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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace information Research Institute.

关键词： Fourier analysis

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace information Research Institute.

关键词： Long short-term memory

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A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm 1

A Novel Hybrid Approach for Threat Detection in Cyber Securi...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Gupta, Abhishek Kumar Dixit, Neetu Kumar, Sujeet Rawat, Priyanka Madhumita Jagannath International Management School Department of Information Technology New Delhi110070 India RBMI Department of Computer Application Greater Noida201306 India GL Bajaj Institute of Management Greater Noida201306 India Lingaya's Vidyapeeth Department of Computer Science & Engineering Faridabad121002 India

ISBN: (纸本)9798350353648

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks. © 2024 IEEE.

关键词： Cyber attacks

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace information Research Institute.

关键词： Support vector machines

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Integrative Analysis of miRNA-mRNA Expression Data to Identify miRNA-Targets for Oral Cancer 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2021

作者： Mahapatra, Saswati Prasath, Rajendra Swarnkar, Tripti Department of Computer Application Siksha O Anusandhan Deemed to be University Bhubaneswar India Department of Computer Science and Engineering Indian Institute of Information Technology Sri City Chittoor India

ISBN: (数字)9783031215179

ISBN: (纸本)9783031215162

Micro RNAs (miRNAs) are small non coding RNA sequences consisting of 20–23 nucleotides that govern the post transcriptional expression of genes in both normal and disease condition of the cell. Thus, identification of most influencing miRNAs and the associated mRNAs becomes a research quest in diagnostic and prognostic application of cancer. In this study we conducted an integrated analysis of Next Generation Sequencing based miRNA-mRNA expression data to identify dysregulated miRNAs and their target mRNAs for Oral Cancer. A sensible combination of datamining tools such as Random Forest (RF), K-nearest Neighbour (KNN), Support Vector Machine (SVM), log-Fold Change, Adjusted p-values, Matthews coefficient correlation (MCC), Prediction accuracy was considered for this analysis. The prioritized cancer specific target genes obtained in this approach exhibited a MCC value of 0.9 and achieved a consistently higher prediction accuracy of 95% when subjected to classifiers RF, KNN and SVM. These target genes can be presented as predictive variables for early diagnosis of cancer. The selected miRNA-target genes can further be biologically validated to confirm their participation in disease specific pathways and biological processes. © 2022, Springer Nature Switzerland AG.

关键词： RNA

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer Science 2024年第4期5卷 1-14页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Review of Attacks on Connected and Autonomous Vehicles (CAV) and their Existing Solutions 10

Review of Attacks on Connected and Autonomous Vehicles (CAV)...

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10th International Conference on Reliability, Infocom Technologies and Optimization ,Trends and Future Directions, ICRITO 2022

作者： Yadav, Neha Ansar, Syed Anas Chaurasia, Pawan Kumar Babasaheb Bhimrao Ambedkar University Department of Information Technology Lucknow India Babu Banarsi das University Department of Computer Application Lucknow India

ISBN: (数字)9781665474337

ISBN: (纸本)9781665474337

Connected and automated vehicles (CAV) have proven to be a boon in recent years since it is a probable method to reduce traffic congestion, emissions, and accidents. In recent decades, many innovations in the automotive industry resulted in high-tech vehicle design having a promising future. Primarily, these innovations turn to account for autonomous and connected vehicles, which may lead to the goal of enhancing road safety, opening new job opportunities, and mitigating the adverse environmental effect caused by the increasing number of vehicles. However, the increase in CAVs also carries the burden of threats of cyber-attacks that jeopardize the security and connectivity in CAVs and make the whole system malfunction. These attacks mainly target the vehicular system, surrounding infrastructure, and traffic control system. This paper provides a promising solution by aligning the comprehensive analysis of attacks against CAVs with current measures. This paper also suggests a blockchain-based architecture that not only addresses the existing threats in CAVs but also provides a diminution of vehicular traffic congestion. © 2022 IEEE.

