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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Jooda, Janet O. Ogundokun, Roseline Oluwaseun Adedeji, Oluyinka T. Ajagbe, Sunday Adeola Adigun, Matthew O. Oke, Alice O. Omidiora, Elijah O. Redeemer's University Department of Computer Engineering Osun State Ede Nigeria Kaunas University of Technology Department of Multimedia Engineering Kaunas Lithuania Landmark University Department of Computer Science Omu Aran Nigeria Ladoke Akintola University of Technology Department of Information System Science Ogbomoso Nigeria University of Zululand Department of Computer Science Kwadlangezwa3886 South Africa First Technical University Department of Computer Engineering Ibadan200255 Nigeria Ladoke Akintola University of Technology Department of Computer Engineering Ogbomoso Nigeria

ISBN: (纸本)9798350327816

Using public fingerprint databases to validate the multi-instance fusion approach of a Multi-instance Biometric Authentication system (MBAS) for accurate authentication is a means of overcoming some of the limitations of a Unimodal Biometric system (UBS). Nevertheless, a significant portion of the web databases used for MBAS (Machine Learning-Based Automated systems) were obtained under controlled conditions, with the photos being curated to align with a certain algorithmic objective. The performance of biometric systems exhibits variability when the datasets used in the algorithms undergo modifications as a result of discrepancies in the settings under which the photos were obtained. In this study, a database containing multiple fingerprint instances was developed locally in an uncontrolled environment. The aim was to create a database containing numerous examples of fingerprints for the same person from one or multiple data collection sessions. The database was produced locally by acquiring six samples of ten fingerprint instances of 150 subjects in an uncontrolled environment using a Futronic fingerprint scanner. The created database and results will be helpful to all researchers in the biometrics field as a tool for improving multi-instance fusion methods and enabling an unbiased evaluation of algorithms. © 2023 IEEE.

关键词： Biometrics Fingerprint Image data acquisition Multiple instances Unimodal biometric system

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup 42

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Gu, Lin Huang, Junhao Huang, Shaoxing Zeng, Deze Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China China University of Geosciences School of Computer Science Wuhan China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350334142

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions. © 2023 IEEE.

关键词： Containers

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Artificial Bee Colony with Cuckoo Search for Solving Service Composition

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Intelligent Automation & Soft Computing 2023年第3期35卷 3385-3402页

作者： Fadl Dahan Abdulelah Alwabel Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Sciences Faculty of Computing and Information Technology AlturbahTaiz UniversityTaiz9674Yemen Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulﬁll compli-cated user demands according to speciﬁc Quality of Services(QoS)*** user requirements are formulated as a workﬂow consisting of a set of ***,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard *** work will introduce a hybrid Artiﬁcial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition *** ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of ***,the ABC suffers from a slow convergence ***,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local *** proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition *** addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s *** results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestﬁtness value,averageﬁtness value,and average execution time)overall datasets in 30 different ***,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

关键词： Cloud computing web service composition artiﬁcial bee colony cuckoo search

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Traffic Light Control Using Reinforcement Learning 2

Traffic Light Control Using Reinforcement Learning

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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Physiological Glucose Sensing in in-vitro 3D Spheroids using SERS Based Nanosensors 26

Physiological Glucose Sensing in in-vitro 3D Spheroids using...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Dey, Koyel Goudar, Venkanagouda S. Tseng, Fan-Gang Santra, Tuhin Subhra Engineering and System Science National Tsing Hua University Hsinchu Taiwan Department of Engineering Design Indian Institute of Technology Madras India

ISBN: (纸本)9781733419048

During growth, tumor spheroids develop gradients of glucose. In this work, we unveiled real time glucose gradient in tumor spheroid using Confocal Raman Spectroscopy. For sensor preparation, 50 nm silver nanoparticle is conjugated with Raman based dye, mercapto-phenylboronic acid(MPBA). The sensor approach a detection limit of 1 mM and dynamic range from 1-100 mM of glucose. MPBA -glucose bonding being reversible, helps for continuous measurement inside 3D microenvironment. Sensors are mixed with co-cultured cells(colon cancer and fibroblast) and seeded in 3D culture chip for spheroid formation. Raman measurement showed an increased glucose concentration from center to surface of spheroid. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Glucose

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Fault Detection Method Using Signal Processing Approaches to Design a Power system Network Control Method 3

Fault Detection Method Using Signal Processing Approaches to...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Mahia, Ram Niwash Prakash Mahela, Om National Institute of Technology Hamirpur Department of Electrical Engineering Himachal Pradesh India Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Power System Planning Division Jaipur India

ISBN: (纸本)9798350381931

This paper formulated a Stockwell-Hilbert composite fault detection method (SHCFDM) using current features extracted by the use of Stockwell transform (ST) and Hilbert transform (HT). A Stockwell-Hilbert composite fault index (SHCFI) is formulated which is effective to detect the faults incident on the power system network which includes fault with phase and ground (FWPG), fault with two phases (FW2P), fault with two phases and ground (FW2PG), fault with three phases (FW3P) and fault with three phases and ground (FW3PG). Fault sorting is done using number of faulty phases and a Stockwell-Hilbert ground fault index (SHGFI) which is computed by processing the zero sequence current (ZSC) using the SHCFDM. SHGFI identifies the ground with a fault. Proposed SHCFDM performs better compared to Discrete Wavelet transform (DWT) derived fault detection method. Study is performed on a two terminal power system transmission line (PSTL) connected on two power system bus (PSB) using MATLAB. © 2024 IEEE.

