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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Narayana, K.Venkata Kumari, Nalli Vinaya Ghantasala, G.S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Gupta, Deepika Mlr Institute of Technology Department of Physical Education Hyderabad India Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Hpsebl Solan India

ISBN: (纸本)9798331518592

This study aims to improve performance in team sports, with a specific focus on football, using machine learning techniques. The research explores different machine learning methods, such as Random Forest, Decision Tree, and Support Vector Machine (SVM), by analyzing a dataset that includes player statistics, match results, and in-game events. The Random Forest model performed the best, with 88% accuracy in predicting match outcomes and identifying key performance indicators. The study highlights how machine learning can help coaches and analysts gain useful insights to enhance team performance and strategy. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Dynamic Data Placement Strategy in Distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

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1st International Conference on Data engineering and Machine Intelligence, ICDEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. Rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement approach is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the Distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed cloud

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Forecasting the Indian Financial Markets with LSTM and Price Indicators 2nd

Forecasting the Indian Financial Markets with LSTM and Price...

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Kumaria, Aayush Rajkar, Ajinkya Raut, Aniket Nair, Rashmi S. Department of Computer Science and Engineering MIT School of Engineering MIT Arts Design and Technology University Pune412201 India

ISBN: (纸本)9789819914098

The stock market in India is exceedingly volatile, with an infinite series of aspects influencing the financial market's trajectory and fluctuations;therefore, forecasting the upswing and decline is a challenging task. Prediction, often known as forecasting, is a significant factor in buying and selling equity market. While a combination of fundamental and technical methodologies can answer the question of "in which direction would the stock price move," it cannot answer the question of "by what margin would the movement occur?" That is where prediction comes into the picture. Despite the numerous answers, this study may reliably conclude that forecasting the stock market continues to be a problematic task impacted by a plethora of variables. The purpose of this study is to illustrate how recurrent neural networks may be utilized in finance to anticipate a stock's closing price and assess sentiment in real-time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Financial markets

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Fig Leaf Disease Prediction and Severity Estimation UsingDeep Learning

Fig Leaf Disease Prediction and Severity Estimation UsingDee...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Prasanna Kumar, K.R. Gunasekar, M. Logeswaran, K. Dhanya Sree, P. Malathi, P. Vignesh, V. Dept. of Computer Science and Design Kongu Engineering College Tamil Nadu Erode India School of Computer Science and Engineering VIT Tamil Nadu Vellore India Dept. of Artificial Intelligence Kongu Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331510299

Fig leaf diseases are among the major causes of crop loss. Better methods for detection and further increased accuracy in the estimation of the severity of the disease are demanded. In basis of what is previously researched, this project continues with not just the identification of the disease but also the identification of the level of infections. Having a large dataset of 1, 871 images that comprises 971 healthy samples and 900 infected samples, the analysis will be more elaborate to determine the disease in comparison with previous estimates. We have used two deep learning models - GoogLeNet and AlexNet with outstanding performance in preceding research to make reliable predictions on leaf diseases. The novel contribution of our work is estimating the disease severity with the help of Convolutional Neural Networks that could extract features and classify disease. For the estimation of the disease severity, we will use the VGG16 model to be able to categorize the infection severity into three classes- healthy, mild, and high. The diseases addressed in this paper are Cerotelium fici (fig rust) and Fig mosaic *** goal is to offer a comprehensive tool designed to identify diseases in fig crops and assess their severity for fig farmers and the researcher so that timely disease management strategies could be undertaken. This integrated approach not only aids in taking prompt intervention but also contributes significantly toward sustainable agriculture by reducing adverse impacts on fig leaf diseases. © 2024 IEEE.

关键词： AlexNet Deep Learning GoogLeNet Plant Disease prediction Severity Estimation VGG16

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Automated Breast Cancer Diagnosis Using Deep Learning CNN Models 2

Automated Breast Cancer Diagnosis Using Deep Learning CNN Mo...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Saravanan, G. Jeevanantham Prabin, S.M. Parthasarathy, R. VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Design Chennai India

ISBN: (纸本)9798331534950

The study introduces a deep learning model, CCNN, developed to identify and classify eight types of breast cancer: normal adenosis, normal fibroadenoma, normal phyllodes tumor, normal tubular adenoma, malignant ductal carcinoma, malignant lobular carcinoma, malignant mucinous carcinoma, and malignant papillary carcinoma. This model was integrated with five pre-existing models - Xception, InceptionV3, VGG19, MobileNetV2, and ResNet101 - all trained for image classification using the ImageNet database, to categorize breast cancer MRI images. The dataset, sourced from Kaggle's repository, was further augmented using the Generative Adversarial Network (GAN) technique. For 30 experiments carried out to evaluate the introduced model and the transfer learning of five and proposed CCNN models, the F1-score values accuracy yielded were respectively: 98.04%, 96.13%, 98.54%, 97.11%, 94.20% and 98.98%. This study emphasizes the importance of improving the accuracy of early breast cancer detection to enhance the survival rates of patients. © 2024 IEEE.

