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Localization for V2X communication with noisy distance measurement

International Journal of Intelligent Networks 2023年第1期4卷 355-360页

作者： Javed, Iram Tang, Xianlun Saleem, Muhammad Asim Javed, Ashir Zia, Muhammad Azam Shoukat, Ijaz Ali School of Computer Science and Technology Chongqing University of Posts and Telecommunications China Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok10330 Thailand Aging Research Center Karolinska Institutet Stockholm171 65 Sweden Department of Computer Science University of Agriculture Faisalabad Pakistan Riphah College of Computing Riphah International University Faisalabad Campus Pakistan

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47. © 2023 The Authors

关键词： Extended Kalman filters

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Improving the Quality of Diabetic Data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Biradar, Divya Dattangire, Rahul Vaidya, Ruchika Inti, NagaSuryaShivani University of Texas at Arlington Computer Science ArlingtonTX76013 United States Data Engineering HoustonTX77002 United States Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer and Information Science ArlingtonTX76013 United States

ISBN: (纸本)9798331539948

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The 'Diabetes' data set from Kaggle is divided into implementing the combined approach Data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach's effective impact in correcting and consistent data within the healthcare dataset. © 2024 IEEE.

关键词： Electronic health record

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An Efficient Proposal for Deep learning-Based Diabetes Prediction 2

An Efficient Proposal for Deep Learning-Based Diabetes Predi...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Baswaraj, D. Raju, Ch V V Narasimha Reddy, Pundru Chandra Shaker Kiran, Ajmeera Shaik, Mohammad Khaja Kumar, D Anil Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad India Marri Laxman Reddy Institute of Technology and Management Department Information Technology Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India St. Ann's College of Engineering and Technology Artificial Intelligence and Machine Learning Andhra Pradesh Chirala India Pace Institute of Technology and Sciences Electronics and Communication Engineering Andhra Pradesh Ongole India

ISBN: (纸本)9798350372892

As a result of its exponential growth in recent years, diabetes now poses a serious danger on a worldwide scale. As of right now, diabetes is one of the foremost causes of death worldwide. Failure to treat diabetes increases the risk of death from cardiovascular disease, cerebrovascular accident, brain stroke, and a host of other complications. In order to minimize further complications, it is crucial to discover diabetes early so that action can be taken to stop the condition from progressing. Predicting diabetes in its early stages may be possible with the use of cutting-edge machine learning techniques that uncover previously unseen patterns. Using the PIMA dataset and a variety of machine learning algorithms, this study lays forth an approach for diabetes prediction. All four of these functional classifiers - Deep learning (DL), artificial Neural Network (ANN), and Naive Bayes (NB) - achieve accuracy rates between 90% and 98%. With a 98.07% accuracy rate on the PIMA dataset, DL outperforms the other three methods when it comes to diabetes onset. In light of this, the suggested system offers healthcare officials a useful tool for prognosis. Incorporating omics data for illness onset prediction considerably improves the accuracy of the DL technique. © 2024 IEEE.

关键词： Adversarial machine learning

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License Plate Detection using Computer Vision technique with artificial intelligence 2

License Plate Detection using Computer Vision technique with...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Rabindranath, S. Pallavi, K.V. Doddegowda, B.J. Guha, Tapas Amc Engineering College Department of Cse Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The term "Automatic Vehicle Plate Recognition,"abbreviated "AVPR,"refers to the process of extracting information about a vehicle's licence plate from a picture or series of photographs. Since the beginning of the last century, AVPR has evolved into a hard and exciting field of study. Applications may be found for AVPR systems in a broad variety of contexts. It has proved beneficial to a great number of real-world applications, including electronic toll collection, automated parking management, access control, radar-based speed control, border control, criminal pursuit, and traffic law enforcement, amongst others. Even if there are a number of commercial AVPR systems on the market right now, there are still a lot of obstacles and problems with the proper recognition of licence plates. In India, the rules for number plates are seldom put into practise. The identification of licence plates is plagued by a number of issues, including the presence of superfluous information, variations in font size and type, blur, skew, and other environmental influences. Recognition of number licence plates may be difficult due to the many different kinds of licence plates and the surroundings in which they are displayed. The creation of a robust, accurate, and dependable automated automobile licence plate recognition system is the primary goal of this thesis. The following are the stages involved in implementing our strategy: The first step is the pre-processing of the picture that was read in. The second step involves the extraction of character areas, and the third phase involves the identification of the characters that have been taken from the previous phase. The work being presented here has been done in order to recognise Indian licence plates. © 2022 IEEE.

