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MV-DUO: multi-variate discrete unified optimization for psychological vital assessments

Neural Computing and Applications 2024年第31期36卷 19777-19793页

作者： Pethuraj, Mohamed Shakeel Burhanuddin, M.A. Dzakiyullah, Nur Rachman Department of Information System Faculty of Computer and Engineering Universitas Alma Ata Yogyakarta Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Melaka Malaysia

Psychological vital assessments are required for monitoring health conditions and observing body reactions toward diseases and medications. Wearable sensors play a vital role in sensing body vitals and presenting them as signals for computer-based analysis. The problem relies on the signal decoding due to its input stream that turns out to be discrete/ continuous. Therefore for addressing the above specific issue, this article introduces MV-DUO (Multi-Variate Discrete Unified Optimization) method. This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-point artificial ecosystem-based optimization for identifying discrete sequences collaborated with continuous signals. The single-point reference is grouped based on the maximum continuity fitness observed under various sensing intervals. In this process, the non-grouped sequences are identified as unhealthy or discrete for which additional detection training and classification are required. Considerably the changes between successive sensing intervals are used for variations detection from unified high-fitness groups. Those grouped instances are used for training new vital changes observed at distinct intervals. This improves detection accuracy under controlled errors. For the varying sensing intervals, the proposed method achieves 14.13% high accuracy, 8.29% high grouping rate, 10.77% less error, and 10.07% less detection time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computer aided analysis

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DiriNet:An Estimation Network for Spectral Response Function and Point Spread Function

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Journal of Beijing Institute of technology 2024年第4期33卷 287-297页

作者： Ting Hu Siyuan Cheng Chang Liu Beijing Key Laboratory of Computational Intelligence and Intelligent System Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Space Star Technology Co. LtdBeijing 100086China Research Institute of Intelligent Wire-less Communication Network Technology Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China

Hyper-and multi-spectral image fusion is an important technology to produce hyper-spectral and hyper-resolution images,which always depends on the spectral response function andthe point spread ***,few works have been payed on the estimation of the two degra-dation *** learn the two functions from image pairs to be fused,we propose a Dirichletnetwork,where both functions are properly ***,the spatial response function isconstrained with positivity,while the Dirichlet distribution along with a total variation is imposedon the point spread *** the best of our knowledge,the neural network and the Dirichlet regularization are exclusively investigated,for the first time,to estimate the degradation *** image degradation and fusion experiments demonstrate the effectiveness and superiority of theproposed Dirichlet network.

关键词： Dirichlet network point spread function spectral response function hyper-spectralimage multi-spectral image

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Detection of Student Sleepiness Using U-Net and Conv-LSTM Methods to Improve Learning Quality 1

Detection of Student Sleepiness Using U-Net and Conv-LSTM Me...

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1st International Conference on technology, Engineering, and Computing Applications, ICTECA 2023

作者： Rahman, Ben Andryana, Septi Gunaryati, Aris Mantoro, Teddy Faculty of Information and Communication Technology Universitas Nasional Department of Information System Jakarta Indonesia Faculty of Information and Communication Technology Universitas Nasional Department of Information Technology Jakarta Indonesia Sampoerna University Jakarta Selatan Faculty of Science and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

In the context of monitoring student engagement during learning sessions, the ability to accurately detect sleepiness can be a valuable asset. Early detection of sleepiness is critical to evaluate the need for intervention to ensure effective learning outcomes. Prolonged student sleepiness can negatively impact learning effectiveness and may result in academic challenges. This research used a semantic segmentation network to monitor and detect the level of sleepiness in students during learning sessions by modifying the U-Net architecture, which is enhanced with Convolutional Long Short-Term Memory (Conv-LSTM) blocks. Conv-LSTM blocks enable efficient feature sharing between network layers and improve accuracy in detecting sleepiness. In this study, experiments were carried out and the proposed method for monitoring students in the classroom was evaluated. The results of this research showed that the percentage of students who made movements detected in the video and extracted in image detection was 92% accurate and not sleepy. © 2023 IEEE.

