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5th IEEE International Conference on Telecommunications and Photonics, ICTP 2023

作者： Islam, Nazrul Islam Khan, Md. Saikat Hasan, Md. Nahid Yousuf, Mohammad Abu Institute of Information Technology Jahangirnagar University Savar Dhaka1342 Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9798350393477

Surface plasmon resonance (SPR) is a sensitive spectroscopic technique for measuring changes in the refractive index (RI) of a medium in contact with a sensor surface. Confinement loss computation is crucial for designing an SPR-based photonic crystal fiber (PCF) sensor as it directly impacts its performance and sensitivity. The study highlights the simple and fast-training feed-forward Artificial Neural Network (ANN) as a technique for calculating the confinement loss of an open-channels-based PCF-SPR sensor. Moreover, ANN demonstrates proficiency in generating precise predictions for the aforementioned optical properties within the conventional parameter range, encompassing wavelengths between 0.60 μm and 0.90 μm, as well as an analyte RI range spanning from 1.33 to 1.40. Additionally, this work demonstrates a higher speed in predicting output values for varying sensor structural parameters than direct numerical simulation techniques. This study signifies a noteworthy progression in the field through the utilization of ANN-driven optimization methods for integrated silicon photonics devices. This advancement leads to improved speed and accuracy in nredicting sensor outputs. © 2023 IEEE.

关键词： Machine learning

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Submarine Hunter: Efficient and Secure Multi-Type Unmanned Vehicles

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computers, Materials & Continua 2023年第7期76卷 573-589页

作者： Halah Hasan Mahmoud Marwan Kadhim Mohammed Al-Shammari Gehad Abdullah Amran Elsayed Tag eldin Ala R.Alareqi Nivin A.Ghamry Ehaa A.Lnajjar Esmail Almosharea Computer Center University of BaghdadBaghdad6751Iraq Department of Management Science and Engineering Dalian University of TechnologyLiaoningDalian116024China Department of Information Technology Faculty of Computer Sciences and Information Technology AL Razi UniversitySana’aYemen Faculty of Engineering and Technology Future University in EgyptNew Cairo11835Egypt Faculty of Engineering Mechanics Dalian University of TechnologyLiaoningDalian116024China Faculty of Computers and Artificial Intelligence Cairo UniversityGizaEgypt Department of Business Administration Dalian University of TechnologyLiaoningDalian116024China College of Software Engineering Dalian University of TechnologyChina

Utilizing artificial intelligence(AI)to protect smart coastal cities has become a novel vision for scientific and industrial *** of these AI technologies is using efficient and secure multi-environment Unmanned Vehicles(UVs)for anti-submarine *** study’s contribution is the early detection of a submarine assault employing hybrid environment UVs that are controlled using swarm optimization and secure the information in between UVs using a decentralized cybersecurity *** Dragonfly Algorithm is used for the orientation and clustering of the UVs in the optimization approach,and the Re-fragmentation strategy is used in the Network layer of the TCP/IP protocol as a cybersecurity *** research’s noteworthy findings demonstrate UVs’logistical capability to promptly detect the target and address the problem while securely keeping the drone’s geographical *** results suggest that detecting the submarine early increases the likelihood of averting a *** dragonfly strategy of sensing the position of the submersible and aggregating around it demonstrates the reliability of swarm intelligence in increasing access *** communication between Unmanned Aerial Vehicles(UAVs)improves the level of secrecy necessary for the *** swarm navigation is based on a peer-to-peer system,which allows each UAV to access information from its ***,in turn,helps the UAVs to determine the best route to take and to avoid collisions with other *** dragonfly strategy also increases the speed of the mission by minimizing the time spent finding the target.

关键词： Unmanned vehicles unmanned aerial vehicles unmanned underwater vehicles high altitude unmanned aerial vehicles anti-submarine warfare re-fragmentation dragonfly algorithm

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Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

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RelicCARD:Enhancing cultural relics exploration through semantics-based augmented reality tangible interaction design

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Visual Informatics 2024年第3期8卷 32-41页

作者： Tao Yu Shaoxuan Lai Wenjin Zhang Jun Cui Jun Tao School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhouChina Guangzhou Okay Information Technology Co. LtdGuangzhouChina Guangdong Provincial Institute of Cultural Relics&Archaeology GuangzhouChina

Cultural relics visualization brings digital archives of relics to broader audiences in many applications,such as education,historical research,and virtual ***,previous research mainly focused on modeling and rendering the *** enhancing accessibility,these techniques still provide limited ability to improve user *** this paper,we introduce RelicCARD,a semantics-based augmented reality(AR)tangible interaction design for exploring cultural *** design uses an easily available tangible interface to encourage the users to interact with a large collection of *** tangible interface allows users to explore,select,and arrange relics to form customized *** guide the design of the interface,we formalize a design space by connecting the semantics in relics,the tangible interaction patterns,and the exploration *** realize the design space as a tangible interactive prototype and examine its feasibility and effectiveness using multiple case studies and an expert ***,we discuss the findings in the evaluation and future directions to improve the design and implementation of the interactive design space.