关键词： License plates (automobile)

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Multimodal Gas Detection Using E-Nose and Thermal Images:An Approach Utilizing SRGAN and Sparse Autoencoder

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Computers, Materials & Continua 2025年第5期83卷 3493-3517页

作者： Pratik Jadhav Vuppala Adithya Sairam Niranjan Bhojane Abhyuday Singh Shilpa Gite Biswajeet Pradhan Mrinal Bachute Abdullah Alamri Artificial Intelligence and Machine Learning Department Symbiosis Institute of TechnologySymbiosis International(Deemed)UniversityPune412115India Symbiosis Centre of Applied AI(SCAAI) Symbiosis International(Deemed)UniversityPune412115India Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS) School of Civil and Environmental EngineeringUniversity of Technology SydneyUltimoNSW 2007Australia Department of Geology and Geophysics College of ScienceKing Saud UniversityRiyadh11362Saudi Arabia

Electronic nose and thermal images are effective ways to diagnose the presence of gases in real-time *** fusion of these modalities can result in the development of highly accurate diagnostic *** low-cost thermal imaging software produces low-resolution thermal images in grayscale format,hence necessitating methods for improving the resolution and colorizing the *** objective of this paper is to develop and train a super-resolution generative adversarial network for improving the resolution of the thermal images,followed by a sparse autoencoder for colorization of thermal images and amultimodal convolutional neural network for gas detection using electronic nose and thermal *** dataset used comprises 6400 thermal images and electronic nose measurements for four classes.A multimodal Convolutional Neural Network(CNN)comprising an EfficientNetB2 pre-trainedmodel was developed using both early and late feature *** Super Resolution Generative Adversarial Network(SRGAN)model was developed and trained on low and high-resolution thermal *** autoencoder was trained on the grayscale and colorized thermal *** SRGAN was trained on lowand high-resolution thermal images,achieving a Structural Similarity Index(SSIM)of 90.28,a Peak Signal-to-Noise Ratio(PSNR)of 68.74,and a Mean Absolute Error(MAE)of *** autoencoder model produced an MAE of 0.035,a Mean Squared Error(MSE)of 0.006,and a Root Mean Squared Error(RMSE)of *** multimodal CNN,trained on these images and electronic nose measurements using both early and late fusion techniques,achieved accuracies of 97.89% and 98.55%,***,the proposed framework can be of great aid for the integration with low-cost software to generate high quality thermal camera images and highly accurate detection of gases in real-time.

关键词： Thermal imaging gas detection multimodal learning generative models autoencoders

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IoT-Deep learning Based Activity Recommendation System

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Computer Systems Science & engineering 2023年第5期45卷 2001-2016页

作者： Sharmilee Kannan R.U.Anitha M.Divayapushpalakshmi K.S.Kalaivani Department of Electronics and Communication Engineering Nandha Engineering CollegePerundurai638052TamilnaduIndia Department of Computer Science Sona College of Arts and ScienceSalem636005TamilnaduIndia Department of Artificial Intelligence and Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabad500090TelanganaIndia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060TamilnaduIndia

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global *** professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical *** Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health *** Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have *** existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient ***’health can be continuously monitored using advanced WS that can be fitted or embedded in their *** research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and machine learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep learning(DL)+Restricted Boltzmann machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it *** recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard *** a healthcare DSS,this framework is clever enough to detect and analyze a patient’s *** results further reveal that the proposed system is efficient and clever enough to deliver health *** data reported in this study demonstrate the *** device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

关键词： Deep learning IoT healthcare system activity recommender system body sensors

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Estimation of Response Time with Neural Networks in Cloud Computing System 1

Estimation of Response Time with Neural Networks in Cloud Co...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： More, Rajesh Suresh, Sanjna Veerappampalayam Easwaramoorthy, Sathish Kumar Rizvi, Haider Ali Kunnathully, Kannan Gupta, Sahil K. Cloudwerx Wordwide Departmen of AIML Hyderabad India Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Yogananda School of Artificial Intelligence Shoolini University Department of Artificial Intelligence and Machine Learning India Veltech University Department of Research Chennai India Tula's Institute Department of Computer Science and Engineering Dehradun248197 India

ISBN: (纸本)9798331518578

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration. © 2025 IEEE.

