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Educational AI and Ethical Growth: Exploring the effects of ChatGPT on student learning strategies, critical thinking, and academic ethics from a Bangladeshi academic perspective 26

Educational AI and Ethical Growth: Exploring the effects of ...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Khan, Md Mizanur Rahman Riad Bin Habib, Showrab Tasnim, Syeda Tasfia Islam, Md Ariful East West University Dept. of Computer Science and Engineering Aftabnagar Dhaka1212 Bangladesh GD Goenka University School of Engineering and Science Goenka Education Haryana 122103 India

ISBN: (纸本)9798350359015

As the pace of technological progress quickens, artificial intelligence (AI) is rising to the forefront as a potentially game-changing breakthrough. The proliferation of AI tools like ChatGPT has significantly helped local communities everywhere. ChatGPT is one of the most widely used AI technologies, and its widespread adoption across a wide range of applications has given it a major impact on people's everyday lives. However, there are both good and bad outcomes associated with how often students use ChatGPT. The aim of this paper is to examine how using ChatGPT can affect students while they are learning. An assessment is carried out by administering a survey to the students and conducting interviews with the instructors, both of which help to pinpoint areas in which the students could use additional help. The findings of this study provide new insight into the effects of using ChatGPT in classrooms and provide a blueprint for initiatives to improve learning outcomes for students. © 2023 IEEE.

关键词： artificial intelligence chatbot chatgpt gpt-4 open-ai

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Techniques for Creating Synthetic Combined Electric and Natural Gas Transmission Grids

Techniques for Creating Synthetic Combined Electric and Natu...

引用

2022 IEEE Texas Power and Energy Conference, TPEC 2022

作者： Abu-Khalifa, Yousef A. Birchfield, Adam B. Texas Am University Dept. of Computer and Electrical Engineering College StationTX United States

ISBN: (纸本)9781665479028

This paper presents a methodology for the creation of a synthetic combined electric and natural gas transmission network, along with representative benchmark results. The systems do not contain actual, confidential network data, but are synthetic, meaning they are built to capture the behavior of a combined network that is geographically constrained. First, natural gas loads are placed in a selected area. Work already done in building synthetic electric grids aids in this process, where the natural gas-powered generators are modeled as loads in the natural gas system. Publicly available data is then used to place the remaining gas loads and the gathering plant. Next, a method is introduced to construct a pipeline network connecting the loads and gathering plant, which acts as the source. The combined electric-gas system is then solved for the nodal pressures, pipeline flow rates, and electric state variables. A 47-node gas test case with 23 loads and 46 pipelines is built and solved in combination with a 173-bus electric system, designed to aid with developing and validating analysis techniques for combined electric-gas systems. © 2022 IEEE.

关键词： Natural gas

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ACNNE: An Adaptive Convolution Engine for CNNs Acceleration Exploiting Partial Reconfiguration on FPGAs

ACNNE: An Adaptive Convolution Engine for CNNs Acceleration ...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Chun-Hsian Huang Shao-Wei Tang Pao-Ann Hsiung Dept. of Computer Science and Information Engineering National Taitung University Taiwan Dept. of Computer Science and Information Engineering National Chung Cheng University Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

In this work, we propose a dynamical function exchange Convolutional Neural Networks (CNN) accelerator architecture named Adaptive CNN engine (ACNNE) that can reconfigure specific convolution layer hardware blocks according to model parameters at runtime. We mainly focus on exploiting reconfigurability for inferencing large-scale CNN on resource- constrained FPGAs. The proposed ACNNE can accelerate the process of convolution layers based on a nested-loop algorithm while a data buffering scheme is presented to reduce the iterations of memory accesses. As a study case, the VGG16 model was implemented on Xilinx ZU3EG MPSoC that can achieve 21.09 giga operations per second in the frequency of 100 MHz with a device resource utilization of 25% to 35%. Experiments show a single-image inference can be completed in 2.5 seconds with an average power consumption of 2.23 W, corresponding to a power efficiency of 9.46 GOPS/W.

关键词： Adaptation models Runtime Power demand Convolution Convolutional neural networks Resource management Logic

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Incremental Conductance MPPT Optimization System Modeling using PID Control with Fuzzy Gain Scheduling 5

Incremental Conductance MPPT Optimization System Modeling us...

引用

5th IEEE International Conference on DC Microgrids, ICDCM 2023

作者： Mumtaz, Wildan Sheina, Avy Hasan, Hafidh Geumpana, Teuku Aulia Roslidar, Roslidar Universitas Syiah Kuala Dept. of Electrical and Computer Engineering Banda Aceh Indonesia Auckland University of Technology Dept. of Electrical and Electronic Enegineering Auckland New Zealand School of Information and Physical Sciences University of Newcastle Newcastle Australia

ISBN: (纸本)9798350321272

Solar irradiation fluctuations can shift the PV module's operating point away from its maximum power point (MPP). To maintain the MPP, the widely used incremental conductance (IC) method is employed as part of the maximum power point tracking (MPPT) technique for PV systems due to its simplicity. In this study, authors enhance the MPPT IC method by integrating a PID control scheme with fuzzy gain scheduling (FGS). Using simulations in Simulink with irradiation variations between 800-1000-800 W/m2, this study observes that the MPPT IC-PID system with FGS outperforms the conventional MPPT IC system. It significantly reduces steady-state oscillations from 5.79% to just 0.22% and shortens the average response time from 12.15ms to 1.06ms. These improvements result in increased energy production (up to 23.36 Ws) and reduced tracking error (by up to 1.05%), compared to the conventional MPPT IC system's 2.297 Ws and 9.43% tracking error. © 2023 IEEE.

