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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Saxena, Sarvagya Khandelwal, Siddhartha Chaudhary, Sneha Gupta, Priyanka Gla University Computer Science Engineering Vrindavan India Gla University Computer Science Engineering Mathura India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798331528201

Major deaths (55% or 1.5 million) of LRI (Lower Respiratory Infections) are caused due to Pneumococcal pneumonia. LRI is the 5th highest cause of demise as mentioned by the Global Burden of Disease Study 2015. Health organization suffers from a lack of Physicians. A Physician handles so many cases which leads to misdiagnoses. To resolve all these issues we came up with an easy-to-handle, and implemented in health organizations, the CNN model. The main aim of this model is to help the healthcare professionals by providing them with a tool which will help them detect Pneumococcal pneumonia. With the help of this tool, the work strain of a doctor can be reduced. This model is developed without any pre-trained model and for better accuracy, an immense dataset is used. All the X-ray images were pre processed before being introduced to the neural network. Different layers have been used to make the model more precise and make it adaptable for X-ray images under different conditions. The aim is to combine deep learning concepts with healthcare to make a positive and meaningful impact in the healthcare industry. © 2024 IEEE.

关键词： CNN Models Deep Learning Healthcare Pneumococcal pneumonia X-Ray

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An Integrated Learning Model for Online Performance Prediction Using LightGBM and Tabnet Fusion Model for Large Laser Facilities 8

An Integrated Learning Model for Online Performance Predicti...

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8th International Symposium on computer Science and Intelligent Control, ISCSIC 2024

作者： He, Zizhou Shen, Wenwen Li, Suicheng Luo, Jun Institute of Computer Application China Academy of Engineering Physics Mianyang621900 China

ISBN: (纸本)9798350380286

Performance prediction is a crucial aspect of ensuring optimal facility operation and maintenance across diverse fields. Although deep learning algorithms have found many applications in several industries, their applicability in tabular data mining that integrates multiple parameter types has remained somewhat limited. To address this gap, this research proposes the introduction of an integrated learning model premised on practical business scenarios, featuring data cleaning, and well-grounded feature engineering methods. Specifically, the study employs the LightGBM and Tabnet fusion model to overcome inefficiencies associated with traditional integrated learning techniques like XGBoost and GBDT. Furthermore, a recursive feature elimination approach is implemented to reduce redundancy during model construction, culminating in the improved online performance prediction. The model developed in this study has demonstrated a 32.66% reduction in prediction deviation compared to traditional manual prediction methods in simulations on large laser facilities. These findings underscore the significance of this integrated learning model in enhancing predictive accuracy, which is a critical component of optimal facility management. © 2024 IEEE.

关键词： Prediction models

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Code-Mixed Sentiment Corpus of Customer Reviews in Malayalam and English for Enhanced Local Market Analysis in India 6

Code-Mixed Sentiment Corpus of Customer Reviews in Malayalam...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Bhagya, Sree S Hrishiraj, G.L. NaderaBeevi, S. TKM College of Engineering Kollam APJ Abdul Kalam Technological University Dept. of Computer Application Kerala India TKM College of Engineering Dept. of Computer Application Kallam Kerala India

ISBN: (纸本)9798350306613

The popularity of online shopping has boosted the volume of customer reviews, which is crucial for business growth and decision-making as they influence consumers' purchasing decisions. Indians frequently engage in code-mixing, a social media practice where multilingual users publish comments that give information about consumer preferences, cultural relevance, and product or service improvement. Businesses with an Indian market emphasis should consider this factor to improve the consumer experience. Indian languages often face resource constraints, making it difficult to create precise language models and carry out operations like sentiment analysis, spam filtering, offensive text recognition, etc. Through this work, we draw the researcher's attention to the relevance of processing code-mixed customer reviews for e-commerce sites in linguistically diverse regions like India and how it helps to serve customers better, improve goods and services, and stay competitive in a globalised and multicultural market. This work aims to expand Malayalam-English (Manglish) code-mixed literature in the commercial domain by constructing a sentiment corpus featuring customer reviews in Manglish related to commercial products annotated by voluntary annotators. The performance of different machine learning (ML) and deep learning (DL) models for the sentiment identification task is then assessed using the new corpus, and a comprehensive analysis of the results is also presented. © 2024 IEEE.

