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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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Pūioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System

arXiv

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arXiv 2023年

作者： Sinclair, Adam Kautai, Kayla Shahamiri, Seyed Reza Department of Electrical Computer and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand

Automated exercise repetition counting has applications across the physical fitness realm, from personal health to rehabilitation. Motivated by the ubiquity of mobile phones and the benefits of tracking physical activity, this study explored the feasibility of counting exercise repetitions in real-time, using only on-device inference, on smartphones. In this work, after providing an extensive overview of the state-of-the-art automatic exercise repetition counting methods, we introduce a deep learning based exercise repetition counting system for smartphones consisting of five components: (1) Pose estimation, (2) Thresholding, (3) Optical flow, (4) State machine, and (5) Counter. The system is then implemented via a cross-platform mobile application named Pūioio that uses only the smartphone camera to track repetitions in real time for three standard exercises: Squats, Push-ups, and Pull-ups. The proposed system was evaluated via a dataset of pre-recorded videos of individuals exercising as well as testing by subjects exercising in real time. Evaluation results indicated the system was 98.89% accurate in real-world tests and up to 98.85% when evaluated via the pre-recorded dataset. This makes it an effective, low-cost, and convenient alternative to existing solutions since the proposed system has minimal hardware requirements without requiring any wearable or specific sensors or network connectivity. © 2023, CC BY.

关键词： Automation

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Aspect Level Songs Rating Based Upon Reviews in English

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computers, Materials & Continua 2023年第2期74卷 2589-2605页

作者： Muhammad Aasim Qureshi Muhammad Asif Saira Anwar Umar Shaukat Atta-ur-Rahman Muhammad Adnan Khan Amir Mosavi Department of Computer Science Bahria University Lahore54000Pakistan Lahore Institute of Science and Technology Lahore 54792Pakistan Department of Multidisciplinary Engineering Texas A&M UniversityCollege Station77843USA Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam 31441Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

With the advancements in internet facilities,people are more inclined towards the use of online *** service providers shelve their items for *** users post their feedbacks,reviews,ratings,*** the use of the *** enormous increase in these reviews has raised the need for an automated system to analyze these reviews to rate these *** Analysis(SA)is a technique that performs such decision *** research targets the ranking and rating through sentiment analysis of these reviews,on different *** a case study,Songs are opted to design and test the decision *** aspects of songs namely music,lyrics,song,voice and video are *** the reason,reviews of 20 songs are scraped from YouTube,pre-processed and formed a *** machine learning algorithms—Naïve Bayes(NB),Gradient Boost Tree,Logistic Regression LR,K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)are *** performed the best with 74.99%*** are validated using K-Fold.

关键词： Machine learning natural language processing songs reviews:sentiment analysis songs rating aspect level sentiment analysis reviews analysis text classification music

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Self-Configured Workflow Platform for MapReduce Job Execution in Cloud Environment

Self-Configured Workflow Platform for MapReduce Job Executio...

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2022 Applied Informatics International Conference, AiIC 2022

作者： Saadoon, Muntadher Admodisastro, Novia University of Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Computer Science and Information Technology Selangor Serdang43400 Malaysia

ISBN: (数字)9781728184289

ISBN: (纸本)9781728184289

Hadoop MapReduce becomes the essential framework to store, process and analyse big data in large-scale computing environments. The framework provides numerous opportunities to handle data-intensive applications like web crawling and indexing, data mining, anomaly prediction and machine learning. However, manual configurations of the framework parameters expose to human errors and lead to performance overhead and resource consumption penalties. Existing deployment automation solutions intended to mitigate the limitations by only facilitating the application execution workflow without considering crucial conditions that violates the overall performance after setting up the infrastructure parameters. This paper proposes a self-configured workflow platform with a user-friendly collaborating environment that provides automated setups of the framework paraments to obtain ideal performance compared to the native deployment of Hadoop MapReduce. The self-configured workflow platform has been evaluated in experiments on a real Hadoop cloud-based cluster and the results shows that the execution times are comparable to the native application. © 2022 IEEE.

