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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Fatema Baqer Suresh Subramanian Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Multimedia science Department College of Information Technology Ahlia University Manama Bahrain

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Autism spectrum disorder, often shortened to ASD, is a developmental disability caused by differences in the brain. Individuals with ASD display deficits in social communication and interaction as well as repetitive behaviors or interests among a list of other symptoms [1]. If left untreated, individuals with autism may observe no progression or a worsening of their symptoms as untreated ASD can increase the risk of the development of depression and anxiety. The best outcomes are expected when the individual is diagnosed as early as possible allowing for early intervention and the provision of therapy and treatments. ASD can be detected in individuals as young as toddlers. Much research has been conducted into the application of machine learning in order to predict ASD in many different age groups, with the category of toddlers having the least dedicated research. This paper aims to explore the application of different machine learning techniques in order to predict ASD in toddlers. Two different toddler datasets were used to train and test the models. Many different traditional machine learning models, ensemble models, and deep learning models were considered in the scope of this research and compared on the basis of performance. K-fold cross validation was applied, and the models were tested. The most positive results in terms of the highest accuracy recorded were seen in ensemble learning which near consistent positive results being seen for all the different experimented techniques. Further favorable results were seen in particular with random forest (100%), decision trees (100%), C4.5 (100%) and logistic regression (100%). In terms of deep learning the most positive results were seen in multilayer perceptron (100%), recurrent neural networks (98.6%), and multilayer kernel machines (100%).

关键词： Deep learning Radio frequency Autism Pediatrics Logistic regression Recurrent neural networks Computational modeling Machine learning Predictive models Ensemble learning

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Optimal Deep Learning Model Enabled Secure UAV Classification for Industry 4.0

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computers, Materials & Continua 2023年第3期74卷 5349-5367页

作者： Khalid A.Alissa Mohammed Maray Areej A.Malibari Sana Alazwari Hamed Alqahtani Mohamed K.Nour Marwa Obbaya Mohamed A.Shamseldin Mesfer Al Duhayyim SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Emerging technologies such as edge computing,Internet of Things(IoT),5G networks,big data,Artificial Intelligence(AI),and Unmanned Aerial Vehicles(UAVs)empower,Industry 4.0,with a progressive production methodology that shows attention to the interaction between machine and human *** the literature,various authors have focused on resolving security problems in UAV communication to provide safety for vital *** current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification(CSODL-SUAVC)model for Industry 4.0 *** suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image ***,the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation(ML-DWT),CSO-related Optimal Pixel Selection(CSO-OPS),and signcryption-based *** proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover *** secret images,encrypted by signcryption technique,are embedded into cover ***,the image classification process includes three components namely,Super-Resolution using Convolution Neural Network(SRCNN),Adam optimizer,and softmax *** integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV *** proposed CSODLSUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct *** simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.

关键词： Unmanned Aerial Vehicles Artificial Intelligence emerging technologies Deep Learning Industry 4.0 image steganography

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Mobile Apps for Healthy Life Education for Young People 2

Mobile Apps for Healthy Life Education for Young People

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Suwandi, Benedictus Evan Warnars, Diana Teresia Spits Warnars, Harco Leslie Hendric Spits Utomo, Wiranto Herry Ramadhan, Arief Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Computing President University Department of Magister Science of Information Technology Bekasi17550 Indonesia

ISBN: (纸本)9781665400527

Health education is crucial for the community, especially the younger generation, because adolescent behavior/lifestyle will carry over into adulthood, and it is difficult to change it. This paper proposes designing and implementing a mobile application for young people for healthy lifestyle education. This mobile application implemented with the android studio is designed using use case diagrams to describe the business processes of this mobile application. In addition, activity diagrams describe the process details of each activity use case, and class diagrams describe the design of the database model, which is implemented using a MySQL database. © 2022 IEEE.

关键词： Mobile computing

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Hand-Foot-Mouth Disease Classification using Features from Fibre Grating Biosensor Spectral Data 4

Hand-Foot-Mouth Disease Classification using Features from F...

