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Artificial intelligence in steering the digital transformation of collaborative technical education

Journal of High technology Management Research 2023年第2期34卷

作者： Lakshmi, A. Jaya Kumar, Ashok Kumar, M. Sunil Patel, Syed Imran Naik, S.K. Lokesh Ramesh, J.V.N. Department of ECE Vardhaman College of Engineering Hyderabad India Department of Mechanical Engineering New Horizon College of Engineering Bangalore India AP Tirupathi India Lecturer Computer Science Bahrain Polytechnic – Bahrain Technical College Higher Education Council Bahrain Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India

In recent years, the United Arab Emirates (UAE) has been a leader in the global adoption of AI and online schooling. Despite using the traditional educational structure, military colleges have embraced this new technology. This research analyzed the present adoption rate, difficulties, and solutions for implementing an AI-based online education system. The results demonstrate that digital technology has a tangible impact on all facets of higher education if it is supported by the institution. The results also indicate that the organization plays a crucial role in the integration of digital technology into teaching and learning, and that an examination of the materiality already present in the Collaborative technical Education (CTE) organization is necessary for comprehending the potential effects of new digital technology. Finally, this paper addressed how AI can make e-learning more interesting, efficient, and tailored to each individual learner, all of which contribute to better learning outcomes and wider access to technical education. The details of how artificial intelligence can be used to promote diversity and equity in the classroom are laid out. To motivate educators to create mixed-reality artifacts and conduct further research to support collaborative educational environments, this article discusses current works and visualizes the current state of the field. © 2023 Elsevier Inc.

关键词： Artificial intelligence

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Research on Image Super-resolution Reconstruction Based on Deep Learning 7

Research on Image Super-resolution Reconstruction Based on D...

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2020 7th International Conference on computer-Aided Design, Manufacturing, Modeling and Simulation, CDMMS 2020

作者： Zhang, Yaobin Zhang, Zhiheng Li, Tingle Computer Science and Engineering South China University of Technology Guangzhou Guangdong510006 China Department of Electrical Engineering Shandong University of Science and Technology Jinan Shandong250031 China College of Computer Science and Technology Tianjin Polytechnic University Tianjin300387 China

Image super-resolution processing technology refers to the technology of reconstructing high-resolution images by using low-resolution images with mutual displacement of multiple frames of the same scene. At present, image super-resolution processing technology is widely used in image reconstruction in various fields, such as: medical images, remote sensing images, robot vision, etc. In recent years, deep learning has also been widely used in various fields, especially in computer vision and speech processing. Since image super-resolution reconstruction the method of deep learning is introduced into the technology. Due to the good performance advantages of the deep learning method, it has become an important method for the research of image superresolution reconstruction technology. In this paper, the current status of domestic and foreign research on image super-resolution reconstruction is first simplified. Then, the theory of deep learning was explained, mainly the artificial neural network theory, the related theory of super-resolution reconstruction based on deep learning, and the method of image super-resolution reconstruction based on deep learning. © Published under licence by IOP Publishing Ltd.

关键词： Image reconstruction

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Global Sparse Attention Network for Remote Sensing Image Super-Resolution

SSRN

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SSRN 2024年

作者： Hu, Tao Chen, Zijie Wang, Mingyi Hou, Xintong Lu, Xiaoping Pan, Yuanyuan Li, Jianqing School of Computer Science and Engineering Macau University of Science and Technology China College of Information Engineering Hangzhou Vocational & Technical College Hangzhou China Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Physics and Optoelectronic Engineering Foshan University Foshan China Sydney Australia

Recently, remote sensing images have become popular in various tasks, including resource exploration. However, limited by hardware conditions and formation processes, the obtained remote sensing images often suffer from low-resolution problems. Unlike the high cost of hardware to acquire high-resolution images, the super-resolution software methods are good alternatives to restore low-resolution images. In addition, remote sensing images have a common nature that similar visual patterns repeatedly appear across distant locations. To fully capture these long-range satellite image contexts, we first introduce the Global Attention Network super-resolution method to reconstruct the images. This network improves the performance but introduces unvital information while significantly increasing the computational effort. To address these problems, we propose an innovative method named the Global Sparse Attention Network (GSAN), which integrates both Sparsity Constraints and Global Attention. Specifically, our method applies Spherical Locality Sensitive Hashing (SLSH) to convert feature elements into hash codes, constructs attention groups based on the hash codes, and computes each element of the attention matrix according to similar elements in the attention group. Our method captures valid and useful global information and reduces the computational effort from quadratic to asymptotic linear regarding the spatial size. Extensive qualitative and quantitative experiments demonstrate that our GSAN has significant competitive advantages compared to other state-of-the-art networks in terms of various metrics and visual quality. © 2024, The Authors. All rights reserved.