关键词： Fault detection

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Toward Precision Diagnosis of Otitis Media: Introducing A Novel EARnet-AR Model 2

Toward Precision Diagnosis of Otitis Media: Introducing A No...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Shakil, Rashiduzzaman Shamrat, F. M Javed Mehedi Sharmin, Sharmin Akter, Bonna Rubi, Maksuda Akter Dutta, Anik Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh University of Malaya Department of Computer System and Technology Kuala Lumpur Malaysia Daffodil International University Department of General Educational Development Dhaka Bangladesh Guru Nanak Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350344882

Otitis media represents inflammation or infection of the middle ear, often stemming from colds, sore throats, or respiratory infections. To address diagnostic challenges, this study introduces the EARnet-AR method, a novel advanced approach for automatically classifying otoscope tympanic membrane images. We employed 1100 otoscopic augmented images were utilized to conduct this research. To enhance the clarity and accuracy of otoscopic images, our research incorporated median filtering, an effective noise reduction technique, and gamma correction to adjust the image's luminance, ensuring a more consistent visual representation. In our initial evaluations, we tested the capabilities of five renowned pre-trained models using a comprehensive set of evaluation metrics, including accuracy, sensitivity, specificity, precision, f1-score, FPR, and FNR. The confusion matrices also detailed the model's capabilities in classifying each specific ear condition. Moreover, our proposed novel model EARnet-AR, enriched with Attention and RNN features, surpasses the other models, achieving training, testing and validation accuracies of 97.5%, 96.0%, and 96.5%, respectively. With such precise diagnostic tools, medical professionals can potentially achieve more timely and accurate treatments, enhancing patient care and outcomes in otitis media cases. © 2023 IEEE.

关键词： Diagnosis

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Visible Light-Based Synchronization Method for Acoustic Positioning in Underground Mines 11

Visible Light-Based Synchronization Method for Acoustic Posi...

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11th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2023

作者： Chavda, Mudra Zeeshan, Mohammad Nayek, Rajdip Malik, Shahid Centre for Sensors Instrumentation and Cyber Physical System Engineering Indian Institute of Technology Delhi Delhi India Indian Institute of Technology Delhi Department of Applied Mechanics Delhi India

ISBN: (纸本)9798350326147

The underground mining environment is one of the most hazardous for workers. Monitoring the miner's location precisely is essential for the miner's safety in this situation. Roof collapses, toxic gas releases and crashes of vehicles in mines are some of the factors that make this occupation more hazardous to human life than others. In order to determine the location of miners, however, a positioning system must be accurate and precise. The synchronization signal between transmitters and receivers is crucial in emergency situations. By using the synchronization signal, it is possible to prevent both data loss and misinterpretation. This article describes a method for combining visible light with an acoustic-based 2D positioning system. The developed proof-of-concept makes use of loudspeakers and a microphone as an acoustic transmitter and receiver, respectively. In addition, LED and LDR are used as transmitter and receiver for visible light. Using a timer circuit, synchronization has been achieved, and the transmitters are turned on sequentially using time division multiplexing. For the safety of miners, the system establishes the viability of using visible light as a synchronization signal and locates the miner with an error of ± 3 cm. © 2023 IEEE.

关键词： Transmitters

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Road Traffic Monitoring from Aerial Images Using Template Matching and Invariant Features

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Computers, Materials & Continua 2024年第3期78卷 3683-3701页

作者： Asifa Mehmood Qureshi Naif Al Mudawi Mohammed Alonazi Samia Allaoua Chelloug Jeongmin Park Department of Creative Technologies Air UniversityIslamabad46000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering Tech University of KoreaGyeonggi-do15073South Korea

Road traffic monitoring is an imperative topic widely discussed among *** used to monitor traffic frequently rely on cameras mounted on bridges or ***,aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger *** this end,different models have shown the ability to recognize and track ***,these methods are not mature enough to produce accurate results in complex road ***,this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with image *** extracted frames were converted to grayscale,followed by the application of a georeferencing algorithm to embed coordinate information into the *** masking technique eliminated irrelevant data and reduced the computational cost of the overall monitoring ***,Sobel edge detection combined with Canny edge detection and Hough line transform has been applied for noise *** preprocessing,the blob detection algorithm helped detect the *** of varying sizes have been detected by implementing a dynamic thresholding *** was done on the first image of every ***,to track vehicles,the model of each vehicle was made to find its matches in the succeeding images using the template matching *** further improve the tracking accuracy by incorporating motion information,Scale Invariant Feature Transform(SIFT)features have been used to find the best possible match among multiple *** accuracy rate of 87%for detection and 80%accuracy for tracking in the A1 Motorway Netherland dataset has been *** the Vehicle Aerial Imaging from Drone(VAID)dataset,an accuracy rate of 86%for detection and 78%accuracy for tracking has been achieved.

关键词： Unmanned Aerial Vehicles(UAV) aerial images dataset object detection object tracking data elimination template matching blob detection SIFT VAID

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