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Satellite Orbit Prediction using a machine learning Approach 5

Satellite Orbit Prediction using a Machine Learning Approach

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Joint 5th international conference on Applied Informatics Workshops, ICAIW 2022: 3rd international Workshop on Applied Artificial Intelligence, WAAI 2022, 4th international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data engineering and Analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation Systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on Knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on Systems Modeling, WSSC 2022

作者： Jadala, Giridhar Meedinti, Gowri Namratha Delhibabu, Radhakrishnan Vellore Institute of Technology Vellore India

The most cutting-edge approaches for orbit prediction right now use physics-based models, which call for exceedingly precise information about the object's trajectory, its operating environment, and its intended course of action. We don't actually have access to this information. Our knowledge of space weather and air density is limited, trajectories are only seldom obtained from noisy ground-based radar systems, and satellite operators are reluctant to disclose their maneuvering intentions. Current orbit predictions that are only based on physics-based models may not achieve the required accuracy for collision avoidance and have already resulted in satellite collisions due to the lack of information regarding the state of the space environment and resident space objects' (RSOs') body characteristics. Two line element sets(TLE) made accessible to the public lack any related error or correctness information. The majority of TLE prediction techniques used today fit polynomials, which cannot capture periodic properties. This paper has presented a methodology for orbital prediction using curve fitting and LSTM on historical orbital data. The proposed machine learning approaches are used with various TLE parameters where the LSTM model is trained to learn through large amounts of historical TLE data. The fitted data is synthesized and then compared with the SGP4 predictions. The two proposed methods focus on reducing prediction errors. The results of the study demonstrate that the proposed machine learning approaches can improve orbital prediction accuracy with good performance in most cases. We go into further optimization and the computing needs for using all-on-all conjunction analysis on the whole TLE collection and visualize when and where conjunction may occur, both currently and in the near future. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Curve fitting

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A Hybrid learning Model for Breast Cancer Diagnosis

A Hybrid Learning Model for Breast Cancer Diagnosis

引用

Sciences and Techniques of Automatic Control and Computer engineering MI-STA, IEEE international Maghreb Meeting of the conference on

作者： Soad Khalifa Ali Yasmina M. A. Abdalnour Wafa I. Eltarhouni Kenz A. Bozed University of Benghazi Libya

Mammography is a technique for detecting breast cancer early and is able to reduce specific mortality rates. However, it is often difficult to accurately identify breast cancer through mammograms due to the difficulty of interpreting it, Therefore, early identification of breast cancer with mammography is an effective strategy for patients' survival rate. In this article, Based on the principles of deep neural network and machicne learning (ML) models will propose a new technique to precisely detect breast cancer. Firstly, data collection and preprocessing of Images; second, Feature extraction by Visual Geometry Group (VGG-19); convolution neural network. In designing the proposed breast cancer detection system, Support Vector machine (SVM), Neural Autoregressive Distribution Estimation (NADE) models, and hybrid of SVM and NADE models of ML are used for classification process. The experimental results showed that the above proposed method is very efficient for Binary classification and provides better accuracy.

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machine learning Techniques for Analysing Security Practises in Electronic Health Records

Machine Learning Techniques for Analysing Security Practises...

引用

Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Birendra Kumar Saraswat Aditya Saxena P.C. Vashist Coputer Sciences & Engineering GLA University Mathura U.P. India Computer Engineering & Applications Institute of Engineering & Technology GLA University Mathura U.P. India Information Technology GL Bajaj Institute of Technology and Management Greater Noida U.P. India

Electronic health statistics (EHR) security is essential because the fitness information saved and transmitted in these data is exclusive. Suppose you want to ensure the proper safety of the information stored in EHRs. In that case, machine-gaining knowledge of strategies is used to analyze the facts and detect any capacity safety problems before they emerge as real problems. Those techniques can identify any capacity weaknesses and help implement corrective measures. Moreover, system study can be used to detect any malicious pastime or ability intrusions that have already passed off. It can allow corporations to respond more speedily to potential security threats and save them from further damage. In conclusion, gadget-mastering techniques may shield the information saved in EHRs efficiently. They could stay a precious asset in enhancing the security of that information in the future.

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Bibliometric Insights into Entrepreneurship Intention Research

Bibliometric Insights into Entrepreneurship Intention Resear...