关键词： Autonomous vehicles

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GPS Based Automated Attendance System 5

GPS Based Automated Attendance System

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Bhatt, Chandradeep Rawat, Adarsh Chauhan, Rahul Bhatt, Pashupati Singh, Teekam Sharma, Sanjay Graphic Era Hill University CSE Department Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Institute of Technology & Management Department of Information Technology Uttarakhand India Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India SGRRITS Computer Application Department Dehradun India

ISBN: (纸本)9798350305449

Attendance management systems are important part of an organization, helps them to track real time employee data. From the past few decades, we have plenty of methods or models for the system, but they come with their limitations. These traditional systems in workplaces are often results a data with inefficiencies and inaccuracies. GPS based attendance management system is an effective way to overcome these past limitations. It utilizes GPS technology for employee tracking, and provide us less time consuming and optimal performance. In Model, Both the parties are free from data processing and handling. A web application implementation of system makes it handier with provide a basic and simple user interface to the user. System is totally centric around the admin, which have max privileges in the system. The main gist of the system is abstracted in the backend processes. Haversine formula is important here, which is for distance calculation between two geographical coordinates. System uses geolocation API (application programming interface) for coordinates generation and believes on other more accurate ways or method of coordinates generation. Reputed organization like Google, Microsoft etc. utilizes GPS based systems in their workplaces. © 2023 IEEE.

关键词： Data handling

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Dimensionality Reduction Method for Early Detection of Dementia 1

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International Conference on Advances and applications of Artificial Intelligence and Machine Learning, ICAAAIML 2021

作者： Areekara Vasudevan, Ambili Kumar, A. V. Senthil Rajeyyagari, Sivaram PG Research and Computer Application Hindustan College of Arts and Science Coimbatore India Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9789811948312

ISBN: (纸本)9789811948305

The use of magnetic resonance imaging (MRI) in the diagnosis of dementia is significant because it reveals morphological differences induced by brain decline. The major obstacles faced by compuer assisted systems are: High dimensionality and unbalanced data. Numerous deep learning methods have been used on an MR image dataset for disease classification and prediction, but most of them have refused to endorse data dimensionality reduction, which resulted in skewed outcomes. The UCI machine learning repository provided the MR image of brain dataset utilised in this study. In this article, we suggest an productive dimensional reduction method to ameliorate the attainment of the dementia detection system. The hybrid Linear Discriminant Analysis –Firefly algorithm (LDA-FA) is used for dimensionality reduction. On the reduced data set we apply an optimized convolutional neural network. The method we have proposed is less erroneous and more accurate than other recent work and also produced 95.21% accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Health Fit system in Current Era using React JS 5

Health Fit system in Current Era using React JS

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Bhatt, Chandradeep Negi, Ayush Singh Chauhan, Rahul Bhatt, Pashupati Singh, Teekam Sharma, Sanjay Graphic Era Hill University CSE Department Dehradun India Computer Science and Engineering Graphic Era Hill University Dehradun India Institute of Technology & Management Department of Information Technology Uttarakhand India Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India SGRRITS Computer Application Department Dehradun India

ISBN: (纸本)9798350305449

The purpose of this system is to update the existing systems with the help of computer technology (i.e. to improve the API's which current systems lacked a lot) and the best computers to meet their needs, so that valuable information/data can be stored for a longer period of time and made it easily accessible and functional. Software and hardware should be simple and easy to use. As mentioned above, the Gym fit system provides error free, safe, secure and fast administration. It helps users focus on other activities rather than focusing on data storage. Therefore, it will help the organization make better use of resources. Organizations can store information on computers without further access. This means that people can access information without being hindered by irrelevant information. The aim is to keep the best computers information to make the current book uses with the help of computer hardware and to meet their needs / useful information can be stored for a longer time and easily accessed and used. Basically, the work describes how to manage to achieve quality and serve customers better. © 2023 IEEE.

关键词： Digital storage

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