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4th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2022

作者： Mahmood, Atif Azzuhri, Saaidal Razalli Qureshi, Adnan N. Jaan, Palwasha Sadia, Iqra University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan University of Malaya Department of Computer System & Technology Kuala Lumpur Malaysia Virtual University of Pakistan Faculty of Science & Technology Lahore Pakistan

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Hand, Foot and Mouth disease (HFMD) is a common viral childhood disease affected by the family of enterovirus and Coxsackie. Current laboratory identification is based on the RT-PCR test, which is expensive, time-consuming, and unsuitable for the pandemic. The SPR- TFBG was biofunctionalized with monoclonal antibody (Mab). Mab is a bioreceptor with an affinity for the virus for detecting EV-A71. A dataset of reflectance spectra of 660 samples of different virus impurities measured with SPR-TFBG biosensor to detect EV-71 virus was developed. The extracted signal has around 4000 different features based on wavelength information. The first subset was selected based on the region of interest analysis, and the dimension has reduced from 4000 to 1496 features. The dimensionality of the large feature set is reduced based on the statistical feature engineering procedure using 10 features including mean, variance, skewness, RMS, kurtosis, standard deviation, range, crest factor, impulse factor and shape factor. Subsequently, classification of the virus (signal) data is achieved through SVM and it is evaluated with different types of kernels. For the evaluation of classifiers, we used accuracy, sensitivity, precision and F1 score performance metrics. The obtained results of accuracy are 87.88 for linear SVM, 86.06 for radial basis, 75.76 for sigmoid SVM, and 75.15 for polynomial SVM, respectively. The results show that for our experiments, Linear SVM performs better than radial, polynomial and sigmoid kernels. This is because projecting the data onto higher dimensions is not required as data exhibits linear properties confirmed by White Neural Network (WNN) test for nonlinearity. © 2022 IEEE.

关键词： Viruses

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Exponential Moving Average for Time-Series Smoothing in Wind Speed Forecasting

Exponential Moving Average for Time-Series Smoothing in Wind...

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International Conference on Informatics and Computing (ICIC)

作者： Rudi Sutomo Risma Yulistiani Melissa Indah Fianty Information Systems Faculty of Engineering & Informatics Multimedia Nusamtara University Banten Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

The need for sustainable energy sources is of utmost importance, and wind power is becoming increasingly prominent. Ensuring accurate and dependable wind speed predictions is crucial for tackling energy sustainability issues. This experiment involved using Exponential Moving Average (EMA) to reduce temporal irregularities in a wind dataset time series. The categorization method Long Short-Term Memory (LSTM) was also applied. The investigation concluded with the calculation of a statistically significant R2 coefficient, which particularly reached a value of 0.935. This demonstrates how effectively the recommended method predicts wind speed to maximize renewable energy.

关键词： Renewable energy sources Smoothing methods Accuracy Wind speed Time series analysis Wind power generation Sustainable development Informatics Forecasting Long short term memory

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Practical Integration of IoT Sensor Data with Open-Source ERP: Optimizing Processes, Driving Insights, and Enabling Smart Vision and Full Business Automation

Practical Integration of IoT Sensor Data with Open-Source ER...

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Artificial Intelligence, Robotics and Control (AIRC), International Conference on

作者： Ahmed Lamey Hatem Abdelkader Arabi Keshk Sherif Eletriby Computer Science Department Faculty of Computers & Information Minoufia University Menofia Egypt Information System Department Faculty of Computers & Information Minoufia University Menofia Egypt

ISBN: (数字)9798350385076

ISBN: (纸本)9798350385083

The integration of Internet of Things (IoT) sensor data with Enterprise Resource Planning (ERP) systems has emerged as a pivotal technology-driven initiative, offering vast potential for process optimization, enhanced data-driven decision-making, and profound business impact. This research endeavors to delve into this integration, comprehensively examining the implications for organizations, with a practical project serving as a proof of concept. The study scrutinizes the intricacies of IoT sensor integration with ERP systems, shedding light on the optimization of business processes and the extraction of valuable data insights. In addition, the research extends its focus towards “smart vision” technologies, aiming to achieve a higher level of automation and real-time decision support. The core of this research lies in the practical project, where a proof of concept is executed to integrate temperature sensors with the Odoo ERP system. This project serves as a real-world demonstration of the integration's applicability and the tangible benefits it brings. By bridging the gap between theoretical concepts and practical implementation through this proof of concept, the study aims to provide actionable insights for organizations looking to harness the power of IoT sensor data, smart vision, and automation within their ERP systems. The outcomes of this research not only contribute to the growing body of knowledge on IoT-ERP integration but also provide a roadmap for organizations to leverage these technologies effectively in the modern business landscape.