引用

Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Richard Andre Sunarjo Adhi Widyakto Mochamad Heru Riza Chakim Rifky Nurfaizi Nanda Septiani Faculty of Economics and Business University of Raharja Tangerang Indonesia Faculty of Economics University of Semarang Semarang Indonesia Faculty of Information Technology Reyzone Tangerang Indonesia Dept. of Management Retail CAI Sejahtera Indonesia Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This study conducts a comprehensive bibliometric analysis of entrepreneurial intention research utilizing the Dimensions database, employing advanced bibliometric techniques and data analytics. Leveraging machine learning algorithms, the research evaluates bibliographies categorized by country, organization, author, document, and source, and performs a keyword co-occurrence analysis. The results reveal that China, the USA, and Spain are the leading contributors in this research area, with Tarumanagara University and the University of Seville emerging as significant institutions. Wibowo is identified as the most frequently cited researcher, while the international Journal of Management Education is recognized as the top journal in this domain. The study places a strong emphasis on the role of technological innovation in driving entrepreneurial intention, highlighting how advancements in AI and data analytics are shaping this field. Key terms identified include entrepreneurial intention, self-efficacy, planned behavior, social entrepreneurship, and structural equation modeling. This bibliometric review not only highlights the dominance of specific countries and institutions but also integrates advanced data analytics techniques to identify influential researchers and journals. By employing cutting-edge machine learning methods and focusing on the intersection of technology and entrepreneurship, the study underscores the growing emphasis on sustainability and social impact within entrepreneurial intention research. The findings suggest that future research should leverage these analytical methods to further explore emerging technological themes and enhance understanding, thereby addressing gaps in the current literature.

关键词： Technological innovation data analysis machine learning algorithms Reviews Bibliometrics Entrepreneurship Organizations machine learning Mathematical models Sustainable development

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Potato disease detection using machine learning 3

Potato disease detection using machine learning

引用

3rd international conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Tarik, Marjanul Islam Akter, Sadia Mamun, Abdullah Al Sattar, Abdus Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9780738111834

In Bangladesh potato is one of the major crops. Potato cultivation has been very popular in Bangladesh for the last few decades. But potato production is being hampered due to some diseases which are increasing the cost of farmers in potato production. However, some potato diseases are hampering potato production that is increasing the cost of farmers. Which is disrupting the life of the farmer. An automated and rapid disease detection process to increase potato production and digitize the system. Our main goal is to diagnose potato disease using leaf pictures that we are going to do through advanced machine learning technology. This paper offers a picture that is processing and machine learning based automated systems potato leaf diseases will be identified and classified. Image processing is the best solution for detecting and analyzing these *** this analysis, picture division is done more than 2034 pictures of unhealthy potato and potato's leaf, which is taken from openly accessible plant town information base and a few pre-prepared models are utilized for acknowledgment and characterization of sick and sound leaves. Among them, the program predicts with an accuracy of 99.23% in testing with 25% test data and 75% train data. Our output has shown that machine learning exceeds all existing tasks in potato disease detection. © 2021 IEEE.

关键词： machine learning

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Prediction of Efficient Driving Mode for Vehicle using machine learning

Prediction of Efficient Driving Mode for Vehicle using Machi...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： Ramakrishna Hegde Sujan D Kripa Das Sriraksha M S Siri R Siri Gowri H Soumyasri S M Computer Science Engineering Vidyavardhaka College of Engg. Mysuru India Computer Science Engineering PES University Bengaluru India Master of Computer Applications The National Institute of Engineering Mysuru India

The field of autonomous driving cars is a rapidly growing area of research, machine learning and image recognition technology plays a pivotal role in ensuring their safe navigation on the roads. Different driving modes are offered by modern cars to accommodate various road conditions and driver preferences. However, manually selecting these modes can be cumbersome. Addressing the dynamic nature of mode switching in autonomous driving systems presents a multifaceted challenge. The system must continuously assess a multitude of variables such as traffic patterns, road conditions, weather conditions, and the wear and tear of vehicle components. Traffic conditions can change rapidly, requiring real-time analysis of traffic flow and congestion. Road conditions, including the presence of obstacles, construction zones, or slippery surfaces, demand instant adaptation for safe navigation. Weather conditions further complicate the scenario, as rain, snow, fog, or even intense sunlight can impact visibility and road grip. Addressing these challenges requires sophisticated algorithms capable of processing vast amounts of data from various sensors in real time. machine learning models must be trained to recognize patterns in diverse conditions, enabling the system to make informed decisions swiftly and accurately. Moreover, the system must possess the ability to prioritize these factors based on their immediate impact on driving safety and efficiency. This can be a future enhancement of the paper. By leveraging machine learning algorithms, the mode selection process can be automated. The system constantly monitors driving conditions, learns from real-time camera sensor data and selects the most optimal mode to adapt to current situation. Different models such as Convolutional Neural Network (CNN), CNN with MobileNetV2 as base and decision trees are analyzed.

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Decentralized Health: Federated Deep learning for Cervical Cytology Image Segmentation 3rd

Decentralized Health: Federated Deep Learning for Cervical ...

