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2nd IEEE International Conference on New Technologies of information and Communication, NTIC 2022

作者： Houda Ouamane, Nour El Belhadef, Hacene University of Mila Institute of Sciences and Technology Department of Mathematics and Computer Science Mila43000 Algeria University of Constantine 2 Faculty of New Technologies of Information and Communication Department of Computer Science and Its Applications Constantine Algeria

ISBN: (纸本)9781665489744

Natural Language Processing represents the domain at the intersection of computer science, artificial intelligence, and linguistics. It consists of creating computer programs that can process and understand human language. Recently, many researchers who have investigated and used them to solve different obstacles have focused their emphasis on reinforcement learning and deep learning. By examining the benefits and disadvantages of the research in the nexus of deep learning and reinforcement learning, we examine the potential for using deep reinforcement learning algorithms for natural language processing in this work. © 2022 IEEE.

关键词： Reinforcement learning

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Challenges of Extended Reality technology in Higher Education: A Review

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第14期18卷 39-50页

作者： Obeidallah, Randa Al Ahmad, Ayat Qutishat, Duha Department of Computer Information System Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science and Applications Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan

Extended reality is proposed as a means to deliver qualitative material to students in higher education institutions. The usage of this technology has increased due to Covid-19 lock down. This paper aims to categorize the challenges encountered by literature that deployed extended reality (XR) in higher education during and after the lockdown. However, the number of available articles addressing these challenges is limited, with a total number of 40 articles obtained from dimensions’ dataset. In this study, bibliometric analysis is used to highlight the number of publications, citations, and authors in this field. Thematic analysis with inductive reasoning is applied to extract the primary challenge themes mentioned in 23 articles. After reviewing the eligible articles;we categorized the challenges into three main themes: Teacher, Student, and technology. © 2023 by the authors of this article. Published under CC-BY.

关键词： Augmented reality

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RAC-Based One-Stop Professional Course Teaching Model Exploration and Practice - Taking the Course of "Microcomputer Principles and Interface technology" as an Example 6th

RAC-Based One-Stop Professional Course Teaching Model Explor...

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6th International Conference on computer Science and Educational Informatization, CSEI 2024

作者： Wan, Xiaorong Li, Yingna Luo, Chuan Li, Chuan You, Jinguo Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming650500 China

ISBN: (纸本)9789819637379

In the context of the country’s requirement for academic disciplines to be aligned and mutually reinforcing with industry chains, innovation chains, and talent chains, the integration of education and industry has become an important direction for the reform of higher education. In response to the iterative demand for edge intelligence in the Intelligent Internet of Things (AIOT), the course "Microcomputer Principles and Interface technology" is deeply integrated with industry and education, establishing a hierarchical model of resource, capability, and commercialization (RAC) layers. It adopts a "one-stop" teaching mainline that starts with microcomputer theory knowledge, relies on hardware development boards, project orientation, competition innovation, and product commercialization training "five points and one line", connects knowledge product channels, integrates ideological and political education in courses, and constructs a multi-dimensional and comprehensive evaluation system to achieve the integration of industry and education from universities to enterprises, classrooms to markets, and from knowledge to products. The innovative teaching mode of this course has achieved significant results in innovative practice. This innovative teaching mode can effectively achieve the knowledge, ability, and quality goals of cultivating new engineering talents, and effectively improve the quality of talent cultivation and teaching. This teaching reform and innovation provide a reference and idea for further exploring the talent cultivation mode of schools and enterprises. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Teaching

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Wideband Reconfigurable Instantaneous Microwave Multi-Frequency Measurement System Without Mirror Image Blurring Based on an Optical Frequency Shifter and Optical Frequency Comb

SSRN

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SSRN 2024年

作者： Zhang, Jiahong Ji, Qian Zhang, Jing Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650500 China

