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2023 International Conference on Electrical, computer, communications and Mechatronics engineering, ICECCME 2023

作者： Yura, Otgonbaatar Shin, Kwangcheol Zundui, Buyankhishig Huree University of Information and Communication Technology Department of Information and Communication Ulaanbaatar Mongolia Huree University of Information and Communication Technology Department of Computer Science Engineering Ulaanbaatar Mongolia The School of Information and Communication Technology Must Department of Communication Engineering Technology Ulaanbaatar Mongolia

ISBN: (纸本)9798350322972

the countries of the world are using various communication services in all areas of society on based satellite networks. The International Telecommunication Union has allocated two kind of satellite position in space segment to Mongolia that are 113.6E longitude for Fixed Satellite Service-(FSS) and 74E longitude for Broadband Satellite Service-(BSS).Mongolia is planning to launch its national satellite in 2030. Therefore, there are studying many kind of science research including estimate satellite geometry parameters, rain attenuation, cloud attenuation and satellite network architecture based on national satellite that will be launched to space *** paper is described by detail that is the mathematical modelling of satellite geometry parameters particularly antenna look angles of the ground control *** this paper, aim of research work is determined the geometrical parameters of the national satellite 113.E and 74E to be launched in Mongolia by using a mathematical model to make a comparative analysis and to show some result. © 2023 IEEE.

关键词： Network architecture

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An Image Processing-Based Statistical Method for Estimating Nutrient Deficiencies in Grape Plants During the Growing Season

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 105-116页

作者： Macriga, G. Adiline Subbiah, Sankari Sudha, G. Saranya, S. Department of Information Technology Sri Sai Ram Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Engineering College Chennai India

Agriculture plays a very important role in the provision of food surplus to expanding population, contribution to capital formation, providing raw material to industries, market for industrial products and major contribution in international trade. To enhance the quality and quantity of the agriculture product there is a need to adopt the new technology. Image processing approach is non-invasive technique which provides consistent, reasonably accurate, less time consuming and cost-effective solution for farmers to manage fertilizers and pesticides. The objective of this study is to analyze the nutrition of grape plant using Wavelength algorithmand statistical method regression analysis is used in our work for the nutrient estimation. The relative requirements of nitrogen, phosphorus and potassium in grapes vary with the growth stages of grapes. There is a high-level requirement of N during the vegetative growth stage, P requirement is high during flowering stage and K requirement is more in crop maturity stage. So pruning was carried out in two stages i.e., in April and in October. This study is carried out in grape farms of Theni District of Tamil Nadu for estimating the macro nutrition nitrogen (N), phosphorus (P) and potassium (K) to analyze the yield. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the two pruning stages. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Population statistics

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INTELLIGENT AUTONOMOUS PARKING SYSTEM INTEGRATING RFID AND IoT FOR SMART CITIES

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第2期83卷 65-77页

作者： Pushgara Rani, D. Prasanna, R. Surenderanath, S. Kalaiselvi, V.K.G. Vaishnavi, V. Rithika, P. Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Department of Information Technology Sri Sai Ram Engineering College Chennai India

An autonomous parking system is the essential innovation for stress free car parking in smart cities. The efficient use of resources is a key goal of smart cities, and their integration with technology has improved productivity and dependability for urban infrastructure. In this paper, an efficient automated parking system integrating Internet of Things (IoT) and radio frequency identification (RFID) technologies is proposed. The proposed system is integrated with IoT in which the consumers can book the parking spots before arriving to the destination—drastically reducing the time taken by the consumers in searching for the parking slot. The proposed system effectively saves time for the consumers and also significantly provides solutions for all the parking-related issues faced by the management of flats, theaters, malls, hospitals, etc. In management perspective, this system will provide better use of their resources and reduce the amount of labor needed to maintain these parking lots. The automated parking system overcomes the important issue of jamming near the gates of parking spots by improving the check-in and check-out process of vehicles. The RFID tags given to the drivers reduce the time for vehicle identification as it is done automatically. The proposed system avoids long queues at the gates, unnecessary traffic jams near the gates, reduces the waiting time, and ensures security by allowing only the registered users identified through RFID tags. Thus, the proposed system will be a sophisticated system for the consumers, drivers, and a boon to mall owners and management. © 2024 by Begell House, Inc.