关键词： Long short-term memory

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I-Know: Revolutionizing Knowledge Management with Interactive Quizzes 1

I-Know: Revolutionizing Knowledge Management with Interactiv...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Zulkifli, Zahidah Halim, Muhammad Darwish Bin Abdul Shaffei, Arief Iskandar Bin Alsaiari, Nisrin International Islamic University Malaysia Kulliyyah of Information and Communication Technology Department of Information System Malaysia Arab Open University Faculty of Computer Studies Saudi Arabia

ISBN: (纸本)9798350373042

This project focuses on the development of a website system designed to facilitate knowledge acquisition for students enrolled in the Kulliyyah of information communication and technology (KICT) at the International Islamic University Malaysia (IIUM). The aim is to alleviate the information overload experienced by new intake students during the Taaruf week (orientation week). Despite its establishment in 2010, KICT faces a persistent challenge where new students remain unaware of crucial system details, including rules, regulations, and academic matters. To address this issue, the project endeavors to provide a comprehensive platform enabling new students to access pertinent information and knowledge about KICT. An analysis of seven similar existing systems has been conducted, highlighting their main functions, advantages, and disadvantages. The system Development Life Cycle methodology has been selected for its practicality in guiding the project through the phases of Final Year Project (FYP) 1 and FYP2. Data collection involved surveying 30 KICT students to identify challenges faced by new students in obtaining the necessary information. Results underscore the urgent need for a technically guided application to enhance student understanding of KICT. The logical design of the app encompasses a Use Case Diagram, Use Case Narrative, Activity Diagram, Interface Flow, Low-Fidelity Design, and Interface Flow. The 'i-know' system has been developed using PHP, HTML, CSS, and Javascript, with connectivity to MySQL through phpMyAdmin for user authentication. The User Acceptance Test (UAT) was conducted during implementation, following beta testing. The paper concludes with insights into the system's effectiveness and its potential to address the identified challenges faced by new KICT students. © 2024 IEEE.

关键词： Knowledge management

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Three-Level Optimized Pulse Patterns with Zero Common-Mode Voltage 17

Three-Level Optimized Pulse Patterns with Zero Common-Mode V...

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17th IEEE International Conference on Compatibility, Power Electronics and Power Engineering, CPE-POWERENG 2023

作者： Koukoula, Isavella Karamanakos, Petros Geyer, Tobias Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland ABB System Drives Turgi Switzerland

ISBN: (纸本)9798350300048

This paper investigates the computation of three-level optimized pulse patterns (OPPs) with zero common-mode voltage (CMV). To do so, a set of linear constraints that guarantee zero CMV is derived, and a systematic way of implementing it is presented. Moreover, to alleviate the increased computational demands of the associated optimization problem, a method that can reduce the computational time of three-level OPPs with zero CMV by up to 99% is proposed. Finally, to mitigate the increased current harmonics due to the CMV elimination, the symmetry requirements of the OPPs are relaxed. In doing so, as shown by the presented numerical results, the current quality can be improved, but alas, only marginally and over a limited range of modulation indices. © 2023 IEEE.

关键词： Pulse width modulation

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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A Knowledge-Based system to Predict Crime from Criminal Records in the Case of Hossana Police Commission 5

A Knowledge-Based System to Predict Crime from Criminal Reco...

