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检索条件"任意字段=2023 International Conference on Computer Engineering and Distance Learning, CEDL 2023"

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Optimizing Patient Flow and Resource Allocation in Hospitals using AI 1

Optimizing Patient Flow and Resource Allocation in Hospitals...

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1st international conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Rathore, Yogesh Sinha, Upasana Haladkar, Jaysing Pandurang Mate, Nilesh R. Bhosale, Supriya Ashok Chobe, Santoshkumar Vaman Shri Shankaracharya Institute of Professional Management and Technology Raipur Dept of Computer Science & Engineering Chhattisgarh India Dept of Computer Science & Engineering India Sant Gajanan Maharaj College of Engineering Department of Electronics and Telecommunication Engineering M.S. Mahagaon416503 India Centre for Distance and Online Education Pune India Sandip University Department of Computer Science & Engineering Nashik India Department of Computer Engineering Pune Ravet India

ISBN: (纸本)9798350330915

The present study investigates the incorporation of artificial intelligence (AI) into healthcare administration, with a specific focus on optimizing patient flow as well as allocating resources in various hospital environments. Using a descriptive research methodology with secondary data gathering, the study adopts an interpretivist philosophy and a deductive technique. The findings show that applying AI-driven techniques significantly improves patient wait times, the efficiency of resource utilization, and patient outcomes. The contrast between AI-enhanced practices and traditional procedures demonstrates how much better they are at optimizing patient flow. On the other hand, implementation-critical elements include initial investment expenses and ethical considerations. Investing in AI infrastructure, resolving algorithmic biases, including protecting data privacy are among the recommendations. Future research ought to concentrate on the scalability, long-term effects, and enhancements to AI systems for objective forecasts © 2023 IEEE.

关键词： artificial intelligence healthcare management interpretivism patient flow optimization resource allocation

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An Effective FL-CNN Based Data Securing Model for Heart Disease Prediction 6

An Effective FL-CNN Based Data Securing Model for Heart Dise...

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

作者： Sharma, Pakhi Sharma, Sanjiv KIET Group of Institutions Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350304480

Cardiovascular disease is the leading cause of death worldwide, according to the WHO. Coronary heart disease is most dangerous. 2015 saw 360,000 US heart attack deaths. Effective heart disease treatment prevents global deaths. An updated FL-CNN model improved cardiac disease diagnosis and prognosis for doctors and patients. Hospitals cannot disclose patient data for security and privacy reasons. Thus, centralizing data is hard. Federated learning can train machine learning and deep learning models using massive volumes of distributed data. On the UCI Cleveland dataset, CNN with Federated learning achieves 94.99% accuracy, while CNN with centralized learning achieves 97% accuracy. © 2023 IEEE.

关键词： Cardiovascular disease Centralized learning Convolutional Neural Network Federated learning UCI Dataset

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A learning Resource Management Model for high-enrollment Programming courses in engineering 4

A Learning Resource Management Model for high-enrollment Pro...

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4th international conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Lopez-Chila, Roberto Mora-Saltos, Nelson Cedeño-Tello, Alicia Llerena-Izquierdo, Joe Gie-Ticea Educational Innovation Group Universidad Politécnica Salesiana Guayaquil Ecuador TICAD Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador GIEDIC Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador Gieaci Researh Group Universidad Politécnica Salesiana Guayaquil Ecuador

ISBN: (纸本)9798350369694

In courses where enrollment is high, especially in courses in the first years of university study, the set of elements necessary for the curriculum implies that the same professor develops the study materials, adapts them, and integrates them into the virtual learning environment provided by the institution. Higher education institutions offer courses for a common subject each semester, which generates an administrative juncture. This paper aims to present a learning resource management model for high enrollment programming courses in engineering. It highlights the collaborative work initiated in the pandemic by a team of teachers who share academic and research interests to enhance student learning. An empirical-analytical work methodology with a quasi-experimental and longitudinal approach was developed. Four phases of work are developed in semestral periods at the Universidad Politécnica Salesiana, Guayaquil, Ecuador. From October 2019 to September 2023, in semesters numbered 55 to 63. About 240 students participate in this offer each semester. The survey technique is used in period 62, to a group of 151 students in a randomized manner to identify the set of significant activities for their learning. The results, with 95% confidence and a margin of error of 4.9%, determine that the learning activities housed in the high enrollment Programming courses have allowed students to achieve a 78% satisfaction rate for the use of activities that allow them to improve their knowledge in the topics addressed. It is concluded that having a base course for its duplication and integration of new learning elements, allow teachers to decrease the burden of configuration, integration and selection of activities, directing their efforts to create new, improve and optimize the existing ones, awakening the teacher's creativity and directing their efforts to the retention of students, as well as a belonging to worry about students at risk or with academic difficulties. © 2023 IEEE.

