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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Aseem Baranwal Alexander Semenov Patricia De Oliveira Salgado Karen B. Priola Yingwei Yao Gail M. Keenan Tamara G. R. Macieira Department of Computer & Information Science & Engineering University of Florida Gainesville USA Department of Industrial and Systems Engineering University of Florida Gainesville USA Department of Medicine and Nursing Federal University of Vicosa Vicosa Brazil Department of Family Community and Health System Science University of Florida College of Nursing Gainesville USA Department of Biobehavioral Nursing Science University of Florida College of Nursing Gainesville USA

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Care plans documented by nurses in electronic health records (EHR) are a rich source of data to generate knowledge and measure the impact of nursing care. However, these structured data are often documented using non- standardized local terms, which hinders interoperability and wide- scale analysis of these data. In this paper, we compared the performance of OpenAl's Generative Pre-Trained Transformer models (GPT -3.5 and GPT -4), accessed through the ChatGPT web application and Application Programming Interface (API) platforms, in mapping local care plan terms to standardized nursing terms. Our results demonstrate that the GPT -4 API model outperformed others by minimizing hallucinations, offering enhanced accuracy, and yielding a higher percentage of matched terms. GPT models are valuable tools in advancing the standardization and interoperability of nursing data.

关键词： Accuracy Medical services Standardization Transformers Chatbots Data models Task analysis

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

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Advancements in Chatbot Technology: Enhancing User Experience through AI and NL

Advancements in Chatbot Technology: Enhancing User Experienc...

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Yojna Arora Ajeet Singh Abhay Sharma Nitin Aggarwal Nidhi Bansal Department of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad Uttar Pradesh India Department of IoT & IS School of Computing and Intelligent System Manipal University Jaipur Jaipur Rajasthan Supervisor Oracle Development Database Admin Data Science PA USA Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering information, assisting with purchasing products, and enhancing customer service. In my paper, I present the fundamental principles and essential concepts of computing related to chatbots and its applications various sectors like Education, E commerce, Entertainment, Healthcare, Telecommunication, Customer Service etc. The paper also mentions detailed literature explain the contribution by various researchers in this fields. Architecture and step wise approach of creating a chatbot is presented lately.

关键词： Quantum computing Customer services Education Medical services computer architecture Chatbots User experience Software Artificial intelligence systematic literature review

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Last-X-Generation Archiving Strategy for Multi-Objective Evolutionary Algorithms

Last-X-Generation Archiving Strategy for Multi-Objective Evo...

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Congress on Evolutionary Computation

作者： Tianye Shu Yang Nan Ke Shang Hisao Ishibuchi Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

For evolutionary multi-objective optimization algorithms (EMOAs), an external archive can be utilized for saving good solutions found throughout the evolutionary process. Recent studies showed that a solution set selected from an external archive is usually superior to the final population. That is, the incorporation of an external archive improves the performance of EMOAs. However, the computation time for maintaining an external archive is long, especially when the archive size is large. To solve this issue, a simple archiving strategy is to save all solutions generated in the last several generations. In this paper, we examine this archiving strategy for three representative EMOAs on artificial test problems (Minus-DTLZ and WFG) and real-world problems (RE). Our results show that archiving the last several generations clearly improves the performance of EMOAs without severely increasing the computation time.

关键词： Costs Sociology Evolutionary computation Statistics Optimization Standards

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Prescribing Decision Conservativeness in Two-Stage Power Markets: A Distributionally Robust End-to-End Approach

arXiv

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arXiv 2024年

作者： Liang, Zhirui Li, Qi Liu, Anqi Dvorkin, Yury Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Applied Mathematics and Statistics Johns Hopkins University BaltimoreMD United States Department of Computer Science Johns Hopkins University BaltimoreMD United States Department of Civil and System Engineering Johns Hopkins University BaltimoreMD United States

This paper presents an end-to-end framework for calibrating wind power forecast models to minimize operational costs in two-stage power markets, where the first stage involves a distributionally robust optimal power flow (DR-OPF) model. Unlike traditional methods that adjust forecast parameters and uncertainty quantification (UQ) separately, this framework jointly optimizes both the forecast model parameters and the decision conservativeness, which determines the size of the ambiguity set in the DR-OPF model. The framework aligns UQ with actual uncertainty realizations by directly optimizing downstream operational costs, a process referred to as costoriented calibration. The calibration is achieved using a gradient descent approach. To enable efficient differentiation, the DROPF problem is reformulated into a convex form, and the Envelope Theorem is leveraged to simplify gradient derivation in the two-stage setting. Additionally, the framework supports distributed implementation, enhancing data privacy and reducing computational overhead. By proactively calibrating forecast parameters and prescribing optimal decision conservativeness, the framework significantly enhances cost efficiency and reliability in power system operations. Numerical experiments on an IEEE 5-bus system demonstrate the effectiveness and efficiency of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Wind forecasting

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Interactive Learning Through the Metaverse and its Impact on Primary Education

Interactive Learning Through the Metaverse and its Impact on...

