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Quantitative Trends Analysis on Russian Literature Research in Korean Academia: Insights from KCI Data 10

Quantitative Trends Analysis on Russian Literature Research ...

引用

10th International Conference on Platform technology and Service, PlatCon 2024

作者： Iosifovich, Ten Alexander Yeo, Sang-Soo Jung, Young-Ae Dept. of Computer Science Sun Moon University Asan Korea Republic of Dept. of Computer Engineering Mokwon University Daejeon Korea Republic of Dept. of AI Software Technology Sun Moon University Asan Korea Republic of

ISBN: (纸本)9798350367874

This paper presents a quantitative analysis of trends in Russian literature and Korean-Russian translated literature within Korean academia from 1996 to 2024. By analyzing frequency themes and contexts, the study explores evolving research trends, revealing how sociocultural, political, and artistic exchanges between Korea and Russia have influenced each other. Future research using text mining promises to uncover more specific patterns, offering deeper insights into literary and cultural exchanges. This study contributes to understanding the role of literature in expressing and understanding sociopolitical and cultural values in both countries. © 2024 IEEE.

关键词： Korean Citation Index Korean-Russian literature Quantitative analysis Research trend analysis Text mining

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Delta-γ Modeling of Wireless System Resilience in Multipath Fading and Interference Environments 7

Delta-γ Modeling of Wireless System Resilience in Multipath...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Upadhyay, Deepak Upadhyay, Abhay Sharma, Kunj Bihari Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350350067

In this paper, we pursue the resilience of wireless communication systems in multipath fading and interference environments utilizing Delta- γ modelling. Furthermore, fading phenomena such as Rayleigh/Rician/Nakagami channels can be simulated to examine the impact on important performance metrics like SNR, BER and throughput. Different types of interference scenarios, covering co-channel and adjacent channel interferences are also studied to understand impact on system capacity as well as reliability. This article investigates the use of adaptive modulation techniques, multiple-input multiple-output (MIMO) strategies and advanced interference management schemes by extensive *** results illustrate the importance of adaptive strategies in overcoming fading and interference which leads to an increase in system resilience as well as a robust communication link. Metrics for the outage probability and mean time to failure (MTTF) can inform how reliable systems are across operational scenarios. This work reconciles theory with practice to provide a systematic way of controlling the operational parameters in wireless networks under varying conditions. These lessons will be crucial in steering the design of forthcoming wireless systems that must more reliably satisfy a broader set of application requirements. © 2024 IEEE.

关键词： Multipath fading

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Facial Shape-Based Eyeglass Recommendation Using Convolutional Neural Networks

Facial Shape-Based Eyeglass Recommendation Using Convolution...

引用

2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Rifat, Rakib Hossain Siddique, Sunzida Das, Laxmi Rani Haque, Mohd Ariful Brac University Dept. of Computer Science and Engineering Mohakhali Dhaka1212 Bangladesh Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh Noakhali Science and Technology University Dept. of Computer Science and Telecommunication Engineering Sonapur Bangladesh Clark Atlanta University Dept. of Cyber-Physical Systems AtlantaGA United States

ISBN: (纸本)9781665430654

Eyeglasses are not only used to protect our vision and prevent dust from getting into our eyes. Additionally, glass that fits properly can give a person an elegant appearance. However, people often find it difficult to choose eyeglasses that fit their face shape;to address this issue, we have proposed a novel architecture in this paper. In order to do this, we created a pipeline that can recommend eyeglasses based on the form of the eyes using multiple transfer learning architecture to predict the face shape from a given image. We utilized InceptionV4 [17], InceptionV3[18], Vit Small [12], DenseNet121 [10], ResNet50 [9], and VGG16 [16] to predict the facial shape from the image and achieve a test accuracy of 75%. We used 5500 photos with five different face shapes (Heart, Oblong, Oval, Round, Square) for this experiment, and two distinct datasets were gathered from Kaggle [2] and GitHub [1]. By simply uploading the photograph to our recommendation system, our proposed solution can assist users in selecting the appropriate eyewear. © 2023 IEEE.