关键词： Students

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Federated learning-Based Network Intrusion Detection Using Deep Auto Encoder 6

Federated Learning-Based Network Intrusion Detection Using D...

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6th international conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Jeyavim Sherin, R.C. Parkavi, K. Vit University School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350394702

The progress of information technology is directly related to different elements of daily life, providing people with services that make living more comfortable. As the network infrastructure grows to support new services, it necessarily produces various weak points that cybercriminals might exploit. Building a reliable network intrusion detection system with deep learning methodologies demands a substantial dataset. In the past, the conventional approach involved collecting data for centralized learning, which was then transmitted to a central server for model training. Nevertheless, this approach raises valid concerns regarding the potential exposure of personal information contained within raw data, which could lead to legal repercussions for data providers. Consequently, this study presents a Modified FedAvg algorithm to enhance the existing FedAvg algorithm for network intrusion detection models. This modified approach maximizes the potential of federated learning, enhancing data privacy and minimizing the transmission of model weights. Employing a Deep Auto Encoder within a federated setting, we evaluated our methodology using the CICIDDoS 2019 dataset, attaining a notable accuracy rate of 98.82% in effectively identifying intrusions. © 2023 IEEE.

关键词： Network security

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Spatial-Spectral Dual-Branch Autoencoder based on Adaptive Convolution for Hyperspectral Unmixing 4

Spatial-Spectral Dual-Branch Autoencoder based on Adaptive C...

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4th international conference on computer engineering and Application, ICCEA 2023

作者： Jin, Baohua Li, Sijia Huang, Wei Zhu, Yunfei School of Computer and Communication Engineering Zhengzhou University of Light Industry ZhengZhou China

ISBN: (纸本)9798350347548

In recent years, there has been a growing interest in the use of deep learning-based hyperspectral unmixing methods. However, most existing networks only extract either spectral information or local spatial-spectral correlations of a single pixel, and their performance is often unsatisfactory. To address this issue and further extract spatial-spectral information from hyperspectral images, In this paper, a novel hyperspectral unmixing method based on spatial-spectral dual branch autoencoder and adaptive convolution is proposed. This method utilizes the autoencoder architecture with different branches designed to extract both spatial and spectral features. The extracted features are then stacked and propagated through the network, followed by mapping to obtain the abundance percentage. Additionally, We have incorporated sparsity constraint on the loss function as a way to exploit the correlation between neighboring pixels and further improve the unmixing performance. We evaluated the performance of our proposed method on two real hyperspectral datasets, and the results demonstrate significant accuracy compared to existing methods. © 2023 IEEE.

关键词： Convolution

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A Review of Convolutional Neural Network-based Approaches for Disease Detection in Plants

A Review of Convolutional Neural Network-based Approaches fo...

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2023 international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Biswas, Barsha Yadav, Rajesh Kumar Delhi Technological University Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665474511

Around 60.3% of land in India is used for agricultural purposes and the whole population depends on agriculture. That's why crop yield is very crucial to get high agricultural output. The economical loss will be very high if the agricultural output is low. So, that's why the diagnosis of disease in plants is very important. And the detection should be in the early stage not in a later stage. Using Deep learning (DL) i.e. a branch of Artificial Intelligence (AI), a farmer can detect plant diseases very easily. In Deep learning(DL), Convolutional Neural Networks (CNNs) are a cutting-edge method for image classification tasks. And Plant Disease Detection is an image classification task in which image is given as input and a class of plant disease is obtained as an output. This research study reviews the CNN-based approaches that are used to detect various diseases in plants. © 2023 IEEE.

关键词： Convolutional neural networks

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Deep learning Methods of Dynamic Sign Language Translation: A Rapid Umbrella Review 4

Deep Learning Methods of Dynamic Sign Language Translation: ...

