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International Conference on Computer, Electrical & Communication engineering (ICCECE)

作者： T. Lalitha Jerzy Ryszard Szymański Marta Żurek-Mortka Mithileysh Sathiyanarayanan K. R. Srilekha Computer Science School of Information Science Presidency University Bengaluru Karnataka India Department of Electrical Drives and Industrial Electronics Faculty of Transport Electrical Engineering and Computer Sciences Casimir Pulaski University of Radom Poland Europe Lukasiewicz Research Network Institute for Sustainable Technologies Kazimierza Pułaskiego Radom Poland Europe MIT Square London I M.Tech Integrated(Software Engineering) VIT University Vellore Tamilnadu India

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

Fuel utilization and emission estimation are essential for assessing the consequence of materials and strict emission control measures due to the startling rate of weather changes. The world's socioeconomic issues are significantly impacted by the growing worldwide average temperature and its effects on climate change. The melting of the polar ice caps, that is a direct result of this rise, leads to a number of other problems, such as the polar animal’s death, coastal flooding, and exposure to bacteria and earliest microorganisms that are frozen in the snow and could cause many more global pandemics and invisible illnesses. In this paper, Enhanced Prediction of Fuel Consumption and Carbon Emissions in Light-Duty Vehicles using Dynamic Graph Convolutional Recurrent Imputation Network (EP-FCCE-DGCRIN) are proposed.. The effectiveness of the EP-FCCE-DGCRIN technique is compared to other existing methods. This research offers evidence-based suggestions to lessen the environmental effects of vehicles for both manufacturers and consumers depending upon the outcomes of the Descriptive Statistics analysis.

关键词： Temperature measurement Pandemics Snow Sea measurements Carbon dioxide Imputation Fuels Vehicle dynamics Socioeconomics Meteorology

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A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

arXiv

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arXiv 2023年

作者： Li, Xiao-Yu Zhu, Qin-Sheng Hu, Yong Wu, Hao Yang, Guo-Wu Yu, Lian-Hui Chen, Geng School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Physics University of Electronic Science and Technology of China Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610103 China Institute of Electronics and Information Industry Technology of Kash Kash 844000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation. © 2023, CC BY-NC-SA.

关键词： Stochastic systems

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Cybersecurity Enhancement in IoT-Enabled Public Health information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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AI-Driven Smart Healthcare for Society 5.0

作者： Mahesh Kumar Mishra Lalit Mohan Trivedi Mansoor Farooq Divya Nimma Malik Bader Alazzam B Kiran Bala Sr SAP Consultant Delta System and Software Inc Texas USA Department of ASH Moradabad Institute of Technology Moradabad UP India Department of Cybersecurity American University of Bahrain Bahrain PhD in Computational Science University of Southern Mississippi Data Analyst in UMMC USA Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

The rising use of IoT devices in Public Health information systems (PHIS) has revolutionized the way patient surveillance and record keeping is being done in real-time, but this has exposed systems to serious cybersecurity threats. Generated by using traditional approaches, like rule-based detection and basic artificial neural network-based models, is not capable of dealing with the dynamic and diverse nature of threats in IoT networks. These approaches are often inadequate in providing a detailed analysis of the attack especially because they do not have the ability to learn from sequences of features. This work presents an improved predictive analytics model using CNN with BiLSTM to improve cybersecurity in IoT connected PHIS. The CNN component performs feature extraction on spatial elements in the data received while the BiLSTM uncovers temporal dependencies and sequence of attacks, which enhance threats identification accuracy. The process of the proposed methodology includes dataset acquisition and cleansing of IoT healthcare datasets and finally, the training and testing of the model with the help of relevant parameter such as accuracy, precision, and recall values. Qualitative comparison with the baseline models also shows that the proposed hybrid CNN-BiLSTM model performs better, and it has fewer false alarms to detect a range of cyber threats. Accordingly, the results point to a feasible solution for enhancing security and reliability of IoT-based healthcare applications to protect patients’ record from cyber threats.

关键词： Training Accuracy Surveillance Bidirectional long short term memory Threat assessment Internet of Things Computer security Public healthcare information systems Testing

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Cybersecurity Enhancement in IoT-Enabled Public Health information systems Using Deep Learning Techniques

Cybersecurity Enhancement in IoT-Enabled Public Health Infor...

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2025 AI-Driven Smart Healthcare for Society 5.0, AdSoc5.0 2025

作者： Mishra, Mahesh Kumar Trivedi, Lalit Mohan Farooq, Mansoor Nimma, Divya Alazzam, Malik Bader Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX United States Department of ASH Moradabad Institute of Technology UP Moradabad India Department of Cybersecurity American University of Bahrain Bahrain University of Southern Mississippi Data Analyst in UMMC United States Faculty of Information Technology Jadara University Irbid Jordan Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798331536336

关键词： Cyber attacks

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RETRACTED ARTICLE: Industrial-IoT-hardware security-improvement using plan load optimization method in cloud

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International Journal of system Assurance engineering and Management 2023年第1-Sup期14卷 578-578页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Karnataka Bangalore India

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CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement

arXiv

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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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information-hierarchical Educational Process Management system

Information-hierarchical Educational Process Management Syst...

