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A novel image descriptor based on dot-diffused block truncation coding with bit probability

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 14841-14858页

作者： Riyono, Dwi Sun, Chi-Chia Guo, Jing-Ming Wong, KokSheik Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electrical Engineering National Formosa University Yunlin Huiwei Taiwan Faculty of Computer Science and Information Technology Monash University-Malaysia Campus Kuala Lumpur Malaysia

With the advent of mobile smart devices and ubiquitous network connections, digital images can now be conveniently captured, edited, and shared online worldwide. The ever-increasing number of pictures poses a technically challenging, yet unavoidable problem of efficiently and accurately finding a desired image. In this work, we develop a new image retrieval system based on an image compression technique, namely dot-diffused block truncation coding (DDBTC) with bit probability. Specifically, the color feature derived from color distribution and the bitmap feature (BF) derived from both edges and textures jointly describe the image. The degree of similarity between two images is then measured by their respective color and BFs by using the modified Canberra distance metric. Experimental results show that the proposed feature descriptor achieves superior image retrieval performance as compared to the former DDBTC image retrieval feature and conventional non-DDBTC based features. Additional experiments on image classification verify that the proposed feature descriptor outperforms conventional image classification methods. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Color

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Dual objective bandit for best channel selection in hybrid band wireless systems

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4115-4125页

作者： Hashima, Sherief M. Fouda, Mostafa Hatano, Kohei Kasban, Hany Mohamed, Ehab Mahmoud Fukuoka819-0395 Japan Engineering Department Nuclear Research center Egyptian Atomic Energy Authority Inshas Cairo13759 Egypt Electrical and Computer Engineering Department College of Science and Engineering Idaho State University PocatelloID83209 United States Faculty of Arts and Science Kyushu University Fukuoka819-0395 Japan Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Wadi Addwasir13759 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

This paper manipulates a creative online learning solution for optimal band/channel assignment in hybrid radio frequency and visible light communication (RF/VLC) systems. Typically, the multiband transmitter (Tx) has no prior information about different channel characteristics including their payoffs (achievable data rate) and energy consumption outcome. Practically, Tx has to choose the best reward arm/band with the lowest energy consumption to prolong its limited battery capacity. Hence, we envision a lightweight cost-aware multi-armed bandit (CA-MAB) as a proper realistic solution to the cumbersome and slowly convergent ordinary band assignment methods, where the transmitter/player intends not only to maximize his cumulative payoff (achievable data rate) but also to mitigate his cost (battery consumption due to the utilized band). Therefore, we propose a dual objective MAB scheme to manage such problem intelligently. Numerical simulations indicate that proposed method outperforms naive MAB versions, including Thompson sampling (TS), Upper Confidence bound (UCB), and traditional hybrid band selection (HBA) approaches, correspondingly. Especially, our proposed algorithm delivers 99% of the optimal performance concerning energy consumption, achievable data rate, and convergence speed. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Millimeter waves

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Forecasting Machine Learning Based Feature Selection for Dengue Prediction in the Early Stage 3

Forecasting Machine Learning Based Feature Selection for Den...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Archana, T. Faritha Banu, J. SRM Institute of Science and Technology Bharathi Salai Ramapuram Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350317022

Dengue fever is rapidly becoming a prominent health concern issue affecting the tropical and sub-tropical regions. This disease can cause serious illness in humans and is spread by bleed-feeding arthropods such fleas, parasites and mosquitoes. This disease is now becoming endemic in many nations. Delayed diagnosis accompanying with unsuitable treatment may result in the risk of death. This study's objective is to inquire into the effects, signs and clinical symptoms allied with dengue. The proposed work's objective is to examine the symptoms relating to Dengue through Statistical and Analytical measures. Machine learning Classification algorithms such as Random Forest, CART and Support Vector Machine are applied to identify the significant symptoms. The symptoms from both the measures are analyzed, ranked and finally grouped as primary symptoms. The performance of the classifier is assessed using performance evaluation measures. The experimental results show that CART is more suitable for proposed prediction model for Dengue with accuracy 97%, precision 98.3%, recall 96.8% and F-measure metrics 97.5%. © 2023 IEEE.

关键词： CART Dengue Machine Learning Random Forest Support Vector Machine

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Security Management of Autonomous Transportation Services in the Tourism Industry 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Thayananthan, Vijey Qasem, Mamoun Qureshi, Muhammad Shuaib Qureshi, Muhammad Bilal Cybersecurity Department Faculty of Computing Engineering and Science University of South Wales Cardiff United Kingdom Department of Computer Science and Engineering Chungnam National University Daejeon Korea Republic of Department of Computer Science and IT University of Lakki Marwat Taja ZaiKPK 28420 Pakistan

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

Autonomous vehicles, lifestyles, and businesses related to the tourism industry are facing a lot of security issues when the digital revolution is considered in the digital version of security management. The aim of this research is to design and develop an efficient framework with security management (SM) and incidence response (IR) for securing autonomous transportation services (ATS). In the SM and IR, the quality of secure ATS and quickness will be considered. To achieve the objective of the research, service encounters (a form of social exchange) through social networks and digital transformation are used as a method. Service encounters based on emerging technology are the best approach because these future services evolve with emerging technologies, which depend on digital transformation and revolution. When the digital age matures in the tourism industry, users are transformed to use digital devices integrated within autonomous transportation systems. Although the legacy of digital systems is still considered in transportation services, the problems of digital transformation create complicated situations for many tourists because service providers transform their ATS from traditional management to digital management, such as the digital transformation of tourism management with security. Many ATS upgrades of digital transformation in tourism management, which prevent the use of service encounters and accessing necessary services at the time, are challenging problems. As a result, this research study will indicate that the SM and IR can be improved through the proposed framework. The proposed framework will provide essential security management and incident response to the users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Leisure