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4th IEEE international conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bopape, Lebogang Puree Tsoeu, Mohohlo Samuel Durban University of Technology Department of Electronic and Computer Engineering South Africa Durban University of Technology Department of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Countries globally are recognizing sign languages, which are used by more than 5% of national and global populations as official languages. In Africa, South Africa was the fourth country to officialize sign language in 2023. This trend combined with advances in computer vision and machine learning has resulted in an increase of scholarly literature on the translation between spoken and signed languages to bridge the language and communication divide. This has been accompanied by a high volume of published literature reviews on the subject, most of them distinguished by time and geographical contexts. Despite numerous published review papers on the subject, to date, no formal or umbrella reviews of these reviews have been performed to consolidate the similarities, contrasts, gaps, and opportunities reported in these reviews. In this paper, we conduct a first-of-its-kind, rapid systematic umbrella review of 11 literature reviews about machine translation and recognition of dynamic sign languages using machine learning. Three lenses namely, the evolution of machine learning and computer vision technologies and resulting applications to sign language translation;geographical contexts and associated applications;and the unique linguistic context of sign languages are used. We found that most reported literature has solved the sign language translation and recognition problem from a purely data-driven perspective, without due attention to specific linguistic complexities of the languages. Consequently, we found that most literature has not paid attention to proper corpora design, model design, and meaningful performance evaluation besides the generic and inherited measures of performance from spoken language processing and computer vision literature. © 2024 IEEE.

关键词： Adversarial machine learning

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Trust-Based Geo-Social Access Control Framework: Employee Management through Machine learning Techniques 4

Trust-Based Geo-Social Access Control Framework: Employee Ma...

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4th international conference on Electrical, Communication and computer engineering, ICECCE 2023

作者： Hiremath, Priyanka C. Raju, G.T. Department of Computer Science and Engineering SJC Institute of Technology Chickballapur India

ISBN: (纸本)9798350369694

In the modern corporate sphere, organizational trustworthiness is crucial, particularly in understanding and predicting employee behavior through data science. Managing employee access levels is vital for security and privacy. Our study introduces a novel approach, utilizing Geo-Social and Trust data. Various machine learning techniques, including Linear Regression, K-Nearest Neighbours, Decision Tree, Random Forest, XGBoost, and Multi-Layered Perceptron, are employed to extract insights from geographical and social sources. This extensive dataset contains detailed geographic employee information, navigation choices, spatial exploration patterns, and choice set formations. Grounded in this blend, we establish an access control data modeling system to evaluate employee access based on trustworthiness. This model categorizes employees into low, moderate, high, and very high access levels, adapting to changing behavior. Our method fosters a cohesive, trustworthy employee network and provides a precise mechanism for assessing trustworthiness. It promises augmented organizational coherence, heightened commitment, and reduced attrition. Additionally, this approach safeguards against data breaches and privacy violations by restricting access for less trustworthy employees. In summary, our innovative access control framework, merging Geo-Social and Trust data with cutting-edge machine learning, heralds a new phase of trust, precision, and security in organizational dynamics. © 2023 IEEE.

关键词： Access control

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Relation Extraction Based on BERT and BGRU in the Chinese Music Scene 27

Relation Extraction Based on BERT and BGRU in the Chinese Mu...

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27th international conference on Knowledge Based and Intelligent Information and engineering Sytems, KES 2023

作者： Wang, Chaoguo Zhang, Liang Yan, Wei School of Information Science and Engineering Shandong Normal University Shandong Jinan250358 China Shandong University Shandong Qingdao266237 China

Due to the lack of music knowledge extraction tools and public datasets, there are relatively few studies on music entity relation extraction. The Bidirectional Encoder Representations from Transformer (BERT) model considers contextual information and provides more dynamic, richer vector representations of generated words. Bidirectional Gated Recurrent Units (BGRU) has long-distance learning capability, which can obtain rich contextual semantic feature. Therefore, this paper proposes a deep learning model based on BERT and BGRU combined with an attention mechanism for Chinese music relation extraction. Experimental results show that the proposed method obtains good results when extracting the relation between musical entities. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bidirectional Encoder Representations from Transformer(BERT) Deep learning Gated Recurrent Units (GRU) Knowledge Graph Relation Extraction

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Exploration of OBE-Based Competence Development Model for Undergraduate computer Science Students 5th

Exploration of OBE-Based Competence Development Model for Un...

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5th international conference on computer Science and Educational Informatization, CSEI 2023

作者： Luo, Juan Qiao, Ying Xiao, Degui Zhao, Huan College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819994984

The inconsistency and lack of synchronization between computer professional competence education and talents' market demand are the bottleneck problems of undergraduate education. In this paper, we establish a "student-centered" output teaching model based on OBE concept, and construct a computer core competence model based on KSD. We propose a personalized teaching system based on the knowledge graph of teaching contents and students’ learning trajectories. It continuously improves and optimizes the training plan according to students’ learning outcomes, and improves and adjusts the course contents, which achieves the cultivation of undergraduate students’ ability to solve complex engineering problems. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Education computing

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