关键词： CNN DenseNet121 Eye Glass InceptionV3 InceptionV4 ResNet50 Trasnfer Learning VGG16 Vit Small

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Optimal Resource Allocation in Dynamic Fading Channels via Meta-Learning Techniques 2

Optimal Resource Allocation in Dynamic Fading Channels via M...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this paper, we address the optimally dynamical resource allocation problem for fading channels utilizing meta-learning methods with an application to improve efficiency and performance in wireless communication systems. Extensive experiments are conducted and our meta-learning model has demonstrated the ability to adapt itself dynamically in various channel conditions, leading to improvement of Signal-to-Noise Ratio (SNR), reduction of Bit Error Rate (BER) and enhancement on throughput when compared with traditional methods. Visualizations like performance analysis comparisons, heatmaps or 3D plots are used to show how the model can be leverage as effectively manager of resources where they are and when they suit best. The performance improvements demonstrated here are statistically significant testifying for the scalability of our work. The findings of this study demonstrate the major benefits that can be achieved by meta-learning to overcome immanent issues in fast-changing wireless conditions and its potential to propel future communication technologies forward. © 2024 IEEE.

关键词： Contrastive Learning

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Design of an Efficient Model for Enhanced IoT Security and Maintenance in Smart Cities 1

Design of an Efficient Model for Enhanced IoT Security and M...

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1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024

作者： Bhandari, Saurav Chaudhary, Sumit Vaidya, Himadri Kumar, Ujjwal Kirola, Madhu Anandaram, Harishchander Uttaranchal Institute of Technology Uttaranchal University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Jb Institute of Technology Dept. of Mechanical Engineering Dehradun India Amrita Vishwa Vidyapeetham Amrita School of Artificial Intelligence Coimbatore India

ISBN: (纸本)9798350392111

It is of the utmost importance to ensure robust security and effective device maintenance in the rapidly changing landscape of smart city infrastructures. Be that as it may, existing procedures frequently miss the mark in tending to the dynamic and different difficulties presented by emerging dangers and the perplexing exchange of IoT environments. This paper presents a clever way to deal with these issues through three inventive strategies: Adaptive Reinforcement Learning for IoT Security (ARLIS), Graph-Analytics-Based Anomaly Detection (GABAD), and Predictive Maintenance using Time Series Forecasting (PMTSF). Customary safety efforts frequently battle to adjust to the advancing danger scene, prompting weaknesses and likely breaks. ARLIS tends to this constraint by loading the force of support figuring out how to progressively change security conventions in view of ongoing IoT information and organization conditions. ARLIS continuously learns from feedback to improve security measures, which reduces response times to security threats and improves overall security efficacy. Also, irregularity location in IoT networks is tested by the sheer volume and complexity of information produced by interconnected gadgets. GABAD beats this obstacle by utilizing diagram examination to recognize oddities in device connections and ways of behaving inside smart city organizations. By building an exhaustive chart model and applying progressed oddity identification calculations, GABAD accomplishes unequaled precision in peculiarity location while limiting misleading up-sides. In addition, optimizing system reliability and minimizing service disruptions require proactive maintenance of IoT devices. PMTSF alters upkeep methodologies by utilizing time series to foresee gadget disappointments before they happen. By examining gadget execution measurements and ecological variables, PMTSF empowers convenient support mediations, prompting a significant decrease in spontaneous margin time and

关键词： Predictive maintenance

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A Data-Driven Approach to Time-Domain Electromagnetic Modeling Based on Dynamic Mode Decomposition

A Data-Driven Approach to Time-Domain Electromagnetic Modeli...

