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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Andrews, Leo John Baptist Alagappan, Annamalai Sarathkumar, D. Fathima, M. Venkatachary, Sampath Kumar Rajeshkanna, R. Raj, Raymon Antony Botho University Faculty of Engineering and Technology Department of Information Technology Gaborone Botswana Botho University Faculty of Engineering and Technology Department of Network and Infrastructure Management Gaborone Botswana Kongu Engineering College Department of Electrical and Electronics Engineering Tamilnadu Erode District638 060 India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India Grant Thornton Botswana Plot 50370 Acument Park Fair Grounds Gaborone Botswana Thamirabarani Engineering College Department of Eee Tamil Nadu Tirunelveli627 358 India

ISBN: (纸本)9798350348460

Internet communication is used as a mode of engagement by businesses, organizations, and even nations in a variety of contexts. As a result, both the quantity of data produced and the importance of the information that can be derived from it are growing. Multiple safeguards are required in order to ensure that the accessibility, integrity, and confidentiality of company information and assets are maintained. When it comes to protecting the privacy of a company and its reputation, adopting countermeasures and minimizing the damage caused by irreversible cyberattacks are both very crucial. It is possible to utilize both active and passive attack tactics, as well as social engineering tests, to evaluate the weaknesses of a corporation's cyber security. The identification and ranking of vulnerabilities both play an active part in the process of raising the level of corporate awareness, as well as closing shortfalls and enhancing measures. In this investigation, ethics committee reports and confidentiality agreements were followed in order to do a cyber security vulnerability assessment on seven separate organizations. These organizations represented the governmental sector, the corporate sector, and the civil society. In order to evaluate the effectiveness of the firms' cyber security infrastructures, security scans such as port, web, application-based system and network penetration tests, distributed denial of service attacks, and social engineering were carried out. The Common Vulnerability Scoring System (CVSS) was used in order to change the base score of each institution and to identify the vulnerabilities that were present. Enterprise Networks, Cyber Security Analysis, Cyber Attacks, Penetration, and Vulnerability are some of the Keywords that can be found in this article. © 2024 IEEE.

关键词： cyber analysis Introduction cyber security cyber-attacks vulnerabilities

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Dispersion compensating fiber with Brillouin fiber laser generation

Dispersion compensating fiber with Brillouin fiber laser gen...

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2023 Al-Sadiq International Conference on Communication and information technology, AICCIT 2023

作者： Ali, Hasanain Naser Abd Harun, Sulaiman Wadi Arsad, Norhana Abdali, Taj-Aldeen Naser Al-Khaleefa, Ahmed Salih Hassan, Mustafa H Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Electrical Electronic and Systems Engineering Bangi Malaysia Faculty of Engineering University of Malaya Photonics Engineering Laboratory Department of Electrical Engineering Kuala Lumpur Malaysia University of Misan Department of Electronic Computer Center Misan Iraq University of Misan Faculty of Education Department of Physics Misan Iraq Imam Ja'afar Al-Sadiq University College of Information Technology Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9798350341881

Short linewidth light lasers are essential for a wide range of applications, including quantum computing, spectroscopy, and sensing. Stimulated Brillouin scattering is an intriguing method for achieving extremely coherent light laser output. The demonstration of an unstable pulsed Brillouin erbium fiber laser (BFL) operating at kHz is shown here. The BFL works at 1550.1 nm, which is 0.09 nm higher than the Brillouin pump wavelength since the dispersion compensating fiber was pumped over the 24.77 mW threshold. It has an extreme power of 0.7 dBm, a side-mode suppression ratio of 29 dB, and a pump power of 59.12 mW at 980 nm. Due to the intrinsic instability in relaxation oscillation, which generates the nonlinear self-pulsing mechanism in the BEFL cavity, the BEFL produced a pulse train running at 200.9 kHz with a 24.77 mW pump. The pulse frequency was raised to 3.02 MHz. The pump power has been increased to 59.12 mW. Yet, as the pump power was raised further, more than one pulse was produced. This is the first example of generating kHz pulses in a BFL cavity. The laser system is straightforward, small, and all-fiber. These are the attempts of experimental work to realize the BFL with Q-switched multi-wavelength output using a specific length of DCF to generate the pulses. © 2023 IEEE.