关键词： Cultural relicsvisualization Interaction design Tangible interaction Augmented reality Immersive analytics

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Research on Facial Landmark Detection Algorithm Based on Improved Attention Mechanism

Research on Facial Landmark Detection Algorithm Based on Imp...

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2022 International Conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Cui, Xi-Shuo Shi, Tian-Wei Cui, Wen-Hua Tao, Ye School of Electronic and Information Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan114051 China

ISBN: (纸本)9781510662988

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task. © 2023 SPIE. All rights reserved.

关键词： Face recognition

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CNN-based device-free health monitoring and prediction system using WiFi signals

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International Journal of information technology (Singapore) 2022年第7期14卷 3725-3737页

作者： Kumar, Amit Singh, Shashwat Rawal, Vibhor Garg, Shivam Agrawal, Anand Yadav, Sonal Department of Computer Science and Engineering Indian Institute of Information Technology Kota Jaipur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Kota Jaipur India Department of Computer Science and Engineering National Institute of Technology Raipur India

Health monitoring has been a growing area of interest in recent times. It provides many beforehand benefits including early detection of potentially harmful illnesses. Till now, it has been achieved primarily by using devices such as wearable bands to monitor breathing rate and heart rate. Wearing and carrying devices make the person uncomfortable. In this paper, we introduce a deep learning based device-free way to monitor breathing rate and heart rate in different human positions to provide insight into whether the person is healthy. A convolutional neural network (CNN) model along with Gaussian naive bayes (GaussianNB) and support vector machine (SVM) is introduced and the device-free monitoring is implemented using WiFi signals. An individual in proximity causes a change in the channel state information (CSI) and the distortion is measured to provide corresponding vitals. The vitals give an insight into the health of the individual. The experimental results are quite encouraging with the evidence of superiority of SVM with CNN. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Channel state information (CSI) Convolution neural network (CNN) Support vector machine (SVM) WiFi signals

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Live Streaming Data Analysis using Distributed Stochastic Bi-LSTM Model

Live Streaming Data Analysis using Distributed Stochastic Bi...

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2022 International Conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Kavitha Nair, R. Mathew, Jimsha K Deepika, R. Sharanyaa, S. Therasa, M. Computer Science and Engineering Acharya Institute of Technology Bangalore India Acharya Institute of Technology Information Science and Engineering Bangalore India Computer Science and Engineering Sri Sairam Engineering College Chennai India Panimalar Engineering College Information Technology Chennai India Panimalar Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9781665479950

In the real world, currently there are more than millions bytes of social media data being created. With the help of modern computational capacity in addition to advanced research in the field of artificial intelligence, a great improvement is shown in natural language processing using deep learning techniques. These AI techniques including machine learning algorithms and deep learning algorithms make it possible to analyze the given tons of data. In addition to it, it also helps us to have a deep dive knowledge in people's mindset towards the current trend in media application. There is another problem of cyber bullying wherein many people start sharing and posting anonymous and fake information to make a significant impact by making riots just as fake heroes. By continuously monitoring these types of online riots will provide a clean and peaceful environment which in turn supports the government in a better way. This article focuses on performing sentiment analysis over a given real time social media data by categorizing it. The effective analysis of live streaming from social media applications is done with word2vec embedding method in natural language processing and trained using bi-directional LSTM model. The performance is compared with the LSTM model, and traditional machine learning techniques like naïve bayes and linear regression model and Bi-LSTM model outperforms the other techniques with 80%-85% accuracy with live streaming unconventional social media data. © 2022 IEEE.

关键词： Learning algorithms

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GSAGE2defect: An Improved Approach to Software Defect Prediction based on Inductive Graph Neural Network 35

GSAGE2defect: An Improved Approach to Software Defect Predic...