引用

3rd international conference on Computing, Communication and learning, CoCoLe 2024

作者： Rayvanth, N. Shreya Shree, S. Challa, Venkata Hemant Kumar Reddy Sharma, Vishwash Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031790409

Cervical cytology image segmentation is a crucial component in the automated analysis of cervical cytology screening. This research investigates the efficacy of federated learning against traditional learning methods utilizing variants of the U-Net model family. The study leverages the Cx22 dataset, recognized as the most prominent dataset, obviating concerns related to additional data generation. In the absence of a dedicated dataset like Cx22, attributed to the confidentiality of organizations, this research investigates the potential of federated learning to deliver comparable efficiency to conventional training and learning approaches that can aid in maintaining the confidentiality of the data from different organizations. Results indicate comparable accuracy levels, with models trained using federated learning attaining a remarkable dice coefficient score of 95%. Consequently, this suggests a promising avenue for advancing deep learning models even in scenarios where data privacy remains a paramount concern. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Advancing Sentiment Analysis Precision through ConvNet-SVMBoVW Mode in a Hybrid Context-Enhanced Deep learning Paradigm

Advancing Sentiment Analysis Precision through ConvNet-SVMBo...

引用

Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： V. Suganya M. Rajeev Kumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Sentiment analysis stands as a crucial component of natural language processing (NLP), tasked with interpreting sentiments expressed within text and categorizing them as positive, negative, or neutral. This research study introduces a novel methodology, the ConvNet-SVMBoVW Mode, within a hybrid context-enhanced deep learning paradigm, aimed at enhancing sentiment analysis precision. By integrating advanced deep learning techniques with context-enhancement strategies, the study seeks to improve accuracy and robustness in sentiment analysis. The research explores various text and image preprocessing techniques, ensuring comprehensive data preparation crucial for effective sentiment analysis. The proposed model achieves sentiment classification accuracy ranging from 85% to 92%, addressing the existing challenges in sentiment analysis tasks and advancing state-of-the-art methodologies in natural language processing applications.

关键词： Deep learning Sentiment analysis Analytical models Adaptation models Accuracy Computational modeling Heuristic algorithms

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Automation of Grievance Registration using Transfer learning

Automation of Grievance Registration using Transfer Learning

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Innovative Practices in Technology and Management (ICIPTM), international conference on

作者： G Sidharth Abijeeth Vasra S Sridevi C Deisy M K A Ahamed Khan Department of Information Technology Thiagarajar College of Engineering Madurai India UCSI University Malaysia

Grievance redressal is an indispensable service but involves a lot of issues, which can be resolved if a proper automated application is introduced which involves grievance classification and location fetching mechanism. To arrive at the solution, machine learning techniques can be used, but another major facet of this application is that it should be compatible and transportable. Hence the solution needs to be in the form of a mobile application. The machine learning model must be incorporated into the mobile application. Since mobile phones have minimal computational power to run a model, an architecture which uses minimal resources must be used. MobileNet V2 is an architecture which is specially designed to incorporate Deep learning (DL) algorithm especially Image classification. MobileNet uses minimal computational resources, and interoperability is achieved through Google's Teachable machine learning, which provides a tft lite (TensorFlow Lite) model for our trained dataset and the model can be imported in to the project's asset. Location manager of android's architecture can be used to fetch the user's current latitude and longitude, which can be used by grievance redressal organization to navigate. On achieving this solution, a lot of tedious processes in our existing grievance management system can be automated. Both the public and the government can be benefited and as a result, a lot of data will be in hand which is of prominent importance now a days.

关键词： Deep learning Performance evaluation Quantization (signal) Navigation Computational modeling Memory management Transfer learning

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Sentiment Analysis of Twitter data Using machine learning

Sentiment Analysis of Twitter Data Using Machine Learning

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Pooja Dahiya Rashi Jain Arushi Sinha Astha Sharma Ashwni Kumar Department of Electronics and Communication Engineering Indira Gandhi Delhi Technical University (IGDTUW) New Delhi India

Social media platforms have become a part of our lives offering a wealth of real-time information and diverse perspectives, on different topics. Among these networks, Twitter stands out as a platform for expression and sharing emotions. With millions of users sharing their thoughts, feelings and experiences, Twitter has become a tool for understanding opinions on various subjects such as events, products and societal matters. In this abstract, I summarize a study on Twitter Sentiment Analysis (TSA) which aims to develop an automated system of extracting and evaluating sentiment from tweets. Using Natural Language Processing (NLP) techniques and machine learning algorithms the system analyzes amounts of data to identify and categorize attitudes as positive, negative or neutral. The methodology employed in this study requires access to a collection of tweets covering topics. To ensure accurate sentiment analysis results preprocessing techniques are applied to address issues like noise, misspellings and emojis. The sentiment analysis model is then. Refined using data. A comparative performance investigation revealed that the Support Vector machine (SVM) classifier gives the best results, with the highest accuracy (94.73%) and F1 score (0.4994). The findings from this research demonstrate the effectiveness of the Twitter Sentiment Analysis system, in capturing and classifying sentiments expressed in tweets. Consequently, this research emphasizes the significance and potential of Twitter Sentiment Analysis, as an approach to examining prevailing sentiment. By utilizing Natural Language Processing (NLP) and other strategies the proposed system enhances sentiment analysis methods and their practical use in fields, through machine learning.

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