关键词： Fiber lasers

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A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

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computers, Materials & Continua 2022年第7期72卷 73-92页

作者： Farman Ali Sadia Khan Arbab Waseem Abbas Babar Shah Tariq Hussain Dongho Song Shaker EI-Sappagh Jaiteg Singh Department of Software Sejong UniversitySeoul05006Korea Institute of Computer Sciences&Information Technology The University of AgriculturePeshawar25130Pakistan College of Technological Innovation Zayed UniversityDubai19282UAE Department of Software Korea Aerospace UniversitySeoul10540Korea Faculty of Computer Science and Engineering Galala UniversitySuez435611Egypt Information Systems Department Faculty of Computers and Artificial IntelligenceBenha UniversityBanha13518Egypt Chitkara University Institute of Engineering&Technology Chitkara UniversityRajpura140401India

Medical Image Analysis(MIA)is one of the active research areas in computer vision,where brain tumor detection is the most investigated domain among researchers due to its deadly *** tumor detection in magnetic resonance imaging(MRI)assists radiologists for better analysis about the exact size and location of the ***,the existing systems may not efficiently classify the human brain tumors with significantly higher *** addition,smart and easily implementable approaches are unavailable in 2D and 3D medical images,which is the main problem in detecting the *** this paper,we investigate various deep learning models for the detection and localization of the tumor in MRI.A novel twotier framework is proposed where the first tire classifies normal and tumor MRI followed by tumor regions localization in the second ***,in this paper,we introduce a well-annotated dataset comprised of tumor and normal *** experimental results demonstrate the effectiveness of the proposed framework by achieving 97%accuracy using GoogLeNet on the proposed dataset for classification and 83%for localization tasks after finetuning the pre-trained you only look once(YOLO)v3 model.

关键词： Tumor localization MRI Image classification GoogLeNet YOLOv3

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Impact of diameter on D-shaped POF sensors for salt detection

Impact of diameter on D-shaped POF sensors for salt detectio...

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International Laser technology and Optics Symposium 2024, iLATOS2024

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. bte Saidin, Norazlina Noor, Ahmad Shukri Muhammad Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala lumpur53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Instrumentation and Control Engineering Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia

This study investigates the relationship between the diameter of a D-shaped plastic optical fiber (POF) sensor and its optical response as a function of varying salt concentrations. A simple and efficient dry etching polishing technique was employed to create D-shaped POF sensors with a range of diameters. All the fabricated sensors were carefully developed to maintain a consistent length of 20 cm with a sensing region of 1cm. To evaluate the optical intensity response, each sensor, with its respective diameter, was examined with the salt concentration ranging from (0 to 5%) examination utilizing a spectrometer. The sensor response was characterized by quantifying the variations observed in the intensity of the optical signal propagating through the sensing region. The sensors having a diameter of 500 μm, 700 μm, and 900 μm had a sensitivity of 149 A.U/%salt, 66 A.U/%salt, and 11.2 A.U/%salt respectively. The sensitivity of the 500 μm diameter sensor increased 13 times approximately as compared to the sensor having diameter of 900 μm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Dry etching

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Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

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computers, Materials & Continua 2022年第7期72卷 871-886页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical *** machine learning algorithms were developed to detect PD ***,these ML methods are lack in generalization and reduced classification performance due to subject *** overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech *** proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden *** consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech *** on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are *** PD datasets is evaluated under 10 fold cross validation without sample *** proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation *** evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LSTM and optimization

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Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration

Real-Time Data-Driven Maintenance Logistics: A Public-Privat...

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2024 Commit2Data, C2D 2024

作者： van Jaarsveld, Willem Bliek, Laurens de Weerdt, Mathijs Kapodistria, Stella Pronk, Verus Verleijsdonk, Peter Voorberg, Simon Akçay, Alp da Costa, Paulo Eshuis, Rik Kaymak, Uzay van Houtum, Geert-Jan Verwer, Sicco Zhang, Yingqian Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands Philips Research Laboratories Eindhoven Netherlands Department of Information Systems Supply Chain Management and Decision Analysis NEOMA Business School Rouen France Etsy Inc. Dublin Ireland Jheronimus Academy of Data Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9783959773515

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice. © Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang.

