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia

ISBN: (纸本)9789819972395

Object identification in the area of computer Vision essentially focus on how robots are able to perceive in comparison of human eyes. The main task for vehicle detection is detecting vehicles from image sources. This study utilises Weka to categorise vehicle photos into 3 vehicle groups based on data mining approach. In feature extraction, three image filtering algorithms are used, which are Colour Layout, Edge Histogram, and Pyramid Histogram of Oriented Gradients (PHOG). The features extracted are then fed into five classification algorithms for a comparative analysis: Multilayer Perceptron (MLP), Simple Logistic, Sequential Minimal Optimization (SMO), Logistic Model Tree (LMT), and Random Forest. The experimental results showed that PHOG filtering algorithm produced the best set of features to accurately classify the vehicle images. During classification, the SMO classifier achieved the best accuracy of 46.3158%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Advanced Cryptocurrency Price Prediction: Incorporating Time-Reversed ARIMA and BVIC 19

Advanced Cryptocurrency Price Prediction: Incorporating Time...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Luc, Minh Tuan Senkerik, Roman Dang, Tran Khanh Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Republic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Viet Nam Institute for Experiential Technology Van Lang University Ho Chi Minh city Viet Nam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Viet Nam

ISBN: (纸本)9798331507817

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements. © 2025 IEEE.

关键词： Prediction models

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Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e Performance Evaluation 9th

Spectrum Serenade: OPNET Expedition Unveiling WLAN 802.11e P...

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9th Future Technologies Conference, FTC 2024

作者： Ali, Ali Mohd Owida, Hamza Abu Al-Qerem, Ahmad Department of Electrical Engineering College of Engineering Technology Al-Balqa Applied University Amman Jordan Medical Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13132 Jordan

ISBN: (纸本)9783031731242

Interest in Wireless LAN 802.11 is climbing owing to its cost-effectiveness and simplicity of installation, yet its restricted Quality of Service (QoS) capabilities present obstacles for real-time apps. This endeavour aims to evaluate QoS characteristics and boosts by devising scenarios within the OPNET simulator. Through contrasting Wireless LAN 802.11e with the standard 802.11, our goal is to measure how advancements in allocated channel access can elevate performance levels. Findings show that the improved standard provides a more effective service mechanism, giving priority to traffic that is highly important while keeping latency within acceptable bounds, hence enhancing QoS support for contemporary applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Quality of service

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