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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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An Exhaustive Meta-Analysis on Consumer Retention Forecasting Using Advanced Machine Learning Techniques

An Exhaustive Meta-Analysis on Consumer Retention Forecastin...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Shreenidhi H S Dhyan Chandra Yadav G Swetha Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India

In this meta-analysis, we present a robust approach for consumer retention forecasting by integrating advanced machine learning algorithms. The method comprises three core algorithms: Consumer Behaviour Neural Network (CBNN), Retention Decision Trees (RDT), and Long Short-Term Memory for Retention (LSTM-R). The objective is to model intricate relationships within consumer behaviours and accurately predict their retention probabilities. CBNN a key component, adeptly captures intricate patterns within consumer behaviour data. It involves multiple hidden layers with activation functions, enabling accurate prediction of retention probabilities based on input features. The flowchart for CBNN showcases the training process involving forward and backward passes, underlining its deep learning approach. RDT this method utilizes decision tree algorithms to predict consumer retention decisions. By recursively splitting the dataset based on information gain, RDT efficiently models consumer behaviour, as depicted in the flowchart. information gain is maximized to create a robust predictive model. LSTM-R tailored for temporal data in consumer retention prediction, leverages specialized LSTM architecture. It effectively processes sequential consumer interactions, crucial for accurate prediction of consumer retention probabilities. The proposed method integrates these algorithms, leveraging historical consumer data, and conducts a comprehensive meta-analysis to evaluate their predictive performance.

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Novel Universal Windowing Multicarrier Waveform for 5G Systems

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computers, Materials & Continua 2021年第5期67卷 1523-1536页

作者： Ahmed Hammoodi Lukman Audah Montadar Abas Taher Mazin Abed Mohammed Mustafa S.Aljumaily Adeeb Salh Shipun A.Hamzah Wireless and Radio Science Centre Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit Raja86400Malaysia Department Communications Engineering University of DiyalaBaqubah32001Iraq College of Computer Science and Information Technology University of AnbarAnbar31001Iraq Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN37901USA

Fifth Generation(5G)systems aim to improve flexibility,coexistence and diverse service in several aspects to achieve the emerging applications *** and filtering of the traditional multicarrier waveforms are now considered common sense when designing more flexible *** paper proposed a Universal Windowing Multi-Carrier(UWMC)waveform design platform that is flexible,providing more easily coexists with different pulse shapes,and reduces the Out of Band Emissions(OOBE),which is generated by the traditional multicarrier methods that used in the previous generations of the mobile *** novel proposed approach is different from other approaches that have been proposed,and it is based on applying a novel modulation approach for the Quadrature-Amplitude Modulation(64-QAM)which is considered very popular in mobile *** new approach is done by employing flexible pulse shaping windowing,by assigning windows to various *** leads to decreased side-lobes,which are going to reduce OOBE and boost the spectral efficiency by assigning them to edge subscribers *** new subband windowing(UWMC)will also maintain comprehensively the non-orthogonality by a variety of windowing and make sure to keep window time the same for all *** addition,this paper shows that the new approach made the Bit Error Rate(BER)equal to the conventional Windowed-Orthogonal Frequency Division Multiplexing(W-OFDM).This platform achieved great improvement for some other Key Performance Indicators(KPI),such as the Peak to Average Power Ratio(PAPR)compared with the conventional(W-OFDM)and the conventional Universal Filtered Multicarrier(UFMC)*** particular,the proposed windowing scheme outperforms previous designs in terms of the Power Spectral Density(PSD)by 58%and the(BER)by 1.5 dB and reduces the Complementary Cumulative Distribution Function Cubic Metric(CCDF-CM)by 24%.

关键词： 5G waveform window-OFDM universal filtered multi-carrier key performance indicators

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Post-Covid-19 Pandemic IT Project Management Skills and Challenges 2

Post-Covid-19 Pandemic IT Project Management Skills and Chal...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Ali, Liaqat Taleb, Nasser Ali, Atif Abu-Alsondos, Ibrahim A. Naseem, Hina Yousaf, Farhan Abdelhakim, Mohamed University of Science and Technology of Fujairah College of Engineering and Technology Fujairah United Arab Emirates Canadian University Dubai Faculty of Management Dubai United Arab Emirates Cyberjaye63100 Malaysia College of Computer Information Technology Dubai503000 United Arab Emirates Allama Iqbal Open Universioty Islamabad Pakistan Applied Science Research Center Applied Science Private University Amman11937 Jordan University of Management and Technology Pakistan