关键词： Scheduled maintenance

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Mining the Chatbot Brain to Improve COVID-19 Bot Response Accuracy

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computers, Materials & Continua 2022年第2期70卷 2619-2638页

作者： Mukhtar Ghaleb Yahya Almurtadha Fahad Algarni Monir Abdullah Emad Felemban Ali M.Alsharafi Mohamed Othman Khaled Ghilan Department of Information Systems College of Sciences and ArtsUniversity of BishaAl Namas67392Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia Computer Engineering Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Communication Technology and Network Universiti Putra MalaysiaSelangor43400Malaysia Faculty of Public Health and Tropical Medicine Jazan UniversityJazan45142Saudi Arabia

People often communicate with auto-answering tools such as conversational agents due to their 24/7 availability and unbiased ***,chatbots are normally designed for specific purposes and areas of experience and cannot answer questions outside their *** employ Natural Language Understanding(NLU)to infer their *** is a need for a chatbot that can learn from inquiries and expand its area of experience with *** chatbot must be able to build profiles representing intended topics in a similar way to the human brain for fast *** study proposes a methodology to enhance a chatbot’s brain functionality by clustering available knowledge bases on sets of related themes and building representative *** used a COVID-19 information dataset to evaluate the proposed *** pandemic has been accompanied by an“infodemic”of fake *** chatbot was evaluated by a medical doctor and a public trial of 308 real *** obtained and statistically analyzed tomeasure effectiveness,efficiency,and satisfaction as described by the ISO9214 *** proposed COVID-19 chatbot system relieves doctors from answering *** provide an example of the use of technology to handle an infodemic.

关键词： Machine learning text classification e-health chatbot COVID-19 awareness natural language understanding

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Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

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computer systems Science & engineering 2023年第8期46卷 1901-1916页

作者： Mohammad Yamin Sarah Basahel Saleh Bajaba Mona Abusurrah ELaxmi Lydia Department of Management Information Systems Faculty of Economics and Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Business Administration King Abdulaziz UniversityP.O.Box 80201Jeddah21589Saudi Arabia Department of Management Information Systems College of Business AdministrationTaibah UniversityAl-MadinahSaudi Arabia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajam 532127India

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed *** detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus *** article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus *** proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making *** the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus ***,the DLLSHDM-DR applies HybridNet for producing a collection of feature *** retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our *** assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS *** comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

关键词： Decision making healthcare sector deep learning diabetic retinopathy emperor penguin optimizer

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Edge-Cloud Computing systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay 2

Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Ca...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Narayana Tinnaluri, V.S. Vyankatesh Ghamande, Manasi Singh, Shashank Kuchipudi, Ramu Dattatraya Bhosale, Minakshi Dharani, R. Koneru Lakshmaiah Education Foundation Department of Computer Science Andhra Pradesh Vijayawada India Vishwakarma Institute of Information Technology Engineering and Applied Sciences Pune411037 India Integral University Lucknow Department of Computer Science and Engineering Uttar Pradesh India Chaitanya Bharathi Institute of Technology Department of Information Technology Telangana Hyderabad India Faculty of Mca Yashoda Technical Campus Satara Maharashtra Wadhe India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Tamilnadu Karur India

ISBN: (纸本)9798350346640

Mobile terminals' limited processing power and memory make it challenging to meet the needs of increasingly complex applications like autonomous vehicles and augmented reality. That's why there's been a rise in the demand for edge cloud computing power from endpoint gadgets. Due to its adaptability and proximity to the user, an unmanned aerial vehicle (UAV) may be used to support mobile edge computing (MEC) via job offloading, potentially relieving strain on edge clouds. Due to limitations in processing power and battery life, Unmanned Aerial Vehicles (UAV) devices face a huge issue with the rise of Applications and services for mobile devices that can't afford to be delayed and that need a lot of processing power. Task offloading is one way that mobile cloud computing may help you get around these restrictions. The biggest difficulties with this paradigm, however, are the significant latency and security concerns. The edge-cloud computing model was then proposed and has since seen widespread adoption as a means of dealing with these complications. However, the present task offloading models allow UAVs to perform their heavy jobs at the linked edge server, leading to unnecessary loads owing to the huge amount of UAVs and, in turn, increasing the latency. For this reason, this study has presented a delay-optimal task offloading strategy for multi-tier edge-cloud computing with many users. In this research, a system model has been proposed to assist an intelligent and perceptive agent in determining the best computational offloading strategy, minimizing both the time it takes to complete a job and the amount of power it needs to do so. The efficacy of an agent was shown, and simulation results revealed that its adoption may greatly cut delay and energy usage. © 2023 IEEE.

关键词： Mobile edge computing

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dependencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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