关键词： MapReduce

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Dysarthric Speech Recognition: A Comparative Study

Dysarthric Speech Recognition: A Comparative Study

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Conference on Speech Technology and Human-computer Dialogue, SpeD

作者： Dhvani Shah Vanshika Lal Zihan Zhong Qianli Wang Seyed Reza Shahamiri Department of Electrical Computer and Software Engineering Faculty of Engineering The University of Auckland Auckland New Zealand

Automatic Speech Recognition (ASR) systems are designed to convert spoken words into written text. These systems have the potential to greatly benefit individuals with speech impairments like dysarthria, improving their overall quality of life. However, there are significant challenges that researchers must address in order to develop an effective ASR system for atypical speech, specifically dysarthric speech. Among these challenges are the limited availability of dysarthric speech data and the need for an accurate interpretation model. In this study, we explored the development of dysarthric ASR systems, starting from traditional approaches that were specifically designed to recognize dysarthric speech. We then delve into deep-learning-based ASR systems and modern ASR engines that utilize transformers. Additionally, we highlighted the notable dysarthric speech collections and identified the key factors that impede the performance of dysarthric ASR systems. The findings indicate that although researchers have achieved gradual improvements in performance, further research is required to achieve practical performance levels. Likewise, it is important to establish guidelines and standards to accurately measure and benchmark the performance of dysarthric ASR systems in order to ensure their usefulness and effectiveness in real-world applications.

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StakePage: Analysis of Stakeholders of an Information System Using Page Rank Algorithm 1

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Hassan, Tanveer Mohammad, Chaudhary Wali Sadiq, Mohd. Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (数字)9789811988257

ISBN: (纸本)9789811988240

The requirements elicitation process is the sub-process of requirements engineering in which stakeholders are involved to identify the requirements of an information system. Various methods have been developed to identify and analyze the stakeholders like goal-oriented requirements elicitation process, traditional methods, etc. Based on our review, we found that little attention is given to the analysis of the stakeholders using different measures of social networks before the starting of the requirements elicitation process. Therefore, to address this issue, this paper presents a StackPage method for the analysis of the stakeholders using page rank algorithm of social network measures. The applicability of the StackPage method is discussed with the help of the stakeholders of an institute examination system. Finally, the results are compared with two state-of-the-art methods, i.e., StakeRare and StakeSoNet methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Requirements engineering

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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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The Development of a Glove-like Controller Interface for VR applications 14

The Development of a Glove-like Controller Interface for VR ...

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14th ACM SIGMM Workshop on Immersive Mixed and Virtual Environment Systems, MMVE 2022

作者： Salgado, Eduardo Pereira Salgado, Eurico Marques Salgado, Débora Pereira Rodrigues, Thiago Braga Faculty of Computer Science Federal University of Uberlândia Uberlândia Brazil Faculty of Mechanical Engineering Federal University of Uberlândia Uberlândia Brazil Faculty of Engineering & Informatics Technological University of the Shannon Athlone Ireland Department Computer and Software Engineering Technological University of the Shannon Athlone Ireland

ISBN: (纸本)9781450393829

Smart gloves are wearable interfaces which provide detection of movements and gestures, kinesthetic, and even tactile feedback. These capabilities are important for immersive applications as it can bring more realistic features. The presented paper shows a concept of a working in progress application of a smart glove. The proposed hardware uses resistive flexible and electromagnets sensors to provide tactile and haptic feedback to be used in immersive applications such as the ones that use Virtual Reality (VR). The project will be done in a way that can provide user-centric requirement such as: the glove must be lightweight, comfortable, adjustable to different hands and finger shapes, and low cost to be manufactured. The intend is to use this glove as part of a biggest framework and immersive application. © 2022 ACM.

关键词： Wearable sensors

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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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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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