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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ramzan, Aneeqa Mustafa, Rafay Kamran, Yashfa NUTECH Department of Electrical Engineering Islamabad Pakistan GIKI Faculty of Computer Science and Engg. Swabi Topi Pakistan IIUI Department of Electrical and Computer Engineering Pakistan

ISBN: (纸本)9798350349719

Providing adequate clinical and technical aid to blinds and visually impaired persons can be very challenging as it put financial strain on families due to the medical examination, treatment, surgical procedures and assistive devices expenses. The specialists to the mentioned individuals' ratio is low and exposure to ophthalmologists is limited. Either requirement of smartphone applications with need of internet connection or only direction oriented solutions but unable to avoid obstacles are the issues in existing market solutions. This paper proposes a novel technology of smart assistant aid for blind and visually impaired people (SSVIP) by developing a smart shoe prototype with sensors to detect obstacles, wet surfaces and ground vibrations, providing real-Time feedback to the user. If any of these are detected, the controller activates the buzzer and vibration motor, providing both audible alerts based on the detected sensor signal in order to alarm the wearer and tactile feedback respectively. SSVIP is then tested on 20 iterations after configuring and calibrating sensors and getting the optimized 20 units for ultrasonic sensor, 650 threshold for water sensor. Output accuracies of 87%, 80% and 83% for ultrasonic sensor, water sensor and vibration sensor resnectively and resulting overall 83% accuracy was acheived. © 2024 IEEE.

关键词： Ultrasonic sensors

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Performance Enhancement in Terms of BER and Data Rate of Next Generation Optical Networking System using Raman Amplifier 3

Performance Enhancement in Terms of BER and Data Rate of Nex...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Patel, Dip Faculty of Computer Science and Applications Charusat Changa Anand India Faculty of Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798331508456

This study explores the integration of Raman optical amplifiers in a Wavelength Division Multiplexing Passive Optical Network (WDM-PON) system to enhance high-speed data transmission. Traditional amplifiers like Erbium doped fiber amplifier (EDFA) and Semiconductor amplifier (SOA) face limitations in noise performance and gain efficiency over long distances. To address these challenges, a four-channel WDM-PON system was designed and simulated over a 50 km fiber link. Performance metrics, including Bit Error Rate (BER), Quality Factor (Q-Factor), and eye diagram characteristics, were analyzed. The results demonstrate that Raman amplification significantly reduces BER, improves Q-Factor, and enhances signal integrity, making it a robust solution for long-reach optical networks. This study contributes to the advancement of optical communication by showcasing the effectiveness of Raman amplifiers in supporting high-capacity, high-speed transmission. The main findings are Raman amplification is used for improving signal quality and network efficiency in next-generation optical systems and is better compared to other optical amplifiers. © 2025 IEEE.

关键词： Erbium doped fiber amplifiers

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Multi-Level Knowledge engineering Approach for Mapping Implicit Aspects to Explicit Aspects

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computers, Materials & Continua 2022年第2期70卷 3491-3509页

作者： Jibran Mir Azhar Mahmood Shaheen Khatoon Computer Science Department Shaheed Zulfikar Ali Bhutto Institute of Science and Technology(SZABIST)Faculty of Computing and Engineering SciencesIslamabadPakistan Department of Computer Science Capital University of Science&TechnologyFaculty of ComputingIslamabad College of Computer Science and Information Technology King Faisal UniversitySaudi Arabia

Aspect’s extraction is a critical task in aspect-based sentiment analysis,including explicit and implicit aspects *** extensive research has identified explicit aspects,little effort has been put forward on implicit aspects extraction due to the complexity of the ***,existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’dependency ***,in this paper,a multi-level knowledge engineering approach for identifying implicit movie aspects is *** proposed method first identifies explicit aspects using a variant of BiLSTM and CRF(Bidirectional Long Short Memory-Conditional Random Field),which serve as a memory to process dependent sentences to infer implicit *** can identify implicit aspects from four types of sentences,including independent and three types of dependent *** study is evaluated on a largemovie reviews dataset with 50k *** experimental results showed that the explicit aspect identification method achieved 89%F1-score and implicit aspect extraction methods achieved 76%*** addition,the proposed approach also performs better than the state-of-the-art techniques(NMFIAD andML-KB+)on the product review dataset,where it achieved 93%precision,92%recall,and 93%F1-score.