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35th International Conference on Software engineering and Knowledge engineering, SEKE 2023

作者： Ma, Ju Sun, Yi-Yang He, Peng Zeng, Zhang-Fan School of Computer Science and Information Engineering Hubei University Wuhan China School of Cyber Science and Technology Hubei University Wuhan China

Graph neural network is an effective deep learning framework for learning graph data. Existing research has introduced different variants of graph neural networks into the field of software defects and has achieved promising results. However, the graph neural network model based on the previous research is essentially transductive, is applied to a single fixed graph, and often ignores the direction and weight of the edges when modeling the network. In practice, software systems are dynamically evolving. Furthermore, in software network modeling, the direction and weight of edges are factors that are worth considering. Based on an inductive graph neural network, we proposed an improved defect prediction method named GSAGE2defect. We first constructed the class dependency network of the program and then used node2vec for embedding learning to automatically obtain the structural features of the network. Then we combined the learned structural features with traditional software code features to initialize the properties of nodes in the class dependency network. Next, we fed the dependency network to GraphSAGE for a deeper class representation. Finally, we evaluated the proposed method based on eight open-source programs and demonstrated that GSAGE2defect achieves an average improvement of 2.09%-26.69% over state-of-the-art methods in terms of F-measure. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Graph neural networks

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Channel-Aware Direct Localization Using One-Bit Quantized Measurements 32

Channel-Aware Direct Localization Using One-Bit Quantized Me...

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32nd European Signal Processing Conference, EUSIPCO 2024

作者： Zhang, Guoxin Liang, Yunfei Yi, Wei Ngo, Hien Quoc Matthaiou, Michail Varshney, Pramod K. School of Information and Communication Engineering University of Electronic Science and Technology of China China Queen’s University Belfast United Kingdom Department of Electrical Engineering and Computer Science Syracuse University United States

ISBN: (纸本)9789464593617

To address the constraints of limited transmission bandwidth and power consumption in wireless sensor networks (WSNs), this paper proposes a direct localization method using one-bit quantized measurements. Specifically, the received signal is quantized into its one bit version at the local sensor before being transmitted to the fusion center (FC) for target localization. Recognizing the imperfections of the wireless channel between the sensors and the FC, a channel-aware position estimator using one-bit quantized measurements is proposed. Furthermore, the Cramér-Rao lower bound (CRLB) of the proposed channel-aware estimator is also derived to theoretically assess the localization performance. Simulation results demonstrate that the proposed channel-aware position estimator approaches its theoretical performance bounds and is superior compared to that of the channel-unaware estimator. © 2024 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Cramer-Rao bounds

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IoT-Based Portable ECG and Heart Rate Monitoring System with Minimal Cost 1

IoT-Based Portable ECG and Heart Rate Monitoring System with...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Tonmoy, Achinta Brata Roy Sultan, Selim Bristy, Suparna Rani University of Wyoming Electrical Engineering and Computer Science Department LaramieWY United States Department of Electrical and Electronics Engineering Jain Bangalore India Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail Santosh1902 Bangladesh

ISBN: (纸本)9798350355611

Cardiovascular disease is a major cause of mortality globally, and early detection through electrocardiograms (ECG) is crucial for effective treatment. ECGs measure the heart's electrical impulses, enabling detection of various cardiac irregularities. In recent years, there has been a push to develop low-cost, IoT-based portable ECG monitoring systems to make healthcare more accessible. However, existing solutions are often constrained by limited Bluetooth connectivity (under 100 meters) or rely on expensive processors like Raspberry Pi, which restricts use in low-resource settings. This research proposes a novel IoT-based ECG monitoring system designed to be both affordable and efficient, overcoming these limitations with a minimal hardware setup. The system utilizes the ESP8266 module as a cost-effective microcontroller, enabling real-time ECG data transmission over the internet and allowing healthcare professionals to monitor patients remotely without range restrictions. At a cost of only ∃16, this system reduces expenses by 88.49% compared to similar existing models, offering a practical, scalable solution for telemedicine. The system architecture includes an ECG detecting node, IoT cloud platform, and a user-friendly interface, with the AD8232 sensor gathering ECG data processed by the ESP8266 and transmitted to a secure cloud platform(Ubidots). Authorized healthcare providers can access this data, with alerts for any detected irregularities, supporting timely intervention. The system's performance has been validated on multiple patients, achieving a 92% accuracy that aligns well with clinical ECG standards. This low-cost and precise IoT-based ECG monitoring technology signifies a significant progression in telemedicine, facilitating efficient remote monitoring and broadening access to vital healthcare services. © 2025 IEEE.

关键词： Telemedicine

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