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Gaps in value-driven connectivity: A case study on connectivity challenges in Northern Europe 35

Gaps in value-driven connectivity: A case study on connectiv...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Karppinen, Pasi Bandaranayake, Sameera Haukipuro, Lotta Elmistikawy, Yomn Saarnisaari, Harri University of Oulu Faculty of Information Technology and Electrical Engineering Oulu Finland University of Oulu Faculty of Science Oulu Finland Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden

ISBN: (纸本)9798350362244

Well-working Internet connectivity is a fundamental citizen right and a necessity in modern digital societies. Nevertheless, the digital divide persists and often affects rural or remote areas. To bridge the digital divide, we need to understand the specific rural use cases and challenges. This would increase understanding about problems weak Internet connectivity causes, what is required from the connectivity technology and what is needed from policymakers and regulation. The goal of this paper is to identify rural use cases and analyze consequences to future connectivity technology through a mixed-methods approach: we investigated the divide and its consequences by a questionnaire and interviews conducted in northern Europe. The study found that the main issues faced by rural internet users are related to capacity, availability, and vulnerability of internet services. These issues hinder their daily life, work activities, and expose them to system-level risks. The article discusses how these findings can inform the development of more inclusive and resilient internet connectivity solutions. © 2024 IEEE.

关键词： digital divide mobile communication rural connectivity social sustainability

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Human-Robot Interaction on Elderly Companion Robot Development Using Dual Intent Entity Transformer 7

Human-Robot Interaction on Elderly Companion Robot Developme...

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7th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2023

作者： Rauf, Naufal Haidar Santoso, Heru Agus Universitas Dian Nuswantoro Faculty of Computer Science Department of Informatics Engineering Semarang Indonesia

ISBN: (纸本)9798350382266

The growing elderly population and the increasing demand for nursing home care have led to a need to improve the quality of life for residents. A popular solution is developing a companion robot to assist residents with various tasks and provide human-like interaction. In this paper, we present the prototype of a companion robot currently website-based, equipped with a Dual Intent Entity Transformer to understand what the user wants. The results of the study show the prototype is capable of classifying user messages according to their appropriate intent with precision, recall, F-score, and accuracy on average 91 %, 91 %, 90%, and 87% respectively using privately owned dataset. Future works such as expanding the robot's insight and tuning the model would improve the service provided by the companion robot. © 2023 IEEE.

关键词： Human robot interaction

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Adaptive Quantum Learning Frameworks for Real-Time IIoT Attack Identification 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Joshi, Poonam Goyal, S.B. Rajawat, Anand Singh Solanki, Ram Kumar Chen, Chuqiao Department of Computer Engineering Thakur College of Engineering and Technology Mumbai India Faculty of Information Technology City University Petaling Jaya46100 Malaysia School of Computer Science & Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

Securing its networks from cyber-attacks is of utmost importance as the Industrial Internet of Things (IIoT) becomes a lynchpin of contemporary industrial ecosystems. With the increasing complexity and sophistication of cyber threats, traditional machine learning approaches, while successful, frequently struggle with real-time identification due to computational restrictions. In this study, we present an adaptive quantum learning framework for real-time IIoT attack identification that takes advantage of quantum computing’s inherent parallelism and speed. We introduce a novel quantum technique that can quickly and accurately identify both new and existing attack vectors as they emerge in dynamic threat landscapes. The experimental results show improved accuracy rates and a drastic decrease in identification delay compared to classical methods. According to our research, quantum-enhanced learning frameworks have great potential to strengthen IIoT security in the face of more sophisticated cyber-attacks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Design and Development of Semi-Steering and IoT Systems for Electric Mobility Systems 6

Design and Development of Semi-Steering and IoT Systems for ...

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Abdelmalek, Shady R. Retig, Ma Althea F. B. Ansari, Fakeha S. Gomaa, Mennatallah A. Alaaeldin, Amr Kaddah, Sahar S. Badr, Basem M. University of Genoa Faculty of Engineering Robotics Department Italy The British University in Dubai Faculty of Engineering and It Computer Science Department United Arab Emirates Mansoura University Renewable and Sustainable Energy Department Mansoura Egypt Mansoura University Faculty of Engineering Electrical Power Department Mansoura Egypt Mansoura University Faculty of Engineering Electrical Engineering Department Mansoura Egypt Lotus Power VancouverBC Canada

ISBN: (纸本)9798350378511

Connecting electric mobility (E-Mobility) systems with Internet of Things (IoT) technology is a crucial strategy for modern transportation and driving assistance systems. IoT and Advanced Driver Assistance Systems (ADAS) applications in the field are examined in this research. Different techniques are designed and developed for ADAS functionality, such as lane and object detection via image and video segmentations. Multiple lane detection models are developed and tested, each of which is then deployed on AWS (Amazon Web Services) cloud and optimized using OpenVINO to speed up inference. Furthermore, telemetry data from various embedded devices are transmitted to AWS IoT core and presented on an easy-to-use web application. This paper shows how deploying systems on cloud platforms like AWS improves scalability and operational efficiency. It also highlights how IoT and cloud technologies may improve the performance of E-Mobility systems and offers a strong foundation for further advancements in the ADAS area. © 2024 IEEE.

关键词： Web services

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