关键词： code mixing customer reviews sentiment analysis

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An Analytic Review on Stock Market Price Prediction using Machine Learning and Deep Learning Techniques

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Recent Patents on engineering 2024年第2期18卷 88-104页

作者： Rath, Swarnalata Das, Nilima R. Pattanayak, Binod Kumar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

Anticipating stock market trends is a challenging endeavor that requires a lot of attention because correctly predicting stock prices can lead to significant rewards if the right judgments are made. Due to non-stationary, loud, and chaotic data, stock market prediction is challenging. Investors need help to forecast where they should spend their money to make a profit. Investment methods in the stock market are intricate and based on the analysis of large datasets. Expert analysts and investors have placed a high value on developments in stock price prediction. Due to intrinsically noisy settings and increased volatility concerning market trends, the stock market forecast for assessing trends is tricky. The intricacies of stock prices are influenced by several elements, including quarterly earnings releases, market news, and other altering habits. Traders use a number of technical indicators based on stocks that are collected on a daily basis to make decisions. Even though these indicators are used to analyze stock returns, predicting daily, and weekly market patterns are difficult. Machine learning techniques have been extensively studied in recent years to see if they might boost market predictions compared to legacy or conventional methods. The existing methodologies have devised several strategies for predicting stock market trends. Various machine learning and deep learning algorithms, such as SVM, DT, LR, NN, kNN, ANN, and CNN, can boost performance in predicting the stock market. Based on a survey of current literature, this work aims to identify future directions for machine learning stock market prediction research. This research aims to provide a systematic literature review process to discover relevant peer-reviewed journal papers from the last two decades and classify studies with similar methods and situations into the machine learning approach and deep learning. In the current article, the methods and the performance of those adopted methods will be id

关键词： Financial markets

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Improving Network Availability through Optimized Multipath Routing and Incremental Deployment Strategies

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computers, Materials & Continua 2024年第7期80卷 427-448页

作者： Wei Zhang Haijun Geng Computer Application Teaching and Research Section China University of Labor RelationsBeijing100048China School of Automation and Software Engineering Shanxi UniversityTaiyuan030006China

Currently,distributed routing protocols are constrained by offering a single path between any pair of nodes,thereby limiting the potential throughput and overall network *** approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is *** contrast,routing protocols that leverage multiple paths within the network offer a more resilient and efficient *** routing,as a fundamental concept,surpasses the limitations of traditional shortest path first *** not only redirects traffic to unused resources,effectively mitigating network congestion,but also ensures load balancing across the *** optimization significantly improves network utilization and boosts the overall performance,making it a widely recognized efficient method for enhancing network *** further strengthen network resilience against failures,we introduce a routing scheme known as Multiple Nodes with at least Two Choices(MNTC).This innovative approach aims to significantly enhance network availability by providing each node with at least two routing *** doing so,it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures,thereby enhancing the overall resilience of the *** ensure the optimal placement of nodes,we propose three incremental deployment *** algorithms carefully select the most suitable set of nodes for deployment,taking into account various factors such as node connectivity,traffic patterns,and network *** deployingMNTCon a carefully chosen set of nodes,we can significantly enhance network reliability without the need for a complete overhaul of the existing *** have conducted extensive evaluations of MNTC in diverse topological spaces,demonstrating its effectiveness in maintaining high network availability with minimal path *** results are impressive,sh

关键词： Multipath routing network availability incremental deployment schemes genetic algorithm

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Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

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Computational Visual Media 2024年第2期10卷 331-353页

作者： Yanran Dai Jing Li Yuqi Jiang Haidong Qin Bang Liang Shikuan Hong Haozhe Pan Tao Yang School of Telecommunications Engineering Xidian UniversityXi’an 710071China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIPthe School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China