In this paper, a wideband reconfigurable instantaneous microwave multi-frequency measurement system without mirror image blurring has been proposed, designed and investigated. In this proposed scheme, an optical frequency shift is employed to shift the optical carrier of the input dual-parallel Mach-Zehnder modulator (DPMZM), aligning it with the first comb line of the optical frequency comb. Simultaneously, the central frequencies of the two demultiplexers are designed to ensure that the optical signals output after coupling through a 90° optical hybrid coupler contain only one local oscillator optical carrier. Furthermore, the local oscillator optical carrier is positioned at the initial location of the channel. This configuration allows for the determination of an unknown signal with only one beat frequency, effectively resolving the issue of ambiguous mirror frequencies. Simulation experiments reveal that when the channel bandwidth is set to 2 GHz, the system frequency measurement range is 0.01-50 GHz, with an error of less than ±5 MHz. By adjusting the channel bandwidth and the spacing of the optical frequency comb, the system measurement range can be extended to 0.01-100 GHz, with a frequency measurement error of 5-14.6 MHz. This approach offers advantages such as a wide measurement range, reconfigurability, and low error. © 2024, The Authors. All rights reserved.

关键词： Mirrors

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NextGen Robot: An Efficient and Effortless Wall Painting Robot Design Based on Artificial Intelligence (AI) Principles

NextGen Robot: An Efficient and Effortless Wall Painting Rob...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Mary, S. Jeya Sajithra, S. Divya, G. Lakshmi, N. Giri, Pallavi Al-Qawasmi, K. Einstein College of Engineering Department of Ece Tirunelveli India R.M.K. Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India S.A. Engineering College Department of Master of Computer Applications Chennai India Laxminarayan Innovation Technological University Department of Civil engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

A fully autonomous wall-painting robot is the focus of this paper's development, expansion, and implementation efforts. Research efforts have not communicated much about interior wall painting. Painters' eyes and respiratory systems are particularly vulnerable to the toxic chemicals used in the industry. Additionally, the monotony, time commitment, and physical exertion inherent in the painting process are hard to resist. The entire construction process may be enhanced and time and labor saved by using robots and human workers appropriately into building jobs. The Dynamic Smart Painting Robo Model (DSPRM) is a novel robot that this study introduces for intense wall painting. To test its efficacy, it is crossvalidated with the usual technique, the usual Painting Bot (CPB). Furthermore, it would provide an opportunity to lessen or eliminate human exposure to challenging and dangerous conditions, which would address most of the issues, provide protection, and make it happen more often. An autonomous robotic painting system is being developed as a result of the wall painting robot. © 2024 IEEE.

关键词： Machine design

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A Hybrid GRU-Transformer Model for Financial Forecasting and Risk Management 3

A Hybrid GRU-Transformer Model for Financial Forecasting and...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Pradhan, Rahul Alazzam, Malik Bader Keswani, Sarika Bhasin, Narinder Kumar Jaff, Nadhema Ahmed Muthuperumal, S. Gla University Department of Computer Engineering & Applications Mathura India Jadara University Faculty of Information Technology Irbid Jordan Pune India Indian Institute of Banking and Finance Mumbai India College of Administrative and Financial Sciences Knowledge University Department of Business Administration Erbil Iraq K.Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798331508456

Financial sector demands precise forecasting models to mitigate risk and optimize asset management. This study presents a novel Hybrid GRU-Transformer Model that integrates Gated Recurrent Units (GRUs) for capturing local temporal patterns and the Transformer T5 architecture for modeling global dependencies. Financial datasets sourced from Kaggle, including stock prices and trading volumes, were preprocessed through normalization and categorical encoding, followed by an 80:20 dataset split for training and testing. The proposed model outperformed existing methods such as ARFIMA-LSTM and EMD-CNN-LSTM, achieving superior metrics, including a Mean Squared Error of 0.08 and an R2 of 0.95. Results underline the model's accuracy and robustness in predicting financial risks and trends. By enhancing predictive performance and risk management strategies, this model addresses the dynamic complexities of financial markets. Future research will explore its adaptability across diverse instruments and markets to further its application in real-time forecasting scenarios. The system is implemented using Python integrated with MATLAB. © 2025 IEEE.

关键词： Risk management

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RDI-SD: An Efficient Rice Disease Identification based on Apache Spark and Deep Learning Technique

RDI-SD: An Efficient Rice Disease Identification based on Ap...