关键词： Cloud platforms

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Enhancing Interpretability: The Role of Explainable AI in Healthcare Diagnostics

Enhancing Interpretability: The Role of Explainable AI in He...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Nikita Zade Meher Langote Prateek Verma Department of Artificial Intelligence & Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education (DU) Sawangi Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

XAI is now transforming the use of AI in diagnosing diseases by overcoming some of the problems inherent in most black-box approaches. In time-sensitive speciality areas like computer-aided diagnosis, image analysis, and predictive modelling, SHAP, LIME, and attention-based XAI techniques explain how AI decides to help doctors and other healthcare professionals trust AI. Therefore, this paper aims to clarify why XAI is needed in healthcare, introduce techniques, and show their examples in cancer diagnosis and disease prognosis. XAI improves diagnostic performance and satisfies regulatory and ethical requirements for increased usage in clinical practice. Because of XAI, clinicians can use the best of artificial intelligence to complement their knowledge in enhancing the treatment of patients. That is why the outlook of XAI in healthcare includes the next steps in achieving higher scalability, managing bias, and working with diverse regulatory frameworks, which will define the future of AI solutions in healthcare.

关键词： Renewable energy sources Explainable AI Scalability Closed box Medical services Predictive models Regulation artificial intelligence Prognostics and health management Cancer

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Development of an EEG Controlled System for Neuro-Prosthetics 2nd

Development of an EEG Controlled System for Neuro-Prosthetic...

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

作者： Rushambwa, Munyaradzi Charles Gezimati, Mavis Jeeva, J.B. Palaniappan, Rajkumar Govindaraj, P. Pondani, Taurayi Electronic Engineering Harare Institute of Technology Harare Zimbabwe Sensors and Biomedical Engineering Vellore Institute of Technology Vellore India Mechatronics Engineering Bahrain University Salmabad Bahrain Artificial Intelligence and Machine Learning Vivekanda College of Engineering and Technology Puttur Mangalore India Harare Institute of Technology Harare Zimbabwe

ISBN: (纸本)9789819914098

Some people are disabled but still are able to do their jobs, others are completely paralyzed while their brains are still working. This project aims to design and implement a device that can help these physically paralyzed people to mimic the hand grip. The system measures EEG (electroencephalographic) waves of the brain, in turn, issues commands at the appropriate brainwave frequency to control the desired output (grab, release, forward, and backward). The human brain is made up of millions of interconnected neurons. The way neurons interact represents different emotions and mental states of the brain that can be measured and analyzed. The signals to be measured are reported as electrical signals and detected by the brain wave sensor. The brain wave sensor in turn converts the signal into packets that are transmittable over Bluetooth to the remote wireless controller. Then the instructions will be sent to the control section to be able to operate the desired action after feature extraction and classification. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neurons

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Predicting Stock Market Trends Using Deep learning: A Case Study of Pakistan Stock Exchange 18

Predicting Stock Market Trends Using Deep Learning: A Case S...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Balouch, Maaz Hamayat, Faizan Abbasi, Humza Fazal Javed, Muhammad Qasim Mehrban, Ali Ahmad, Rana Fayyaz Department of Business Studies Namal University Mianwali Pakistan National Center for Physics Machine Learning and Deep Learning Lab Artificial Intelligence Technology Center Islamabad Pakistan Department of Computer Science Bahria University Islamabad Pakistan School of Electrical and Electronics Engineering Newcastle University Newcastle United Kingdom

ISBN: (纸本)9798331508692

This research investigates the short-term stock price prediction in the Pakistan Stock Exchange (PSX) using Deep learning (DL) techniques. By integrating historical stock data, economic indicators, and market news, the research aims to address the limitations of traditional stock analysis methods. Data was collected and pre-processed from various sources, including the PSX, economic indicators, and market news. Four techniques were implemented: ARIMA, LSTM, BI-LSTM, and a hybrid CNN-BI-LSTM. Their performance was evaluated using RMSE, MAE, and MAPE metrics. Comparative analysis revealed that BILSTM outperformed the other techniques, with RMSE, MAE, and MAPE scores of 0.9298, 0.6099, and 13.9443, respectively. Despite ARIMA's lower accuracy, it had the fastest inference time, followed by LSTM, CNN-BI-LSTM, and BI-LSTM. Results indicate that using comprehensive data with DL models significantly improves stock price forecast accuracy, highlighting the potential of technological innovation in enhancing financial market decision-making. These DL algorithms can provide valuable insights for investors and analysts in the PSX, leading to better decision-making and more accurate economic predictions. © 2024 IEEE.

关键词： Deep learning

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