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检索条件"任意字段=International Conference on Information Technology - Coding and Computing"

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Virtual Reality-Based Interventions for Improving Learning Outcomes in Children with ADHD 19

Virtual Reality-Based Interventions for Improving Learning O...

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19th international conference on Future Networks and Communications, FNC 2024 / 21st international conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th international conference on Sustainable Energy information technology, SEIT 2024

作者： Alobidi, Noof Alnanih, Reem Bakhsh, Helen Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

The utilization of virtual reality (VR) has been increasingly applied in various fields, including medicine and education, to address the challenges faced by children with attention deficit hyperactivity disorder (ADHD) and provide solutions. This study investigates the impact of VR intervention on the academic performance of ADHD children. The researchers implemented a modified VR game specifically designed to accommodate the unique challenges and preferences of ADHD children. In evaluating the academic performance of ADHD children, a pre-test and post-test were performed on both the VR intervention group and the control group. The pre-test aimed to determine baseline academic performance, while the post-test assessed any improvements following the intervention. The study included a total of 20 participants, with 10 assigned to the VR intervention group and 10 to the control group. Moreover, the researchers recorded the behavior of the child during VR gameplay and performed post-gameplay interviews to assess the user experience of the VR environment. Quantitative analysis, including ANCOVA, reveals a statistically significant improvement in post-test scores among participants in the VR intervention group compared to the control group, suggesting the effectiveness of VR in improving learning outcomes. The results indicate high levels of enjoyment and engagement among participants, with positive emotional responses. Overall, the study underscores the potential of VR interventions as effective tools for addressing the educational needs of children with ADHD. © 2024 The Authors. Published by Elsevier B.V.

关键词： Virtual environments

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The Role of Cloud computing in Ensuring Business Continuity during Disasters 6

The Role of Cloud Computing in Ensuring Business Continuity ...

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Bansal, Chiranjeev Tanwar, Sarvesh Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350304480

The most important aspects of a firm are business continuity planning and disaster recovery planning, yet they are frequently overlooked. Even before a tragedy strikes, businesses need to have a well-organized strategy and document for company continuity. Disasters may be brief or prolonged, but when a company is prepared for any difficulty, it flourishes and endures. This essay provides information on the value of business continuity and disaster recovery benefits that cloud computing may offer. This article aims to explain how cloud computing supports business continuity and disaster recovery through its service models. It also emphasizes system virtualization, another distinctive aspect of cloud computing that aids disaster recovery. It also makes an effort to comprehend how cutting-edge technologies like virtualization, SAAS, and CAAS help business continuity and disaster recovery. © 2023 IEEE.

关键词： computing Disaster Recovery Security Services

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Reversible Data-Hiding Scheme Using Color coding for Ownership Authentication 7th

Reversible Data-Hiding Scheme Using Color Coding for Ownersh...

引用

7th international conference on Microelectronics and Telecommunication Engineering, ICMETE 2023

作者： Singh, Anuj Kumar Kumar, Sandeep Singh, Vineet Kumar School of Computing Science and Engineering Galgotias University Greater Noida India ABES Institute of Technology Ghaziabad Ghaziabad India

ISBN: (纸本)9789819995615

The concept of reversible data hiding (RDH) enables the full restoration of the cover image while simultaneously recovering the concealed data from a previously obscured image. Hence, it is the favored choice when complete restoration of the cover image is required in situations where the concealment of critical data is important. This work introduces a system that utilizes interpolation-based color coding method (CCM) to temporarily conceal data without permanent deletion. The refinement of the expanded form of the original image is achieved by the utilization of two distinct methods, namely enhanced neighbor mean interpolation (ENMI) and modified neighbor mean interpolation (MNMI). This procedure is conducted prior to the inclusion of any confidential data. The experimental findings indicate that the suggested approach has the potential to be implemented and exhibits superior performance compared to standard methods in relations of highest signal-to-noise ratio and data whacking size. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Interpolation

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Segmentation of Skin Lesion Image Using FCM with Color Transformation Saliency 1

Segmentation of Skin Lesion Image Using FCM with Color Trans...