关键词： Three term control systems

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Optimizing Photovoltaic System Efficiency: A Comparative Study of RNN-based Controller and Incremental Conductance in MPPT

Optimizing Photovoltaic System Efficiency: A Comparative Stu...

引用

IEEE Conference Photovoltaic Specialists (PVSC)

作者： Babatunde D. Soyoye Indranil Bhattacharya Mary Vinolisha Trapa Banik Electrical and Computer Engineering dept. of Tennessee Tech University Cookeville TN USA Antony Dhason Electrical and Computer Engineering dept. of Tennessee Tech University Cookeville TN USA

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

In this study, a Recurrent Neural Network (RNN)- based PV controller is thoroughly examined and compared with conventional Incremental Conductance techniques for Maximum Power Point Tracking (MPPT). The study emphasizes the improved responsiveness and efficiency of the RNN controller and focuses on the dynamic adaptation of PV systems to changing environmental conditions. In this study, the better performance of the RNN controller in monitoring the maximum power point, its fast convergence time, and its stability under various environmental conditions are demonstrated by comprehensive simulations and comparative studies. According to the results, using cutting-edge machine learning methods, such as RNNs, may greatly increase PV systems' operational effectiveness, highlighting their potential to optimize renewable energy systems. This study offers insights that advance the realm of renewable energy technologies.

关键词： Maximum power point trackers Photovoltaic systems Renewable energy sources Recurrent neural networks Machine learning algorithms Heuristic algorithms Machine learning Stability analysis Real-time systems Monitoring

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Next Generation HR: Transforming Talent Management with AI and ML Innovations 2

Next Generation HR: Transforming Talent Management with AI a...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Niharika Singh, Pooja Chakraborty, Sudeshna Gl Bajaj Institute of Technology & Management Dept. of Master of Computer Application U.P. Greater Noida India School of Computing Science & Engineering Galgotias University Department of Cse Greater Noida India School of Computing Science and Engineering Galgotias University Department of Computer Application Greater Noida India

ISBN: (纸本)9798350371055

Artificial intelligence (AI)-based technology is now accepted as standard. Today, everything is driven by AI, which has changed our way of life. The widespread deployment of AI is helping businesses and companies by streamlining processes, increasing production, increasing effectiveness while cutting expenditures. The way businesses choose, manage, and engage their personnel is evolving as a result of the integration of artificial intelligence (AI) into HRM operations. simulated intelligence (AI) is allowing robots to make judgements that are more accurate than those made by people based on current data sets and behavioral patterns. As a result of this change, machines have taken the place of all manual labor. professionals now play more strategic roles. It is critical for businesses and professionals to comprehend how this technology operates and how it contributes to a variety of HRM activities. In order to determine how AI is bringing about change in the field a study of human resource management evaluates the work of numerous notable scholars. This review outlines the main advantages and unspoken difficulties of The use of AI in HRM and its potential in the future. This essay provides an in-dept. analysis. of the utilization of AI and ML techniques in HRM, highlighting their impacts on recruitment, employee performance management, training, and overall organizational success. © 2024 IEEE.

关键词： Artificial intelligence human resource management machine learning Natural Language Processing

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A Dual-Band Leaky Wave Antenna Design for K-Band and W-Band

A Dual-Band Leaky Wave Antenna Design for K-Band and W-Band

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2024 IEEE Research and Applications of Photonics in Defense Conference, RAPID 2024

作者： Willis, Andrew Zhang, Jincheng Louisos, Dustin Hofmann, Tino Touma, Jimmy E. University of North Carolina at Charlotte Electrical and Computer Engineering Dept. CharlotteNC28025 United States University of North Carolina at Charlotte Dept. of Physics and Optical Science CharlotteNC28025 United States Air Force Research Laboratory Eglin AFBFL32542 United States

A bi-directional dual-band meta-surface antenna designed to operate at 20 GHz in the longitudinal direction and 80 GHz in the transverse direction is described and simulation results are presented. © 2024 IEEE.

ISBN: (纸本)9798350373097

关键词： Microwave antennas

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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Data Visualization for Corona Patients Globally using Real-Time APIs 2

Data Visualization for Corona Patients Globally using Real-T...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Durai, S. Iqbal, M. Mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVID-19

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Predictive Modeling of Battery Second-Life Performance Using Support Vector Regression and Voltage Features 2

Predictive Modeling of Battery Second-Life Performance Using...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Hossian, Md. Ismail Hannan, Nasif Das, Hridoy Rahman Akash, Md. Ashiqur Ashraf, Md Sazid Shufian, Abu American International University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Texas Tech University Dept. of Electrical and Computer Engineering LubbockTX United States Lamar University Dept. of Electrical and Computer Engineering BeaumontTX United States

ISBN: (纸本)9798331506995

Extensively used in electric vehicles (EVs), lithium-ion (Li-ion) batteries, undergo significant degradation after several charge-discharge cycles, leading to their retirement from high-demand applications. However, these batteries can still serve fewer demanding applications in their second life. Estimating the State of Health (SoH) in second-life applications is crucial for ensuring their reliable performance. This paper presents a method for SoH estimation using a Support Vector Regression (SVR) model trained in first-life battery data and applied to second-life usage. Two health indicators, TIECVD (Time Interval of Equal Charging Voltage Difference) and TIEDVD (Time Interval of Equal Discharging Voltage Difference) are extracted from voltage measurements and used as predictors in the SVR model. The method is validated using NASA's battery ageing dataset, consisting of three batteries (B0005, B0006, and B0007) with a rated capacity of 2 Ah. The proposed model achieves RMSE values of 0.0061, 0.0158, and 0.0047 for the respective batteries during second life SoH estimation. This approach provides a practical, low-cost solution for estimating battery health in second-life applications without extensive monitoring systems. © 2025 IEEE.

关键词： Lithium-ion batteries

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