ISBN: (纸本)9798350335644

Since December 2019, the COVID-19 pandemic has harmed social, financial, and work life. The novel coronavirus has caused problems in all business sectors, including information technology(IT). Many Projects have been stopped or delayed due to the impact of this pandemic. Most of the companies recognized the importance of IT to achieve competitive advantage and to survive. Companies are investing a lot of money in IT projects. More than 60% of IT project fail. Lack of professional IT project management is one of the main reasons for that fail. Managing IT projects is a complex problem. Crises such as COVID 19 and uncertainty are increasing the complexity and challenges in IT projects management. This paper investigates what are the IT project management Key success factors required during and after the Pandemic. Secondary data were collected from literature review in the field of IT project management. an online questionnaire was used to collect the primary data from 107 IT firms. 323 respondents were participated. It is concluded that the following Key success factors are required: e-communication, centralization of data, online project monitoring, designing new policies and guidelines for incorporating new work culture, gaining and establishing project risk management exercises, especially cybersecurity and data protection by gaining access to the latest tools, establishing a culture for strict usage of Personal Protective Equipment (PPEs) to help project managers complete projects safely, and top management support. © 2023 IEEE.

关键词： COVID-19

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 IEEE.

关键词： Image enhancement

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Wireless Liberty: Sensors Measuring Pressure and Temperature

Wireless Liberty: Sensors Measuring Pressure and Temperature

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Advances in Computation, Communication and information technology (ICAICCIT), International Conference on

作者： Pavan Chaudhary M Vanithalakshmi K N Raja Praveen School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Bangalore Karnataka India

The continuous collection of physiological data, especially temperature and pressure, throughout the human body, using wireless, skin-adherent detectors based on NFC (near-field communication) technology, is presented in this research study. To better understand human health, forecast illnesses, and avoid problems like pressure ulcers, the article stresses the significance of monitoring skin temperature as well as pressure in diverse anatomical locations. This study proposes a novel NFC-based method for continuous physiological monitoring. It provides hitherto unattainable tracking capabilities for pressure and temperature throughout the human body and has the potential to significantly enhance healthcare results. In order to increase the radius and sensitivities of these sensors, future research will examine enlarged clinical investigations and developments in wireless technology.

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Machine Learning Models for Brain Signal Classification: A Focus on EEG Analysis in Epilepsy Cases

Machine Learning Models for Brain Signal Classification: A F...

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Cyber Resilience (ICCR), International Conference on

作者： Ali M Ali Shadi Nashwan Ahmad Al-Qerem Ammar Almomani Mahmoud Al Sakhnini Amjad Aldweesh Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. This study presents a machine learning framework for classifying normal and epileptic EEG recordings. The approach involves feature extraction using discrete wavelet transform (DWT) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. Specifically, DWT decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. Supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. This epilepsy EEG classification framework demonstrates the potential for AI to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities.

关键词： Neurological diseases Machine learning algorithms Scalability Epilepsy Transforms Feature extraction Electroencephalography

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A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

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Intelligent Automation & Soft Computing 2023年第1期35卷 163-180页

作者： Yassir Edrees Almalki Maryam Zaffar Muhammad Irfan Mohammad Ali Abbas Maida Khalid K.S.Quraishi Tariq Ali Fahad Alshehri Sharifa Khalid Alduraibi Abdullah AAsiri Mohammad Abd Alkhalik Basha Alaa Alduraibi M.K.Saeed Saifur Rahman Department of Medicine Division of RadiologyMedical CollegeNajran UniversityNajran11001Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran11001Saudi Arabia Department of Chemical Engineering PIEAS UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University Islamabad(CUI)Sahiwal Campus57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraydah52571Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran11001Saudi Arabia Faculty of Human Medicine Zagazig University44631Egypt

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the *** X-ray is considered very useful and less time-consuming for monitoring COVID *** doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is *** address this issue,we proposed to utilize deep learning *** this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients *** performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray *** proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of *** system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 ***,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

关键词： Biomedical systems chest X-ray images CNN COVID-19 swin transformer image processing

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Powering Next-Generation Wireless Communication With Cooperative Strategies

Powering Next-Generation Wireless Communication With Coopera...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Amrutha V Nair Panduranga Rao M V Trapty Agrawal Department of Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Recent research has been focused on RF energy transmission and recovery as a potential strategy to greatly increase the lifespan of resource wireless networks. This method has the ability to remotely supply items with vitality that is comparatively steady and prolonged; as a result, it introduced a new methodological approach known as WPC, that had created numerous new research avenues with broad uses. In these, the creation and evaluation of effective joint plans has recently garnered a lot of study attention. The collaboration continuum communicating initiatives for behavioral connectivity networks, the collaborating crosstalk techniques forward into mobile broadband network security, and the explaining policies and procedures for wap data transmission are all highlighted in this article's general introduction of working in multiple approaches. Again once we wrap up this piece, we also highlight a number of intriguing study paths in this field.

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Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

SSRN

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SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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