Free-viewpoint video allows the user to view objects from any virtual perspective,creating an immersive visual *** technology enhances the interactivity and freedom of multimedia ***,many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision,particularly for sports fields having large areas and numerous moving *** address these issues,we propose a freeviewpoint video synthesis method based on distance field *** central idea is to fuse multiview distance field information and use it to adjust the search step size *** step size search is used in two ways:for fast estimation of multiobject three-dimensional surfaces,and synthetic view rendering based on global occlusion *** have implemented our ideas using parallel computing for interactive display,using CUDA and OpenGL frameworks,and have used real-world and simulated experimental datasets for *** results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 ***,the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

关键词： free-viewpoint video view synthesis camera array distance field sports video

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Sentiment Analysis of Speeches using VADER, Sentiment Intensity Analyzer, and TextBlob 4

Sentiment Analysis of Speeches using VADER, Sentiment Intens...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Joshi, Ankush Joshi, Deepika Kumar, Vikash Kumar, Anuj Singh, Ritik Kumar Chauhan, Naman Coer University Computer Science and Application Roorkee India Amarpali University Computer Science and Engineering Haldwani India

ISBN: (纸本)9798350364729

In today's world the words of a leader are the most valuable entity for his/her followers, and when the leader is a prime minister of one of the largest democratic countries then the value of his/her words is more valuable and important. In this research paper researchers have analyzed the data in the form of sentiments of the two most popular prime ministers of India Dr. Manmohan Singh and Mr. Narendra Modi. By this analysis, researchers can check the impact of their words on the people of India. For analyzing the sentiments researchers have used tools such as TextBlob, VADER, and Sentiment Intensity Analyzer, and for checking the occurrence frequency of words word cloud is used. © 2024 IEEE.

关键词： Public policy

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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An autoencoder based unsupervised clustering approach to analyze the effect of E-learning on the mental health of Indian students during the Covid-19 pandemic

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Multimedia Tools and applications 2024年 1-21页

作者： Banerjee, Pritha Jana, Chandan Saha, Jayita Chowdhury, Chandreyee Computer Application and Science Institute of Engineering and Management West Bengal Kolkata700091 India Computer Science and Engineering Jadavpur University West Bengal Kolkata700032 India Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal Karnataka Bengaluru560064 India

Due to the Covid-19 pandemic, the education system in India has changed to remote that is, online study mode. Though there are works on the effect of teaching learning on Indian students, the effect of online mode and associated mental state, particularly when the entire country is going through a crisis could not be found in the literature. Our goal is to analyze data and find some pattern through which we can understand the effectiveness of the online study and also try to figure out the stress level. The dataset we collected from 500 undergraduate college students during April-May, 2021 is in questionnaire format. Our contribution in this paper are - (i) publishing a dataset of student feedbacks, and (ii) designing a data processing pipeline involving autoencoders followed by clustering approach. The dataset is in text format so for our analysis we have converted the dataset into a numerical format using the concept of a binary bag of words. Dimensionality reduction is applied through autoencoder for an effective latent space representation. Finally, for finding patterns out of this dimensionally reduced feature space, we have applied unsupervised learning algorithms - kMeans and DBSCAN. A thorough analysis of the clustering process reveals that the absence of social communication in purely online education provokes isolation irrespective of the urban or rural background of the students. However, it could supplement offline classes as a substantial number of students welcomed the concept of online learning as reported in the data. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Research on assembly constraints reconstruction based on Unreal Engine 2

Research on assembly constraints reconstruction based on Unr...

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2nd International Conference on computer Technology and Information Science, CTIS 2024

作者： Tang, Lei Wang, Daiyong Mou, Zixin Li, Ling Long, Yang Li, Dong Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang621025 China

In the process of digital twin modeling, the assembly constraints of the model in the Unreal Engine usually requires manual intervention. To realize the intelligence and high efficiency of digital twin modeling, an Unreal Engine based assembly constraints reconstruction method is proposed. The method explores the high-fidelity assembly constraints mapping from the modeling software to the Unreal Engine, divides the model in the modeling software into two parts: the entity structure and the assembly constraints, and reconstructs the assembly constraints in the Unreal Engine, to realize the transformation from the modeling software model to the Unreal Engine model. Finally, the proposed method is applied to the digital twin engineering of various institutions to verify its rapidity and effectiveness in practical application scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Engines

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