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International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON

作者： Cheilsi Sharma Basant Agarwal Lunchakorn Wuttisittikulkij Daksh Joshi Akshat Bhatnagar Sushank Chaudhary Pruk Sasithong Dept. of Computer Science Rajasthan University Jaipur India Department of Computer Science and Engineering Central University of Rajasthan Kishangarh India Telecommunication System Research Laboratory Chulalongkorn University Bangkok Thailand Bangkok Thailand Department of CSE Indian Institute of Information Technology Kota (IIIT Kota) Kota India Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

The rise of the Metaverse, an immersive, three-dimensional digital environment, has been gradually transforming human-computer interaction. This paper focuses on exploring the potential of the Metaverse to improve learning through interactive games. The Metaverse serves as a dynamic platform for students to collaborate, explore and engage with. It enables educators to create interactive learning environments that amalgamate both physical and virtual spaces through technologies such as virtual reality, augmented reality, and artificial intelligence. These interactive games within the Metaverse foster critical thinking and problem-solving on top of making learning enjoyable. This paper discusses the development of educational environments within the metaverse, the integration of interactive games as well as their impact on learning outcomes and student engagement. The Metaverse poses a novel route towards effective education with this gamified approach. This study highlights the potential for the Metaverse to prepare students for the challenges inherent in this digital age by transforming traditional educational methods.

关键词： Metaverse Space technology Education Mixed reality Games Learning (artificial intelligence) Telecommunications

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Designing of a Solar MPPT Controller with the Technique of Differentiation Between Uniform Irradiance and Partial Shading Conditions 9

Designing of a Solar MPPT Controller with the Technique of D...

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9th IEEE International Conference on Power and Energy, PECon 2022

作者： Ahmed, Sajib Mekhilef, Saad Mubin, Marizan Tey, Kok Soon Rawa, Muhyaddin University of Malaya Power Electronics and Renewable Energy Research Laboratory Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia School of Science Computing and Engineering Technologies Swinburne University of Technology HawthornVIC3122 Australia University of Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah21589 Saudi Arabia

ISBN: (数字)9781665409902

ISBN: (纸本)9781665409902

Solar energy is considered one of the best essential energy sources to solve the present power crisis. The solar photovoltaic system has a low conversion efficiency that inflicts maximum power extractions. The non-linear relationship between the voltage and current insists on using a maximum power point tracking (MPPT) controller. Perturb and observe (P&O) MPPT controller is popular because of its simple design and cost-effectiveness features;nonetheless, it has drawbacks while operating under partial shading conditions (PSC). The maximum power trapezium (MPT) method modified the P&O algorithm by incorporating an appropriate scanning mechanism for PSC. Despite having good PSC performance, this method's tracking time is much longer than the conventional algorithm in both uniform irradiance conditions (UIC) and PSC. The algorithm proposed in this paper modifies the MPT method with an extra strategy to distinguish between the UIC and PSC. If the pattern is recognized as UIC, the proposed method utilizes the conventional P&O algorithm to track the MPP;otherwise, scanning techniques are being used. The proposed algorithm's operation was tested on simulation using distinct UIC and PSC patterns and worked effectively. © 2022 IEEE.

关键词： Cost effectiveness

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things 11

Intrusion Detection using Deep Neural Network Algorithm on t...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Ikhwan, Syariful Wibowo, Adi Warsito, Budi School of Postgraduate Studies Diponegoro University Doctoral Program of Information System Semarang Indonesia Institut Teknologi Telkom Purwokerto D3 Telecommunication Engineering Banyumas Indonesia Diponegoro University Computer Science Informatics Department of Science and Mathematics Faculty Semarang Indonesia Diponegoro University Statistics Department of Science and Mathematics Faculty Semarang Indonesia

ISBN: (纸本)9781665460309

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small. © 2022 IEEE.

关键词： Internet of things

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Two-layer ensemble prediction of students’ performance using learning behavior and domain knowledge

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computers and Education: Artificial Intelligence 2023年 5卷

作者： Priyambada, Satrio Adi Usagawa, Tsuyoshi ER, Mahendrawathi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Information System Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

The ability to predict students' performance is important not only for the students but also for academic stakeholders in higher education institutes. Predictions can be made by using data stored in an academic information system on students' behavior related to taking courses that are an important part of a higher education institute with a coherent vertical curriculum. A student's course-taking behavior can be used as an indicator of their potential performance by investigating the alignment of their course-taking activities with curriculum guidelines. Domain knowledge is also considered as a variable due to the varying compositions of courses in curriculum guidelines. Past performance also needs to be taken into consideration. The result of the prediction can be used to help academic stakeholders take actions such as intervening to ensuring that students graduate on time. In this paper, we propose a two-layer ensemble learning technique that combines ensemble learning and ensemble-based progressive prediction and it utilizes students' learning behavior data and domain knowledge for current and past performances. The results show that the accuracy of our proposed framework on a real-world student dataset is improved. © 2023 The Authors

关键词： Educational data mining Ensemble learning Learning behavior Students' performance

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Lightweight Authentication of Devices in Industrial Control systems

Lightweight Authentication of Devices in Industrial Control ...

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IEEE International Conference on Software engineering and Service sciences (ICSESS)

作者： Long Wang Siqi Niu Henggao Zhai Jing Fan Lei Wang National Computer System Engineering Research Institute of China Beijing China Department of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Coocaa Network Technology Co. Ltd. Guangdong China

A security authentication protocol based on number theory research unit (NTRU) for industrial control devices is proposed to address the security of data transmission in industrial control systems and the lack of resistance to attacks of security authentication protocols. Most of the original protocols contain complex encryption and decryption operations, which are difficult to run on industrial control devices with simple structures and cannot resist new types of network attacks. In this paper, the protocol takes advantage of the simple operation of NTRU public key encryption system and sets the authorization center to send only the public key of the device in a variant form to achieve the purpose of resisting new attacks such as quantum attacks and internal attacks, and the anti-attack capability is strengthened.

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