关键词： Temperature sensors Automation Decision making Organizations Machine learning Robot sensing systems Real-time systems

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Elevating PCP Level Implementation: A Strategic Approach with Architecture Software in the Covid-19 Era

Elevating PCP Level Implementation: A Strategic Approach wit...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Handoko, Yeffry Zukarnain, Zuriani Ahmad Wahdiniwaty, Rahma Rahman, Noorihan Abdul Universitas Komputer Indonesia Master of Information System Department Bandung Indonesia Universiti Teknologi Mara Faculty of Computer and Mathematical Science Selangor Malaysia Universitas Komputer Indonesia Master of Management Department Bandung Indonesia

ISBN: (纸本)9798350317862

Between 2020 and 2021, Indonesia and Malaysia were both impacted by the COVID-19 pandemic. These two nations adopted a strikingly similar approach to restrict access to certain areas, primarily relying on identical criteria. In the case of Indonesia, the PPKM (Community Activity Restriction Enforcement) strategy was employed, this contained a component involving the exposure of COVID-19 affected persons. The determination of the extent of this exposure was primarily based on monitoring the rise in the number of individuals who had been in contact with COVID-19-positive individuals. However, during a pandemic, it's imperative to consider not only those individuals who are confirmed to have COVID-19 but also those who exhibit symptoms similar to COVID-19 but have not been diagnosed. These individuals can also act as potential agents for the spread of the pandemic within their communities. This is referred to as the "Precaution COVID-19 Pandemic (PCP) Level."It's important to note that the PCP level does not have a universally agreed-upon definition at present. Instead, it is influenced more by the number of individuals displaying symptoms akin to those of COVID-19, rather than solely relying on the increase in confirmed COVID-19 cases. In essence, both countries shared a comparable approach to pandemic management during this period, but the emergence of the PCP level underscores the need for a broader perspective when assessing the pandemic's impact on communities, considering not only confirmed cases but also symptomatic individuals who may contribute to its spread. The PCP Level determination can be used for preventative policy and to supplement the previous Pandemic Level Methods. Two methods are employed to realize this concept: the AHP approach for calculating the Covid-19 pandemic level and the K-Mean algorithm for pattern clustering. Data was gathered from 11 health centers in West Java province. The findings of this study underscore the potential of a multifac

关键词： COVID-19

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Computational Linguistics with Optimal Deep Belief Network Based Irony Detection in Social Media

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computers, Materials & Continua 2023年第5期75卷 4137-4154页

作者： Manar Ahmed Hamza Hala J.Alshahrani Abdulkhaleq Q.A.Hassan Abdulbaset Gaddah Nasser Allheeib Suleiman Ali Alsaif Badriyya B.Al-onazi Heba Mohsen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of English College of Science and Arts at MahayilKing Khalid UniversityAbha62217Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkah24211Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Computer Deanship of Preparatory Year and Supporting StudiesImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt

Computational linguistics refers to an interdisciplinary field associated with the computational modelling of natural language and studying appropriate computational methods for linguistic *** number of social media users has been increasing over the last few years,which have allured researchers’interest in scrutinizing the new kind of creative language utilized on the Internet to explore communication and human opinions in a *** and sarcasm detection is a complex task inNatural Language Processing(NLP).Irony detection has inferences in advertising,sentiment analysis(SA),and opinion *** the last few years,irony-aware SA has gained significant computational treatment owing to the prevalence of irony in web ***,this study develops Computational Linguistics with Optimal Deep Belief Network based Irony Detection and Classification(CLODBN-IRC)model on social *** presented CLODBN-IRC model mainly focuses on the identification and classification of irony that exists in social *** attain this,the presented CLODBN-IRC model performs different stages of pre-processing and TF-IDF feature *** irony detection and classification,the DBN model is exploited in this *** last,the hyperparameters of the DBN model are optimally modified by improved artificial bee colony optimization(IABC)*** experimental validation of the presentedCLODBN-IRCmethod can be tested by making use of benchmark *** simulation outcomes highlight the superior outcomes of the presented CLODBN-IRC model over other approaches.

关键词： Computational linguistics natural language processing deep learning irony detection social media

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

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2022 International Conference on Computing, Networking, Telecommunications and Engineering sciences Applications, CoNTESA 2022

作者： Swathi, Bandi Kumar, Madapuri Rudra Riyaz Belgaum, Mohammad Alansari, Zainab G. Pulla Reddy Engineering College Computer Science and Engineering Kurnool India G Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia University of Malaya Computer System and Information Technology Kuala Lumpur Malaysia Information Technology Muscat Oman

ISBN: (纸本)9798350398014

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies' safety and security. Speech is a uniquely human trait used to express and communicate one's point of view to others. Emotion recognition from speech audio signals is obtaining a presenter's emotions from the presenter's audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach's performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers' performance in making accurate detections with few false positives. © 2022 IEEE.

关键词： Genetic algorithms

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