引用

2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Zhang, Yanming Gao, Steven Jiang, Lijun Chinese University of Hong Kong Dept. of Electronic Engineering Shatin Hong Kong Missouri University of Science and Technology Dept. of Electrical and Computer Engineering RollaMO United States

ISBN: (纸本)9798350355581

This paper presents a data-driven methodology that utilizes Dynamic Mode Decomposition (DMD) for the time-domain (TD) electromagnetic (EM) modeling of microwave devices. As an unsupervised machine learning technique, DMD leverages a limited set of unlabeled spatio-temporal electromagnetic (EM) data to determine DMD eigenvalues and eigenmodes. Then, the obtained DMD model reconstructs the dynamics as a series of exponential terms based on linear assumptions. The effectiveness of this approach is demonstrated through the TD EM modeling of photonic crystal waveguides. Comparative analysis with the finite-difference time-domain (FDTD) method shows that the DMD model not only achieves precise modeling but also facilitates robust short-term forecasting. © 2024 IEEE.

关键词： Dynamic mode decomposition

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Dynamic Resource Allocation and Security Strategies in Cooperative Relaying Systems with Eavesdropping Threats 27

Dynamic Resource Allocation and Security Strategies in Coope...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Upadhyay, Abhay Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Deemed University Centre for Internet of Things Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9798350392319

The dynamic resource allocation and security strategies for cooperative relaying systems proposed here provide significant performance improvements along with robust security features over traditional methods. The method improves throughput, energy efficiency and secrecy capacity by dynamically provisioning resources according to real-time network conditions in conjunction with its interference-suppression techniques such adaptive beamforming and artificial noise. The system is quite good at preventing eavesdropping, and it has such high signal-to-noise ratios that there is a very low error rate. The use case results show that the integrated framework optimizes resource consumption while mitigating security threats, thereby offering a trade-off between existing solutions thus solving some of their shortcomings. Our work demonstrates the opportunity for efficient and secure communication in dynamic environments. © 2024 IEEE.

关键词： Energy efficiency

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT Attack Detection Method based on Synthetic Minority Over-Sampling with Random Forest Technique 10

IoT Attack Detection Method based on Synthetic Minority Over...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Tripathi, Neha Mishra, Amit Kumar Bagla, Piyush Pandey, Neeraj Kumar Mittal, Saksham Dept. of Computer Science and Engineering Dehradun India National Institute of Technology Dept. of Computer Science and Engineering Jalandhar India School of Computing Dit University Dehradun India

ISBN: (纸本)9789380544472

The primary objective of this study is to create a self-protection system for the Internet of Things (IoT) that relies on machine learning. One such resampling approach is the proposed IoT Attack Detection Method based on Synthetic Minority Over-sampling with Random Forest (IADMSMO-RF). To identify risks in the IoT ecosystem, we employ IADMSMO-RF, which improves network search computation. Assessment is performed on the Network Security Laboratory - Knowledge Discovery in Databases (NSL-KDD) dataset to see how effective the trial is in recognizing attacks on the IoT. For the success of the IoT, it is crucial to have a reliable system for detecting and protecting against cyberattacks. Compared to Synthetic Minority Oversampling Technique - Synthetic Minority Oversampling Technique (PPDT -SMOTE), the suggested technique outperforms it in attack detection and categorization. The proposed system has an accuracy of 97.46%, a false alarm rate of 0.2%, and a curve area of 98.76%. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Internet of things

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Efficiency Measurement of k-Partitioning of Mobile WSN 4

Efficiency Measurement of k-Partitioning of Mobile WSN

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Hossain, Azad Rehena, Zeenat Murshidabad Institute of Technology Lecturer in Computer Science and Technology Berhampore India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350306118

Network partitioning improves the energy efficiency of the network. In this work, the algorithm (KP-MWSN) can determine the no. of partitions (k) required based on the delay tolerance limit of each node of MWSN. It can be fruitful for the events where a small amount of delay can be accepted but efficiency has not been calculated in respect of a metric. So, we have measured the efficiency of KP-MWSN algorithm and the results are compared with the Topology Partitioning Scheme (TPS). The simulation result shows that in every randomly created event the efficiency of KP-MWSN is always better than other related state-of-The-Art work. © 2023 IEEE.

关键词： Energy efficiency

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