关键词： Hash functions

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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TCPW BR: A Wireless Congestion Control Scheme Base on RTT

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computers, Materials & Continua 2020年第1期62卷 233-244页

作者： Liwei Tian Jinfeng Li Longqing Zhang Yu Sun Lei Yang College of Computer Science Guangdong University of Science and TechnologyDongguan523083China College of Information Technology Macao University of Science and TechnologyMacao SAR Department of Information Technology Huizhou City CollegeHuizhou516025China College of Information Engineering Guangdong Innovative Technical CollegeDongguan523960China

The wireless network is limited by the transmission medium,and the transmission process is subject to large interference and *** jitter can cause sporadic loss and is mistaken for congestion by the congestion control *** TCP Westwood protocol(referred to as TCPW)is such that it cannot distinguish between congestion loss and wireless jitter loss,which makes the congestion mechanism too sensitive and reduces bandwidth *** on this,the TCPW protocol is modified based on the estimate of the Round-Trip Time(referred to as RTT)value-called TCPW *** algorithm uses the measured smooth RTT value and divides the congestion level according to the weighted average idea to determine congestion loss and wireless jitter *** simulation results show that the TCPW BR algorithm enhances the wireless network’s ability to judge congestion and random errors.

关键词： Congestion control RTT TCP Westwood

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HBPSO based Resource Scheduling for Optimization of Cost and Makespan in Cloud Data Centers

HBPSO based Resource Scheduling for Optimization of Cost and...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： A. Sathya Sofia C. P. Thamil Selvi P. Francis Antony Selvi P. Emmanuel Nicholas Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India Department of Artificial Intelligence & Data Science Rathinam Technical Campus Coimbatore Tamil Nadu India Department of Electronics and Electrical Engineering RVS College of Engineering Dindigul Tamil Nadu India Department of Mechanical Engineering Govt. Polytechnic College Chekkanurani Madurai Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Now present years, Cloud computing has emerged as a computational model for High-Performance Computing (HPC) applications, which deals with complex and highly intensive data. Cloud computing is a promising new style of scheduling environment which provides an scalable with on-demand supply methods to the consumers. In a cloud context, job or application scheduling is difficult and NP-complete. The objective of this work is to develop an algorithm to schedule for efficiently managing cloud resources besides finishing jobs with scientific applications applied in the Cloud Resource Broker (CRB) framework in Cloud Data Centre (CDC). The proposed HoneyBee based PSO (HBPSO) scheduling algorithm is deliberated for efficient resource allocation and henceforth the suggested optimization method anticipates to accomplish the objectives in minimizing both execution time and cost required to execute the job. It promotes resource allocation and job scheduling functions by balancing the load and reducing the skew, this in turn will eventually decrease cost, make span and resource utilization. Our goal is to transform the cloud datacenter to an efficient and effective datacenter in terms of time, cost and resource utilization so as to enhance the business values. By reducing completion times and costs while increasing the number of applications completed and user satisfaction, the simulation results utilizing Cloudsim and Cloud analyst demonstrate the efficacy of the suggested research work.

关键词： Cloud computing Data centers Schedules Costs Scheduling algorithms Simulation Optimization methods Transforms Scheduling Resource management

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Digital Teaching and Training System of Vocational colleges Based on a Cloud Platform

Digital Teaching and Training System of Vocational Colleges ...

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Industrial Application of Artificial Intelligence (IAAI), IEEE International Conference on

作者： Wenda Xie Computer Engineering Technical College (Artificial Intelligence College) Guangdong Polytechnic Science and Technology Zhuhai China

ISBN: (纸本)9781665465816

To effectively integrate theory and practice, teaching and training, and improve the overall quality of teaching and training, a cloud platform-based digital teaching and training system is proposed. The hardware unit of the design system includes the building unit of the cloud platform, the processor selection unit and the controller selection unit. The software module includes a teaching and training data collection and processing module, a training room management module and a database building module. Through the design of the hardware unit and software module, the running of the digital teaching and training system in vocational colleges is realized. The experimental data show that the evaluation results of the teaching and training quality are above the qualification standard after the application of the design system, which fully shows that the design system has better application performance and provides support for society to cultivate more high-skilled talent and research on the teaching and training system.