关键词： Generative adversarial networks

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Network-Based Rail Running Band Anomaly Recognition via Recurrent Attention Graphs

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IEEE Transactions on Automation science and engineering 2025年 22卷 15381-15395页

作者： Ni, Xuefeng Fieguth, Paul W. Ma, Ziji Shi, Bo Liu, Hongli Hunan University College of Electrical and Information Engineering Changsha410012 China University of Waterloo Department of Systems Design Engineering WaterlooONN2L 3G1 Canada The Hong Kong University of Science and Technology Department of Computer Science and Engineering Sai Kung Hong Kong

Anomaly detection for rail running bands, the pattern of wheel-rail contact area, is crucial to analyze composite rail irregularities. This paper presents an all-weather vision-based solution for running-band inspection. However, two major algorithmic challenges restrict inspection effectiveness: 1) the identification of high-quality features for diversified and imbalanced data subject to noise and outliers, and 2) inferring implicit anomaly co-occurrence patterns. We regard overall running-band anomaly detection as a multi-label classification problem and develop a novel deep multi-anomaly recognition network via recurrent attention graphs (RAGRN). © 2004-2012 IEEE.

关键词： Network theory (graphs)

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HRTF SPATIAL UPSAMPLING IN THE SPHERICAL HARMONICS DOMAIN EMPLOYING A GENERATIVE ADVERSARIAL NETWORK 27

HRTF SPATIAL UPSAMPLING IN THE SPHERICAL HARMONICS DOMAIN EM...

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27th International Conference on Digital Audio Effects, DAFx 2024

作者： Hu, Xuyi Li, Jian Picinali, Lorenzo Hogg, Aidan O.T. Audio Experience Design Dyson School of Design Engineering Imperial College London United Kingdom Centre for Digital Music School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

A Head-Related Transfer Function (HRTF) is able to capture alterations a sound wave undergoes from its source before it reaches the entrances of a listener’s left and right ear canals, and is imperative for creating immersive experiences in virtual and augmented reality (VR/AR). Nevertheless, creating personalized HRTFs demands sophisticated equipment and is hindered by time-consuming data acquisition processes. To counteract these challenges, various techniques for HRTF interpolation and up-sampling have been proposed. This paper illustrates how Generative Adversarial Networks (GANs) can be applied to HRTF data upsampling in the spherical harmonics domain. We propose using Autoencoding Generative Adversarial Networks (AE-GAN) to upsample low-degree spherical harmonics coefficients and get a more accurate representation of the full HRTF set. The proposed method is bench-marked against two baselines: barycentric interpolation and HRTF selection. Results from log-spectral distortion (LSD) evaluation suggest that the proposed AE-GAN has significant potential for upsampling very sparse HRTFs, achieving 17% improvement over baseline methods. Copyright: © 2024 Xuyi Hu* et al.

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Exploring VQE for Ground State Energy Calculations of Small Molecules With Higher Bond Orders

Exploring VQE for Ground State Energy Calculations of Small ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Rutwik, S. Uttarkar, Akshay Setlur, Anagha S. Ashwath Babu, H. Niranjan, Vidya Iiit Dharwad Department of Computer Science and Engineering Hubballi India Department of Biotechnology Bangalore India Iiit Dharwad Department of Arts Science and Design Hubballi India

ISBN: (纸本)9798350379716

In this research paper, we explore the application of quantum computing technologies, specifically the Variational Quantum Eigensolver, to accurately determine the ground state energies of small molecules, especially for molecules with higher bond orders. Our study showcases the feasibility and accuracy of quantum algorithms in performing complex quantum mechanical calculations for molecules like hydrogen, neon, water, methane, nitrogen, oxygen, ethyne, and ethene. Our results demonstrate high precision and reliability in molecular simulations with errors from theoretical values of less than 2 percent for most of the molecules. We also address the challenges associated with scalability and error rates when dealing with larger and more complex molecules. By combining the power of quantum computing with molecular simulations, we aim to enhance our understanding of molecular behavior, leading to more precise predictions in material science and drug discovery. © 2024 IEEE.

关键词： Quantum computers

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Digit Recognition and Separation of Overlapped Digits using Deep CapsNet 2

Digit Recognition and Separation of Overlapped Digits using ...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Moudgil, Aditi Sareen, Bhavna Ghantasala, G S Pradeep Rajesh Sharma, R. Sungheetha, Akey Soubache, I.D. Vidyullatha, Pellakuri Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Alliance University Department of Computer Science and Engineering Bengaluru India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Pondicherry University Department of Eee Pondicherry India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364699

Separation of overlapping digits is observed as a very difficult task when done with human eye, but subsequently it becomes easy when computer vision is used for the same task. The authors have constructed a capsule network which recognizes and separates the overlapped digits. Small sized convolutional kernels were used for the first step of feature extraction which provides grained features this reducing the training parameters. The same process could also possibly be applied for overlapping alphabets and characters also. The process was further followed by insertion of capsule layer into the model which resulted in 93.08% accuracy. Finally the overlapped digits were separated and the image of each digit was reconstructed. © 2024 IEEE.

关键词： Deep neural networks

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Examining learners’ cognitive processes and learning emotions in digital game-based vocabulary learning: evidence from eye-tracking technology and facial emotion recognition

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Interactive Technology and Smart Education 2024年第2期22卷 304-331页

作者： Wang, Chun-Chia Chen, Hsuan-Chu Hung, Jason C. School of Information and Design Chang Jung Christian University Tainan Taiwan Department of Digital Media Design Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

Purpose: This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools. design/methodology/approach: This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process. Findings: The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments. Originality/value: Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning. © 2024, Emerald Publishing Limited.

关键词： Affective computing Convolutional neural networks Digital game-based learning Eye-tracking technology Transfer learning

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