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International Conference on Advanced Learning Technologies (ICALT)

作者： Seyed Reza Shahamiri Department of Electrical Computer and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand

Affecting approximately 1 in 60 individuals, Autism spectrum disorder (ASD) is a lifelong developmental condition associated with substantial healthcare costs and time-consuming diagnosis, whereas early detection of ASD traits may help limit further development of the condition. To help speed up ASD diagnosis, screening approaches have been introduced, but they are not widely used due to their reliability issues since these approaches use basic scoring functions to identify traits of autism rather than intelligently learning autistic indicators from historical cases and controls. Autism Artificial Intelligence is one such system that visualizes autistic behavioral indicators and leverages a Convolutional Neural Network to learn the behavioral patterns associated with autism using historical data collected from more than 6000 ASD cases and control. Autism AI has been operational since August 2018, and more than 10,000 individuals have benefited from this platform. In this paper, after briefly explaining the system, we will analyze how well it has performed compared to AQ-10 and Q-CHAT-10 screening methods and benchmark it against the diagnosis data we have collected.

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Load Balancing Framework for Cross-Region Tasks in Cloud Computing

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computers, Materials & Continua 2022年第1期70卷 1479-1490页

作者： Jaleel Nazir Muhammad Waseem Iqbal Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Nadia Tabassum Department of Software Engineering The Superior CollegeLahorePakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer Science University of Veterinary and Animal SciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Virtual University of PakistanLahore54000Pakistan

Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between *** maximize various performance parameters in cloud computing,researchers suggested various load balancing *** store and access data and services provided by the different service providers through the network over different regions,cloud computing is one of the latest technology systems for both end-users and service *** volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been *** of the cloud are looking for services that are intelligent,and,can balance the traffic load by service providers,resulting in seamless and uninterrupted *** types of algorithms and techniques are available that can manage the load balancing in the cloud *** this paper,a newly proposed method for load balancing in cloud computing at the database level is *** database cloud services are frequently employed by companies of all sizes,for application development and business *** balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource *** balancing is the process of distributing the load on various nodes to ensure that no single node is *** avoid the nodes from being overloaded,the load balancer divides an equal amount of computing time to all *** results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

关键词： Load balancing performance database region virtualization

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Ranking Center-based NSGA-II

Ranking Center-based NSGA-II

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Ibrahim, Amin Makrehchi, Masoud Lab Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Engineering Department St. CatharinesON Canada Ontario Tech University Faculty of Business and Information Technology OshawaON Canada

ISBN: (纸本)9798350337020

Multi-objective optimization is a branch of computation to solve mathematical optimization problems when conflicting multiple objective functions must be simultaneously optimized. Many population-based algorithms, such as NSGA-II are used to find an optimal Pareto set of solutions in a short time. However, previous research works showed the performance of classic NSGA-II is degraded when solving problems with many objectives (M ≥ 5). In this work, we aim to take advantage of center-based sampling scheme to increase the exploration and exploitation capability of NSGA-II algorithm. This sampling strategy has demonstrated promising results on several single-objective evolutionary algorithms such as GA, DE, and PSO. Recently, a novel clustering center-based strategy has been proposed, which motivated us to utilize center-based sampling scheme in NSGA-II to solve multi-objective optimization problems. The outcomes confirm that the proposed clustering center-based NSGA-II is able to effectively solve CEC-2017 multi-objective benchmark problems with 2, 3, 5, 10, and 15 objectives. © 2023 IEEE.

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SmartCoach: Comprehensive Tutor Recommender and Student Attentiveness Measuring Platform 3

SmartCoach: Comprehensive Tutor Recommender and Student Atte...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Samarasekara, H.D.K. Hansaka, H.M.P. Rajapaksha, S.D.D. Tharaka, W.A.D.G. Manathunga, Kalpani Sriyarathna, Disni Charuka Faculty of Computing Department of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9781665408622

With the pandemic, coaching or tutoring classes became to halt as the underlying system wasn't facilitating to adapt to emergency teaching mechanisms due to sparse of relevant technologies and tutor unawareness. SmartCoach is an integrated platform that allows students to find suitable tutors based on recommendations and other parameters like proximity, previous track record etc. using sentiment analysis and natural language processing. For tutors, SmartCoach allows publishing learning materials, automatic quiz creation, tracking attendance and, attentiveness during classes using OpenCV library. Moreover tutors get a dashboard featuring critical aspects about their classes and, personal income. This research is aimed to introduce a comprehensive distance learning platform with novel technological approaches that connect both potential students and tutors to a common platform. © 2021 IEEE.