关键词： Training Databases Conferences Buildings Training data Process control Software

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From A-to-Z Review of Clustering Validation Indices

arXiv

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arXiv 2024年

作者： Hassan, Bryar A. Tayfor, Noor Bahjat Hassan, Alla A. Ahmed, Aram M. Rashid, Tarik A. Abdalla, Naz N. Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Information Technology Kurdistan Technical Institute Sulaimani Iraq Department of Database Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaimani Iraq Information Technology Department Faculty of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

Data clustering involves identifying latent similarities within a dataset and organizing them into clusters or groups. The outcomes of various clustering algorithms differ as they are susceptible to the intrinsic characteristics of the original dataset, including noise and dimensionality. The effectiveness of such clustering procedures directly impacts the homogeneity of clusters, underscoring the significance of evaluating algorithmic outcomes. Consequently, the assessment of clustering quality presents a significant and complex endeavor. A pivotal aspect affecting clustering validation is the cluster validity metric, which aids in determining the optimal number of clusters. The main goal of this study is to comprehensively review and explain the mathematical operation of internal and external cluster validity indices, but not all, to categorize these indices and to brainstorm suggestions for future advancement of clustering validation research. In addition, we review and evaluate the performance of internal and external clustering validation indices on the most common clustering algorithms, such as the evolutionary clustering algorithm star (ECA*). Finally, we suggest a classification framework for examining the functionality of both internal and external clustering validation measures regarding their ideal values, user-friendliness, responsiveness to input data, and appropriateness across various fields. This classification aids researchers in selecting the appropriate clustering validation measure to suit their specific requirements. © 2024, CC BY.

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SFUSNET: A SPATIAL-FREQUENCY DOMAIN-BASED MULTI-BRANCH NETWORK FOR DIAGNOSIS OF CERVICAL LYMPH NODE LESIONS IN ULTRASOUND IMAGES

arXiv

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arXiv 2023年

作者： Yue, Yubiao Xue, Jun Liang, Haihua Luo, Bingchun Li, Zhenzhang College of Mathematics and Systems Science Guangdong Polytechnic Normal University Guangzhou China School of Biomedical Engineering Guangzhou Medical University Guangzhou China School of Computer Science and Technology Anhui University Anhui China School of Computer Science and Technology Harbin Institute of Technology Weihai China

Booming deep learning has substantially improved the diagnosis for diverse lesions in ultrasound images, but a conspicuous research gap concerning cervical lymph node lesions still remains. The objective of this work is to diagnose cervical lymph node lesions in ultrasound images by leveraging a deep learning model. To this end, we first collected 3392 cervical ultrasound images containing normal lymph nodes, benign lymph node lesions, malignant primary lymph node lesions, and malignant metastatic lymph node lesions. Given that ultrasound images are generated by the reflection and scattering of sound waves across varied bodily tissues, we proposed the Conv-FFT Block. It integrates convolutional operations with the fast Fourier transform to more astutely model the images. Building upon this foundation, we designed a novel architecture, named SFUSNet. SFUSNet not only discerns variances in ultrasound images from the spatial domain but also adeptly captures micro-structural alterations across various lesions in the frequency domain. To ascertain the potential of SFUSNet, we benchmarked it against 12 popular architectures through five-fold cross-validation. The results show that SFUSNet is the state-of-the-art model and can achieve 92.89% accuracy. Moreover, its average precision, average sensitivity and average specificity for four types of lesions achieve 90.46%, 89.95% and 97.49%, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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Typhoon Track Image Prediction Using a Comprehensive LSTM Attention Model in Deep Learning

Typhoon Track Image Prediction Using a Comprehensive LSTM At...

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Neural Networks, Information and Communication engineering (NNICE), International Conference on

作者： Yiping Jiang Yi Yu Yuelong Ma Jiahao Zhuang Xianze Ye Yifeng Yuan Guanpeng Su School of Computer Science Guangdong University of Science and Technology Dongguan China Faculty of Science and Technology FST University of Macau Macau SAR China School of Information and Optoelectronic Science and Engineering South China Normal University Guangzhou China Faculty of Applied Sciences Macao Polytechnic University Macau SAR China School of Computer Zhuhai College of Science and Technology Zhuhai China

Typhoons are exceptionally destructive tropical cyclones that possess the capacity to inflict significant harm upon society. For institutions engaged in risk assessment and disaster mitigation, accurately forecasting typhoon paths and trajectories is of paramount importance. In this study, we propose a Typhoon Track Image Prediction model based on a Comprehensive Long Short-Term Memory (LSTM) Attention Model in the field of Deep Learning. This model significantly improves the accuracy of typhoon track prediction. To assess its performance, we utilize typhoon data from the China Typhoon Network spanning from 1949 to 2022. The Comprehensive LSTM Attention Model is compared against other popular models such as Temporal Convolutional Networks (TCN), traditional LSTM, and Seq2Seq models for forecasting the typhoon track over a 12-hour period. The results demonstrate the superiority of our proposed model, showcasing its effectiveness in terms of superior accuracy in typhoon track prediction.

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