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1st IEEE international conference on computing and Data Science, ICCDS 2024

作者： Bavya, S. Saravana Kumar, V. Kavitha, M. Anantha SivaPrakasam, S. Vels Institute of Science Technology & Advanced Studies Department of Computer Science & Engineering Chennai India Sreenidhi Institute of Science and Technology Department of Information Technology Hyderabad India S A Engineering College Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science & Technology Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

In the majority of Western nations including America, Australia and Europe, skin cancer is badly-behaved. Skin concealing, inadequacy of Sun-lights, climate, age, and inherited are major risk factors. Early identification of skin cancer and proceed the treatment can avoid death. The abnormal skin region determines where the skin diseases had been found. Various sorts of skin ailments be existent currently. Melanoma is the sort of skin threat which has grows the greater part of mortality rate. The timely identification and intercession of melanoma include myriad changes of fix. Melanoma has been recognized by shape, size, concealing and outward of the skin injury. This paper demonstration about the traditional clustering technique in particular Fuzzy Clustering technique is acted in these skin lesion pictures. This strategy is segmenting this melanoma from these dermoscopic images effectively. The presentation of this strategy is assessed as far as pixel grouped and time complexity. © 2024 IEEE.

关键词： Feature extraction

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Mutual Fund Analysis Using Large Language Model

Mutual Fund Analysis Using Large Language Model

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2024 MIT Art, Design and technology School of computing international conference, MITADTSoCiCon 2024

作者： Ghodake, Mrunali Misal, Pranjal Tendulkar, Tanvi Hiray, Swapnaja Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350362879

Large language models (LLMs) have the potential to revolutionize mutual fund analytics. LLMs can be trained on massive datasets of financial data and news articles, and they can then be used to perform a variety of tasks, including: Identifying patterns and trends in mutual fund data. LLMs can be used to identify patterns and trends in mutual fund data that would be challenging for people to independently identify. This can be helpful for identifying potential investment opportunities and for understanding the risks associated with different mutual *** insights into mutual fund performance. LLMs can be used to generate insights into mutual fund performance that would be extremely difficult for humans to produce on their own, if not impossible. For example, LLMs can be used to identify the factors that are driving a mutual fund's performance and to predict how a mutual fund is likely to perform in the future. Creating personalized mutual fund recommendations. LLMs can be used to create personalized mutual fund recommendations for individual investors. This can be done by taking into account the investor's individual financial situation, risk tolerance, and investment goals. LLMs have the potential to make mutual fund analytics more accessible and affordable for individual investors. They can also help investors to make more informed investment decisions. © 2024 IEEE.

关键词： Risk management

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A brief evaluation of deep learning-based retinal disease approaches

A brief evaluation of deep learning-based retinal disease ap...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Regotra, Reetika Tamana Singh, Samridhi Yadav, Shekhar National Institute of Technology Himachal Pradesh Hamirpur India Madan Mohan Malaviya University of Technology Gorakhpur India

ISBN: (纸本)9781032669663

One of the most delicate parts of the eye is the retina. It is a brain limb that is linked by an optical nerve. The visual information can be organized by retina only. Numerous diseases, including ophthalmology and laryngology, can affect this substantial portion of the eye. Here, segmentation in medical image processing enters the picture for the correct identification of the various retinal diseases. The computer-aided diagnosis makes this simple to accomplish (CAD). CAD greatly helps in the routine clinical application of segmenting blood vessels closer to the retina. The key goal of this article is to present all of the most recent research and studies conducted on retinal disease using CAD since the paradigm has transformed from machine learning to deep learning. In our most recent comparison, we considered studies from 2019 to 2022 as well as benchmark datasets from the public domain (DRIVE, STARE, CHASE ERC) and performance evaluation metrics based on images taken with a fundus camera. Although the field of deep learning has made significant progress, there are still numerous challenges. As a result, we carefully examined these techniques in order to compile a list of unresolved research problems and potential solutions that could suggest a new direction of study for the scientific community. © 2024 The Author(s).

关键词： Ophthalmology

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Optimization of dedicated domain text classification based on data augmentation using BERT generation pre-trained model

Optimization of dedicated domain text classification based o...

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2023 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Xiang, Yuzhe Cui, Can Zhu, Xinjie Wang, Zhi North China Institute of Computing Technology Platform R&D Center China

ISBN: (纸本)9781510671881

Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data augmentation method based on the BERT model is proposed in this paper for augmenting the small amount of available annotated data. The optimization of this data augmentation method is demonstrated by experiments. In a text classification experiment, this data augmentation method can improve the training effect of the source data by 2.9%. © 2023 SPIE. All rights reserved.