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International Conference on Actual Problems of Electronic Instrument engineering (APEIE)

作者： Firnafas Yusupov Iroda A. Khajieva Davronbek F. Yusupov Guzal Yu. Khujaniyazova Department of Software Engineering Candidate of Technical Sciences Associate Professor Urgench Branch of Tashkent University of Information Technologies Named After Muhammad al-Khwarizmi Urgench Uzbekistan Department of Social and Humanitarian Disciplines Candidate of Philology Associate Professor Urgench Branch of Tashkent University of Information Technologies Named After Muhammad al-Khwarizmi Urgench Uzbekistan First Vice-Rector for Spiritual and Educational Work Philosophy (PhD) in Pedagogy Urgench State Pedagogical Institute Urgench Uzbekistan Department of Social and Humanitarian Disciplines Tashkent University of Information Technologies Named After Muhammad al-Khwarizmi Urgench Uzbekistan

The implementation of the strategy of socioeconomic development of industry, ensuring the economic security of any country is associated with the need to significantly increase the efficiency of domestic production, the competitiveness of products and services, the potential reserves of which are hidden in the internal environment of manufacturing enterprises, as well as higher educational institutions. One of these potential reserves is the improvement of management systems for the training of highly qualified specialists. At the present stage, a higher educational institution operating in an unstable external environment and the influence of random internal disturbances that cause various economic problems on the lack of qualified specialists, there is an urgent need to sharply increase the efficiency of the educational process in higher educational institutions. Consider a controlled educational process as a complex intellectual complex, consisting of a number of specialties united into structural units of dean's offices, departments, laboratories, an information resource center (scientific and educational literature), as well as information and communication tools (local computer network of the university), learning technologies, monitoring the effectiveness of training. It is proposed to build a management system for educational processes in a higher education institution on the basis of interrelated submodels, into which it is divided into a four-level information-hierarchical system in the process of decomposition of the general model.

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Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

Gaussian Decay Function-based Improved Moment Matching for O...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Jiang, Hua Shi, Qiuxia Liu, Siying Zhang, Jiuying Zhao, Qinglin Hu, Bin Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering China Lanzhou University Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Chinese Academy of Sciences China Engineering Research Center of Open Source Software and Real-Time System Lanzhou University Ministry of Education China

ISBN: (纸本)9781665468190

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene. © 2022 IEEE.

关键词： Electroencephalography

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Investment models for enterprise architecture (Ea) and it architecture projects within the open innovation concept

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Journal of Open Innovation: Technology, Market, and Complexity 2021年第1期7卷 1-18页

作者： Ilin, Igor V. Levina, Anastasia I. Dubgorn, Alissa S. Abran, Alain Institute of Industrial Management Economics and Trade Peter the Great St. Petersburg Polytechnic University Saint Petersburg 195251 Russian Federation Department of Software Engineering and Information Technologies École de Technologie Supérieure Université du Québec Montréal H3C 1K3 QC Canada

information technologies (IT) architecture and infrastructure is a significant cost item, especially for enterprises with complex production infrastructure and equipment that require automated and digital devices to collect and process primary data on technological and production pro-cesses. Most investment models for enterprise‐wide development projects usually do not take into account the automation’s costs, including the design and implementation of information systems. The Enterprise Architecture (EA) paradigm has been proposed to bridge the gap between the business and the IT sector. The study aims to develop investment models for projects for the implementation and development of EA solutions, including IT architectures that eliminate the shortcomings of existing approaches. The research methodology is based on the analysis of published approaches to investment models for projects creating and developing EA, IT architectures with the identification of their advantages and limitations, and on the analysis of IT investment assessment practices in Russian infrastructure‐intensive companies. As a result, investment and appraisal models are proposed that have advantages associated with the ability to calculate the effect of an integrated approach to the implementation of IT solutions, a more accurate calculation of an investment project cost by taking into account the IT system’s cost, a reduction in the investment cycle of development and implementation of architectural solutions, including physical and IT component. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： COSMIC‐ISO 19761 Enterprise architecture (EA) Investment models IT architecture design

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NLP for Sentiment Analysis in Social Media Posts to Detect Suspicious Behaviour

NLP for Sentiment Analysis in Social Media Posts to Detect S...

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Intelligent Algorithms for Computational Intelligence systems (IACIS), International Conference on

作者： Abhishek Awasthi Mohammad Bdair A. Nandha Kumar Sanjog Thapa Nalini Devi S Bharathi Ramesh Kumar Software Developer Sporta Technology pvt Ltd Maharashtra India School of Architecture Computing & Engineering University of East London London United Kindom Department of EEE Bannari Amman Institute of Technology Sathyamangalam India Department of Information System and Analysis Lamar University Texas USA Department of Management Studies St. Joseph's Institute of Technology Chennai India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

In an era where social media is an almost omnipresent means of communication, online safety is crucial. Still, conventional sentiment analysis methods sometimes fall short of accurately spotting dubious conduct. A novel approach utilizing advanced Natural Language Processing (NLP) techniques, especially Deep Learning (DL) models like BERT (Bidirectional Encoder Representations from Transformers), is proposed to overcome these challenges. DL is used by the suggested method to identify nuanced attitudes in social media posts, which raises the accuracy of identifying suspicious activities. That is not the case with conventional systems that depend on basic algorithms. With detection rates of 88%, 85%, and 90% for hate speech, violent statements, and extreme opinions, respectively, the system accomplishes remarkable outcomes via meticulous data gathering, preprocessing, and model training. Through comparison, it outperforms existing systems in terms of F1 score (0.92), recall (0.91), precision (0.94), and accuracy (0.92). The proposed system effectively reduces potential risks and encourages a safer online environment by utilizing the effective analysis of sentiment in social media posts, therefore indicating a major advancement in online security.

关键词： Training Sentiment analysis Accuracy Social networking (online) Hate speech Bidirectional control Transformers Encoding Data models Security

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