关键词： Movie NEs(named entities) aspects opinion words annotation process memory implicit aspects implicit aspects mapping word embedding and BiLSTM

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Fractal Measurement of Images Human Brain from MRI

Fractal Measurement of Images Human Brain from MRI

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Anarova, Shahzoda Jabbarov, Jamoliddin Uralova, Iroda Tashkent University of Information Technologies Department of Convergence of digital technologies Computer Engineering Faculty Tashkent Uzbekistan Department of Computer science and technology Intelligent systems and computer technologies Faculty Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9798400716928

This article is devoted to the determination of the fractal size of the damaged part of the human brain on the basis of images obtained from MRI (magnetic resonance imaging). There are various mathematical methods for determining the size of fractal structures, this article uses two variants of the cell counting method and describes the analysis of errors in determining the fractal size of the damaged part of the human brain. The article shows that the damaged part of the human brain has a fractal structure and that it has a fractal dimension. Fractal measurement has also been used to determine the size of the damaged part of the human brain, unlike common methods. In determining the fractal scale, cases of changes in human body organs were not taken into account. In the article, the determination of the size of the damaged part of the human brain is studied on the basis of mathematical formulas. Two different variants of the special cell counting method were used to calculate the fractal measurements, as well as the corresponding number of experimental results. In addition, these measurements were used to assess the extent to which patients suffered brain damage and to provide information on the practical application of these fractal measurements. © 2024 ACM.

关键词： Magnetic resonance imaging

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Unsupervised Few-Shot Adaptive Re-Learning for EEG-Based Motor Imagery Classification

Unsupervised Few-Shot Adaptive Re-Learning for EEG-Based Mot...

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

作者： Ng, Han Wei Guan, Cuntai Nanyang Technological University Faculty of Computer Science and Engineering 50 Nanyang Ave 639798 Singapore

ISBN: (纸本)9781665410205

To address the effect of both intra- and inter-subject variability in the EEG-based motor-imagery classification, adaptive schemes have been proposed whereby the pre-trained model. However, collection and labelling of additional target data is resource intensive. Furthermore, there may exist significant signal variations across time especially across multiple recording sessions which can result in model performance deterioration following adaptation. This introduces another challenging problem to training classifiers as they are typically unable to automatically determine which data is suitable for fine-tuning purposes, leading to some subjects facing performance drops even after adaptation. To address the data scarcity and variability in adaptive performance, we propose a novel machine-relearning Siamese architecture which utilizes few samples of unlabeled evaluation data to perform data efficient model re-learning. Machine re-learning optimally selects a sub-section of previously known data to update the model parameters. This is implemented via the use of comparative contrastive loss between the Gaussian distributions of target and known data to perform data selection. The highest subject-independent performance achieved an average (N=54) accuracy of 86.63% (±11.79%) without additional data and 88.23% (±10.36%) when utilizing a single unlabeled supplementary data for two-class motor imagery. The previous best accuracy on this dataset is 85.90% (±11.20%) using the best-known method in the literature. Therefore, significantly superior adaptation performance can be achieved while utilizing lesser amount of information from the target subject through reducing EEG feature variability in the training and fine-tuning sets. Codes may be found at: https://***/NgHanWei/EEG_Relearning © 2024 IEEE.

关键词： Contrastive Learning

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IoT-Based Healthcare Monitoring System 1

IoT-Based Healthcare Monitoring System

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hadi, Marwa Abdulrahman Al-Gaphari, Ghaleb Hamoud Al-Baltah, Ibrahim Ahmed Julian, Francisco Bacallao Al-Hadi, Ahmed Abdulrahman Department of Computer Science Faculty of Computer Science and IT Sana’a University Sana’a Yemen Department of Information Technology Faculty of Computer Science and IT Sana’a University Sana’a Yemen Faculty of Science and Engineering Al-Hikma University Sana’a Yemen Cardiac-Center Military Hospital Yemen Department of Science and Technologies Faculty of Engineering and Technology Mohamed Boudiaf University M'sila Algeria Department of Industrial Electronic and Automatic Engineering Faculty of Higher Polytechnic Technology Burgos University Burgos Spain

ISBN: (纸本)9798331533557

In an era when healthcare was becoming increasingly crucial, many developing nations, including Yemen, struggled to provide basic medical services. Nearly half of Yemen's population lacked access to adequate healthcare, with the situation being even more dire in rural and remote areas. While sectors such as industry, agriculture, and environmental science rapidly embraced technological advancements, healthcare systems in these regions lagged, with limited research addressing affordable and accessible solutions. This gap was further highlighted by the global healthcare crisis and the urgent need for innovative, cost-effective approaches. In response, this study outlined the creation and deployment of a cost-effective health monitoring system utilizing Internet of Things (IoT) technology to address these challenges. The system was designed to monitor vital signs, specifically heart rate (HR), using an Arduino Uno and ECG sensor, at a meager total cost of $27.25. It provided real-time monitoring for situations requiring immediate intervention. Power consumption tests demonstrated that the system operated efficiently, consuming between 1.061 and 1.35 watts, making it practical and affordable. Additionally, the potential integration of deep learning techniques promises to enhance the system's accuracy and efficiency. Although this study focused on IoT-based health monitoring, its potential extended beyond this, offering broader implications for future healthcare technology solutions. © 2024 IEEE.