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2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

作者： Prasad, M S Guru Pratap, M.S. Jain, Prithviraj Gujjar, J Praveen Kumar, M Anand Kukreti, Anurag Dept. of Computer Science & Engg Dehradun India School of Computing and Information Technology Reva University Bengaluru India SDM Institute of Technology Dept. of Computer Science & Engg Ujire India Faculty of Management Studies Bengaluru India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665493048

Early and accurate detection of diseases affecting rice plants may have devastating impacts on productivity, and early and accurate detection of these diseases is essential for mitigating their consequences. However, the current methods for diagnosing rice diseases are neither precise nor effective. When it comes to diagnosing plant diseases, convolutional neural networks (CNNs) are the gold standard. The difficulty of using CNN for large-scale data analysis using conventional methods remains a major barrier to its wider adoption. In our work for massive data processing, we have adapted the CNN technique for use with pySpark. We used a dataset of 3,472 images of rice diseases to train and evaluate our suggested system. The proposed system will provide a tailored answer to the challenge of assessing rice diseases by employing smartphone plant images as its major data source. With the proposed model for identifying different rice diseases, a total accuracy of 99% was reached, which is pretty impressive given that some of the diseases look similar. © 2022 IEEE.

关键词： Convolutional neural networks

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Deployment of neural networks for WLAN thumbprint indoor positioning system

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International Journal of information technology (Singapore) 2025年 1-7页

作者： Kushwaha, Ajay Agarwal, Trapty Singh, Manpreet MP, Sunil Sharma, Amit Jain, Romil Bhuvaneswari, P. Computer Science and Engineering Rungta College of Engineering and Technology Chhatisgarh Bhilai India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Centre of Research Impact and Outcome Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology Jain (Deemed-to-be University) Karnataka Bangalore India School of Computer Applications Lovely Professional University Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Chandigarh India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

With the application and development of related technologies based on user location information, location-based services have become essential for daily lives. Despite this fact, Wireless Local area networks (WLAN’s) often face time complexities in streamlining and collection of data. Thus, the given paper introduces a deep-learning model based on convolutional neural network (CNN) to predict user’s indoor location from the received wireless signal strength fingerprint maps. The aim is to enhance WLAN indoor localization by streamlining the data collection process. The proposed indoor positioning model leverages a region of interest (ROI) extraction technique to simulate the wireless received signal strength intensity in WLAN’s. By incorporating this advanced technique, the model aims to improve the accuracy and efficiency of WLAN localization. To optimize and evaluate the model’s performance in real-world scenarios, experiments are conducted using simulated datasets obtained from the publicly available database such as UCI repository. The results show that the proposed model attains the highest validation accuracy (97.11%) and training accuracy (97.34%) as compared to the existing deep learning algorithms used for indoor localization. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： CNN Deep learning Indoor localization Region of interest (ROI) Signal strength WLAN

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An Examination of Cybersecurity Threats and Authentication Systems 3

An Examination of Cybersecurity Threats and Authentication S...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Shakeel, Mohammed Lakshmana Rao, Chintala Shyam Prasad, T. Alam, Tanweer Rawat, Navneet Kavitha, R. Invertis University Faculty of Computer Applications Bareilly India Deemed to Be University School of Law Gitam Visakhapatnam India Anurag University Department of Cse Telangana Hyderabad India Islamic University of Madinah Faculty of Computer and Information Systems Department of Computer Science Madinah Saudi Arabia Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode Sathyamangalam638401 India

ISBN: (纸本)9798350399264

Each and every nation in the globe has very crucial infrastructures that offer crucial services like internet activity, energy, banking and finance, crucial public services, transportation, and water management. For vital infrastructure based on sectors, each nation has a unique strategy. As IoT-based solutions proliferate, these crucial infrastructures now have network and Internet connectivity. Consequently, these crucial systems that are a part of information networks are equally vulnerable to online attacks. It is crucial to recognise the potential cyber-attack types, devise defence strategies, and implement various preventative measures. In the present day, it is critical to protect potential cyberattacks on these vital infrastructures. The most common attacks on crucial infrastructures are looked at in this study, especially those that have recently happened. Security precautions are also discussed with a view to reducing or preventing IP-based intrusions. The Internet of Things is the next step in machine-to-machine connectivity. IoT has made it possible for anything to be online nowadays. IoT also refers to the networking of uniquely identified, pervasive computing devices that have the ability to transmit data over a network without necessarily requiring or machine-to-human interaction. © 2023 IEEE.

关键词： Cybersecurity

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

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