关键词： Sentiment analysis

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Exploiting the sequential nature of genomic data for improved analysis and identification

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computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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Campus safety monitoring system based on deep learning 20

Campus safety monitoring system based on deep learning

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20th International Symposium on Distributed Computing and Applications for Business engineering and Science, DCABES 2021

作者： Zhang, Qianying Hang, Jin Zhang, Boxuan Wei, Mengzhong Zhu, Quanyin Li, Xiang Huaiyin Institute of Technology Faculty of Computer Software Engineering Huai'an China

ISBN: (纸本)9781665428897

In view of the safety hazards existing on campus, a campus safety detection system based on deep learning behavior recognition, license plate recognition and speed detection is proposed and designed. The design divides the campus safety monitoring system into three functional modules: hazard behavior identification, license plate tracking and vehicle speed measurement, and on this basis, designs the database of storage information and the warning system of speed too fast to realize the monitoring function of campus safety. © 2021 IEEE.

关键词： License plates (automobile)

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Magnificent Frigatebird Optimization: A New Bio-Inspired Metaheuristic Approach for Solving Optimization Problems

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computers, Materials & Continua 2024年第8期80卷 2721-2741页

作者： Tareq Hamadneh Khalid Kaabneh Ibraheem AbuFalahah Gulnara Bektemyssova Galymzhan Shaikemelev Dauren Umutkulov Sayan Omarov Zeinab Monrazeri Frank Werner Mohammad Dehghani Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Computer Engineering International Information Technology UniversityAlmaty050000Kazakhstan Department of Big Data Analytics and Data Science Software Development Company《QazCode》Almaty050063Kazakhstan Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany

This paper introduces a groundbreaking metaheuristic algorithm named Magnificent Frigatebird Optimization(MFO),inspired by the unique behaviors observed in magnificent frigatebirds in their natural *** foundation of MFO is based on the kleptoparasitic behavior of these birds,where they steal prey from other *** this process,a magnificent frigatebird targets a food-carrying seabird,aggressively pecking at it until the seabird drops its *** frigatebird then swiftly dives to capture the abandoned prey before it falls into the *** theoretical framework of MFO is thoroughly detailed and mathematically represented,mimicking the frigatebird’s kleptoparasitic behavior in two distinct phases:exploration and *** the exploration phase,the algorithm searches for new potential solutions across a broad area,akin to the frigatebird scouting for vulnerable *** the exploitation phase,the algorithm fine-tunes the solutions,similar to the frigatebird focusing on a single target to secure its *** evaluate MFO’s performance,the algorithm is tested on twenty-three standard benchmark functions,including unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** results from these evaluations highlight MFO’s proficiency in balancing exploration and exploitation throughout the optimization *** studies with twelve well-known metaheuristic algo-rithms demonstrate that MFO consistently achieves superior optimization results,outperforming its competitors across various *** addition,the implementation of MFO on four engineering design problems shows the effectiveness of the proposed approach in handling real-world applications,thereby validating its practical utility and robustness.

关键词： Optimization metaheuristic magnificent frigatebirds exploration exploitation

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Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning

Determining the Critical Factors of Antenatal Care Follow-Up...

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International Conference on Soft Computing and Measurements (SCM)

作者： Igor A. Bessmertny Bidru C. Enkomaryam Adane L. Mamuye Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russia Department of Computer Science University of Gondar Gondar Ethiopia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman’s age, and HIV testing to predict antenatal care follow-up in the study area.

关键词： Machine learning algorithms Accuracy Scalability Clustering algorithms Machine learning Predictive models Prediction algorithms

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Streamlining software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore software Maintenance

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computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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