关键词： Electronic health record

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Analysis of Sensing Matrix Design for Under-Sampling MRI 9

Analysis of Sensing Matrix Design for Under-Sampling MRI

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9th International Conference on Communication and Information Processing, ICCIP 2023

作者： Andryani, Nur afny catur Oey, Endra Laurent, Velix Arvino Nasya, Alexandra Husin, Husna Sarirah Computer Science Department School of Computer Science Bina Nusantara University Indonesia Computer Engineering Department Faculty of Engineering Bina Nusantara University Indonesia Taylor's University Malaysia

ISBN: (纸本)9798400708909

MRI currently is the most powerful medical diagnostic tomography system. It provides high resolution with high detail medical image. Its magnetization sensing is also free from radiation impact hence safe for the patient. However, the data acquisition process takes quite a long time with high-rate sampling. The sensing patient should stay still in the MRI sensing device for 20 to 40 minutes. It can affect claustrophobia for some patients. The high-rate sampling will also linearly affect the cost, which is considerably high. Hence, the research on enabling under-sampling MRI imaging has been raised. The main objective of under-sampling MRI Imaging is reducing the scanning time by under-sampling the data acquisition while at the same time the resulting qualified reconstructed image can be maintained. Compressive Sensing (CS) framework is one of the approaches that can be utilized on solving projection with less measurement. It can recover signals which have sparsity property. The signal is sparse if the signal being sensed has low information rate. Thus, the framework of Compressive Sensing matches under-sampling MRI Imaging. The sparsity requirement is closely related to the design of sensing matrix and dictionary on the Compressive Sensing framework. This paper presents analysis of two proposed sensing matrix designs for Under-sampling MRI Imaging: square sensing matrix and sorting-based sensing matrix designs. The proposed sensing matrix design is intended to appropriately sample the data measurement on certain sampling rate in frequency domain to maintain the quality of the reconstructed MRI image closely to the ground truth (full sampling MRI Imaging). The simulations show that both proposed sensing matrix designs are able to conduct under-sampling MRI Imaging for at least at 40% sampling rate with average of PSNR 27.72 and 33.07 respectively subject to the ground truth. © 2023 Copyright held by the owner/author(s)

关键词： Medical imaging

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Even Search in a Promising Region for Constrained Multi-Objective Optimization

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 474-486页

作者： Fei Ming Wenyin Gong Yaochu Jin School of Computer Science China University of GeosciencesWuhan 430074China IEEE Faculty of Technology Bielefeld UniversityNorth Rhine-Westphalia 33619Germany School of Engineering Westlake UniversityHangzhou 310030China

In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general ***, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs.

关键词： Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems

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A Systematic Review of Human Fall Detection Algorithms Using Machine Learning 2nd

A Systematic Review of Human Fall Detection Algorithms Using...

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2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Singh, Divya Gupta, Meenu Kumar, Rakesh Obaid, Ahmed J. Department of Computer Science and Engineering Chandigarh University Punjab India Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq

ISBN: (纸本)9789819909681

In the past century, due to healthy lifestyle of people, they suffered from very less disease. As era changes, the life style of people also changes. People are taking unhealthy food in today’s era owing to a busy schedule, and they are suffering from various diseases such as human falls. The most common causes of human falls among the elderly include high (low) blood pressure, arthritis, osteoarthritis, coronary heart disease, dementia, depression, and stroke. As a result, intervening early in the event of a fall could cut morbidity and death dramatically. In recent years, the machine learning-based human fall model has made significant progress. A human fall detection model assists the elderly by recognizing the fall and saving their lives. Machine learning (ML)-based models have now become an important research subject because of its capacity to proactively assist the elderly. In this proposed study, the most recent research trends in fall detection system using the machine learning techniques have been analyzed. Moreover, the analysis focuses on the shortcomings associated with ML-based tactics in order to conduct deeper research. The goal of the study is to find out how various type of machine learning algorithms are detecting fall effectively. It is a comprehensive review of existing fall detection models and trends. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fall detection

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Providing an Intelligent Hybrid Routing Method in Wireless Sensor Networks 23th

Providing an Intelligent Hybrid Routing Method in Wireless S...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Hosseinabadi, Ali Asghar Rahmani Mirkamali, Seyedsaeid Hajiabadi, Mahdi Rohani Abraham, Ajith Department of Computer Science University of Regina Regina Canada Department of Computer Engineering and IT Payame Noor University Tehran Iran Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Qazvin Iran School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

ISBN: (纸本)9783031647758

Optimal energy consumption in Wireless Sensor Networks (WSNs) is important. Previous research has shown that by organizing network nodes into a number of clusters, one can achieve greater energy efficiency leading to longer network life. The WSN is made up of a very large number of sensor nodes that communicate with each other to perform specific tasks. These nodes have limited energy, processing power, and memory. Because the lifetime of the network depends on the brightness of the nodes, the energy consumption of each node is very important. Therefore, we need to find solutions to reduce the energy consumption of the nodes. One way to reduce energy consumption is clustering, many algorithms have been introduced in this field. In this paper, we optimize Particle Swarm Optimization (PSO) algorithm based on fuzzy logic and PSO algorithm. The fitting criterion will be based on the minimum energy consumed by the nodes of the network during each operation period, It leads to a balance in the energy consumption of the headers and, as a consequence, a lengthening of the life of the network. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Fuzzy logic

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