关键词： Internet of things

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Visual quality-enhanced reversible data hiding scheme based on interpolation and variant Sudoku matrics

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Multimedia Tools and Applications 2025年第18期84卷 20219-20247页

作者： Yuan, Longfei Lin, Chia-Chen Chang, Chin-Chen Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung Taiwan Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan

Utilizing interpolation techniques (IT) within reversible data hiding (RDH) algorithms presents the advantage of a substantial embedding capacity. Nevertheless, prevalent algorithms often straightforwardly embed confidential data into non-reference pixels without accounting for the relationships between neighboring pixels. This practice results in suboptimal visual quality in the stego images. To rectify this issue, we have introduced the augmentation and modification interpolation (AMI). Additionally, we have devised two variant Sudoku matrices to facilitate distinct data-hiding strategies tailored for different types of reference pixels: Single type and Paired type. By incorporating these innovative designs, the interpolation operation now preserves the pattern-level information of the block in the original image, rather than focusing solely on the pixel level. In assessing the effectiveness of our proposed scheme, we performed experiments utilizing two representative image datasets. The outcomes of these experiments validate that our scheme attains an average PSNR of 44.11 dB and an average hiding capacity of 1.63 bpp. Consequently, it is affirmed that the advantage offered by our proposed scheme is well-suited for scenarios demanding high-quality stego images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Interpolation

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Performance Evaluation of Natural Language Processing Algorithms for Sentiment Analysis

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SN computer Science 2024年第6期5卷 724页

作者： Silviya, S. H. Annie Faith, S. Julia Seetha, R. Hemalatha, M. Department of Computer Science and Engineering Rajalakshmi Institute of Technology (RIT) Department of Information Technology S.A.Engineering College Department of Information Technology Vellore Institute of Technology Department of Computer Science and Engineering RMD Engineering College

Sentiment analysis can be used to identify if a text’s sentiment is neutral, positive, or negative. One type of natural language processing is sentiment analysis. An interdisciplinary field encompassing linguistics, computer science, and artificial intelligence called “natural language processing” studies how computers and human language interact, with a focus on how to teach computers to process and analyze vast volumes of natural language data. Sentiment analysis, sometimes referred to as opinion mining or emotion artificial intelligence, is the methodical identification, extraction, quantification, and study of affective states and subjective data using natural language processing, text analysis, computational linguistics, and biometrics. Sentiment analysis is frequently used in online and social media platforms, voice of the customer materials (such as reviews and survey replies), and healthcare materials for marketing, customer service, and clinical medicine purposes. This paper discusses different sentiment analysis methods and selects the best approach for a certain task. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Biometrics Computational linguistics Natural language processing Sentiment analysis Text analysis

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Abnormal behaviors recognition in crowd environments based on semi-supervised deep learning and hierarchical approach

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Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3925-3943页

作者： Asl, Vahid Fazel Karasfi, Babak Masoumi, Behrooz Keyvanpour, Mohammad Reza Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Computer Engineering Alzahra University Tehran Iran

This paper tackles the complexity of detecting abnormal behavior by focusing on temporal patterns, such as social force and optical flow, rather than spatial patterns. The CycleGAN system trains on normal behaviors, ensuring that abnormal behaviors are not reproduced during testing. Hierarchical supervised learning and transfer learning techniques are applied to recognize abnormal behaviors. Given the scarcity of abnormal patterns, geometric techniques are employed to augment these patterns, followed by training with temporal models. Experimental results on the UCSD and UMN datasets demonstrate that the proposed method significantly outperforms existing approaches, achieving average AUCs of 99.90% on Ped1, 99.89% on Ped2, and 99.82% on UMN. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9043-9055页

作者： Mohammed Mustafa, M. Kalpana Devi, S. Althaf Ali, A. Gunavathie, M.A. Department of Information Technology Sri Krishna College of Engineering & Technology Coimbatore India Department of Computer Science and Engineering RV University Bengaluru India Andhra Pradesh India Department of Information Technology Panimalar Engineering College Chennai India

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. © 2024 – IOS Press.

关键词： Location

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Verification of news and certificate, and plagiarism detector

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Multimedia Tools and Applications 2025年 1-28页

作者： Jain, Pushpak Anand, Dewansh Rajni Behal, Vansh Kapoor, Vipul Sharma, Neha Jindal, Neeru Department of Electronics and Communication Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Patiala India

Fake news, Fake certification, and Plagiarism are the most common issues arising these days. During this COVID-19 situation, there are a lot of rumors and fake news spreading and some of us are using fake certification to avoid vaccination and plagiarism became the most common topic during online studies. It is found that several apps and websites are present for the detection of false news and plagiarism. However, there is no single website for the detection of fake certificates. Moreover, nothing is made that consists of all these features in one platform. So, this proposed work develops a website for detecting fake news, Fake certificate, and plagiarism detector. The proposed method uses Natural Language Processing techniques for detecting NPC (news, plagiarism, and certificate). The proposed work uses image processing with some Python libraries like OpenCV in all the detections and that makes it quite interesting. For detecting NPC (news, plagiarism, and certificate) the algorithm is built using a k-mean model, logistic regression, and neural network. The proposed work uses the datasets that are trained using the count vectorizer method for the detection and to get its accuracy which will be tested using machine learning algorithms. The proposed method has shown an accuracy rate of 94.28% on Logistics Regression in comparison to existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fake news Machine learning Natural language processing Plagiarism Social media platform

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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