关键词： Classification (of information)

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Intrusion Detection System (IDS) in Cloud computing using Machine Learning Algorithms: A Comparative Study 15

Intrusion Detection System (IDS) in Cloud Computing using Ma...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rathod, Ganesh Sabnis, Vikrant Jain, Jay Kumar Faculty of Engineering & Technology Mansarovar Global University Bhopal India Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331300579

Organizations have witnessed a significant transformation in the realm of data storage and processing owing to the advent of cloud computing. Nonetheless, with its advantages, cloud computing has also brought forth new security concerns that need to be addressed. In this regard, the use of Intrusion Detection Systems (IDSs) has become indispensable for identifying and preventing a wide range of attacks that may occur in cloud computing environments, thereby ensuring data security. In modern years, machine learning (ML) algorithms have emerged as a promising approach for IDSs, as they can analyze huge amounts of data and identify patterns that may not be detectable by traditional rule-based IDSs. This review paper presents a comprehensive analysis of ML-based IDSs & Tradition-based IDSs for intrusion detection in cloud computing environments. The literature review covers various Traditional & ML algorithms used for intrusion detection, including decision trees, Neural Networks (NN), Support Vector machines (SVM), random forests, and k-nearest neighbours. The performance evaluation metrics used in this review paper include accuracy and false positive rate. These metrics are generally used to evaluate the performance of IDS and ML algorithms. The results of the analysis indicate that ML-based IDSs outperform traditional IDSs in terms of accuracy and false positive rate. However, ML-based IDSs may also have limitations, such as a high rate of false negatives and the usefulness of huge amounts of training data. Overall, the analysis suggests that ML-based IDSs have the potential to improve the usefulness of intrusion detection in cloud computing environments, but further research is needed to address the limitations of these systems. © Grenze Scientific Society, 2024.

关键词： Network intrusion

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Online doctor-patient matching method based on patient consensus and doctor fairness 24

Online doctor-patient matching method based on patient conse...

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2024 2nd international conference on Internet of Things and Cloud computing technology, IoTCCT 2024

作者： Li, Fangshun Zhang, Yinrunjie Liang, Decui School of Management and Economics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798400710148

With the rapid development of Internet and smart medicine, online medical platform has gradually become an important channel to meet the basic medical needs of the public. How to meet the needs of patients and achieve precise matching between doctors and patients is an urgent problem to be solved. At the same time, it is necessary to ensure the fairness of doctors and avoid wasting medical resources in the process of doctor-patient matching. Therefore, we propose an online doctor-patient matching method based on patient consensus and doctor fairness to assist online medical platforms in making decisions. Firstly, we build the consensus degree of patients based on the feature information of doctors and the patient demands. Then, we use the Rawlsian maximin fairness to measure doctor fairness and construct a doctor-patient matching model with minimizing patient risk losses and maximizing doctor fairness. This model can obtain the doctor-patient matching results for the online medical platform. Finally, a case study of the HaoDaiFu online medical platform verify the effectiveness of the proposed doctor-patient matching method. © 2024 Copyright held by the owner/author(s).

关键词： Electronic health record

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Blockchain-Based Ensemble Deep Learning Model Technique for Breast Cancer Classification 2

Blockchain-Based Ensemble Deep Learning Model Technique for ...

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2nd international conference on computing and Data Analytics, ICCDA 2024

作者： Rahal, Hakima Rym Slatnia, Sihem Kazar, Okba Harous, Saad University of Mohammed Khider Linfi Laboratory Computer Science Department Biskra Algeria College of Arts Sciences & Information Technology University of Kalba Sharjah United Arab Emirates College of Computing and Informatics University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331542573

Breast cancer is a condition brought on by the uncontrollable growth and multiplication of aberrant cells in the breast tissue. The misdiagnosis of breast cancer can result in needless or delayed therapies, mental and physical health, increased anxiety and stress, and decreased survival rates for patients. Also, the exchange of healthcare data is difficult to secure due to strict privacy laws, online threats, and the possibility of human error. Blockchain technology can enhance the security and privacy of patient data, give patients greater control over their health data, and facilitate safe data exchange between healthcare providers. In this study, we propose a Blockchain-based Ensemble Deep Learning Model method that can classify Breast Cancer either Malignant or Benign. This method is built on combining different breast cancer classification ANN models and sharing them through blockchain to use their weights to train and optimize the performance of a final model. This model achieved 98% accuracy, and showed a promising in protecting patients from fatal diagnosis mistakes. © 2024 IEEE.

关键词： Differential privacy

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