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5th International Conference on information and communication technology for Development for Africa, ICT4DA 2023

作者： Wubineh, Betelhem Zewdu Metekiya, Biniyam Yirsaw Wroclaw University of Science and Technology Faculty of Information and Communication Technology Wroclaw Poland School of Computing and Informatics Wachemo University Department of Information Technology Hossana Ethiopia College of Informatics Department of Information System University of Gondar Gondar Ethiopia

ISBN: (纸本)9798350328486

Crime is a complex social phenomenon, and its cost continues to rise over time. Police have collected a large amount of data on criminals every day for different crimes. However, extracting valuable information from these data takes time and effort for the law enforcement organization at hand. Therefore, effective crime prevention strategies and policies are necessary to prevent crime. Criminology is one of the most important areas for data mining. The purpose of this study is to extract useful crime patterns from past data and develop a knowledge-based system to prevent crime. Different criminals commit different crimes, but we do not know which criminal commits which crime. For this purpose, the 910 criminal records are used to train and test the model. To predict a crime, the classification technique and the J48 decision tree, random forest (RF), PART, and ensemble algorithms are used. As a result, the algorithm scored an accuracy of 75.38%, 80.4%,73.7%, and 81.3% in the J48, RF, PART, and ensemble methods, respectively, in a 10-fold cross-validation. Additionally, the offender's job is the determining factor that causes and predicts crimes. Finally, we integrate the rules extracted from a data mining task into a knowledge base system using PROLOG, which is used to make decisions. © 2023 IEEE.

关键词： Forecasting

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Cocktail Party Effect Using Parallel Intra and Inter Self-attention 3rd

Cocktail Party Effect Using Parallel Intra and Inter Self-at...

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3rd International Conference on Advances in information and communication technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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Integration of Li-Fi and IoT for Sixth-Generation (6G) communication systems

Integration of Li-Fi and IoT for Sixth-Generation (6G) Commu...

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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： Ajay Sharma Peter Albert Xuereb Lalit Garg Computer Information System Faculty of Information and Communication Technology University of Malta Msida MALTA

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

Light Fidelity (LiFi) to enhance future communication solutions along with IoT connectivity is a must to design the Sixth Generation (6G) networks. LiFi, which uses visible light for data communication, offers several advantages over RF-based technologies, including a high signaling rate, better security, and reduced interference. This paper aims at analyzing LiFi's possibilities of integrating with IoT to solve the problems of poor coverage and sensitivity to ambient light and interconnections with existing RF networks. The proposed network structure to integrate both LiFi and RF does provide a path for smooth transition between LiFi and RF to maintain the connection and performance. New modulation techniques and enhanced security schemes are incorporated to overcome technical constraints and gain better communication with security. In projecting how LiFi can enhance green technology, the study shows that through using energy-efficient LED, LiFi is capable of complementing green technology for energy-efficient communication of IoT via 6G networks.

关键词： 6G mobile communication Radio frequency Electric potential Protocols Green products Modulation Interference Light fidelity Energy efficiency Security

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Piano Concerto Source Separation Based on Speech Separation and Channel Attention

Piano Concerto Source Separation Based on Speech Separation ...

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2024 International Conference on Culture-Oriented Science and technology, CoST 2024

作者： Liu, Jingyu Zhou, JingJing He, Xinyan He, Wei Communication University of China State Key Laboratory of Media Convergence and Communication 100024 China Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Ministry of Culture and Tourism 100024 China Communication University of China School of Information and Communication Engineering 100024 China Communication University of China School of Faculty of International Media 100024 China

ISBN: (纸本)9798350380347

The Mossformer model excels in speech separation but has not been effectively applied to music source separation. Music sources have complex characteristics and higher sampling rates, making separation tasks more challenging. We addressed a rarely explored task of separating piano concerto recordings into individual piano and orchestral tracks. This process involves intricate coordination between the piano and orchestra, creating highly complex audio signals in both time and frequency domains. Our main contributions include: (1) adapting the speech separation model for the novel task of piano concerto source separation, constructing and processing a specialized dataset.(2) introducing channel attention in the separation module to dynamically adjust feature focus based on instrument characteristics, enhancing key features. Experiments on the Piano Concerto Dataset (PCD) showed improved separation performance, with a 0.22dB average Signal-to-Distortion Ratio (SDR) increase over the baseline model. © 2024 IEEE.

关键词： Source separation

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