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Selection of Access Point Based on Handover Decision Using Fuzzy Logic in LTE Network

Journal of engineering science and technology Review 2021年第6期14卷 193-200页

作者： Mohan, Divya Mary, A. Geetha School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Handover decision-based establishment of access point plays a vital role in LTE networks. For making a proper handover decision it is essential to implement various techniques. This gives a break less network connection for a fast-moving node. Billions of mobile nodes need connectivity with a major access point with a balanced cell load and signal strength. This is possible with a successful handover. However, making handover results in ping-pong effect, channel fading and an increase in call drop rate. This can be effectively avoided by choosing proper neighbouring access point within a time slot frequently. The node when varied rapidly from one location to another it needs a proper network connection. The previously used techniques in LTE network are not so effective. Hence, we propose a novel Dynamic magnetic Force Optimization (DMFO) technique for choosing access point appropriately and fuzzy logic for a successful handover. The implemented system model terminates the negative impacts of the previously existing algorithms and the results of comparison are represented. Hence, it proves that the proposed model is a better solution for making handover decision © 2021 School of science, IHU. All rights reserved

关键词： Fuzzy logic

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Forecasting Continuous Air Pollution with Cloud-Based Big Data Platform using Spatial-Temporal Visualization 8

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Chang, Yue Shan Abimananan, Satheesh Shukla, Saurabh Satheesh, Dhivyadharsini National Taipei University Department of Computer Science and Information Engineering New Taipei City Taiwan Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350334012

In today's era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting. © 2023 IEEE.

关键词： Data visualization

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Web-based remote sensing image retrieval using multiscale and multidirectional analysis based on Contourlet and Haralick texture features

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International Journal of Intelligent Computing and Cybernetics 2021年第4期14卷 533-549页

作者： Rajakumar Krishnan Arunkumar Thangavelu P.Prabhavathy Devulapalli Sudheer Deepak Putrevu Arundhati Misra School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndia School of Information Technology and Engineering Vellore Institute of TechnologyVelloreIndia School of Computer Science and Engineering Vellore Institute of TechnologyVelloreIndiaand Space Applications Centre AhmedabadIndia

Purpose-Extracting suitable features to represent an image based on its content is a very tedious *** in remote sensing we have high-resolution images with a variety of objects on the Earth’s *** distance metric is used to measure the similarity between query and database *** low distance obtained image is indexed at the top as high relevant information to the ***/methodology/approach-This paper aims to develop an automatic feature extraction system for remote sensing image *** texture features based on Contourlet transform are fused with statistical features extracted from the QuadTree(QT)decomposition are developed as feature set to represent the input *** extracted features will retrieve similar images from the large image datasets using an image-based query through the web-based user ***-The developed retrieval system performance has been analyzed using precision and recall and F1 *** proposed feature vector gives better performance with 0.69 precision for the top 50 relevant retrieved results over other existing multiscale-based feature extraction ***/value-The main contribution of this paper is developing a texture feature vector in a multiscale domain by combining the Haralick texture properties in the Contourlet domain and Statistical features using QT *** features required to represent the image is 207 which is very less dimension compare to other texture *** performance shows superior than the other state of art methods.

关键词： Image retrieval Remote sensing Contourlet Texture features Web-based search CBIR Multiscale texture

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Neural Network Aided Kalman Filter to Maximize Accuracy

Neural Network Aided Kalman Filter to Maximize Accuracy

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2022 International Conference for Advancement in technology, ICONAT 2022

作者： Agarwal, Varun Niraj Kanshi, Avaneesh Melarkode, Navneet Vinod Krishna, Hemanth Hota, Malaya Kumar School of Computer Science Engineering Vellore Institute of Technology Vellore India School of Electronics and Engineering Vellore Institute of Technology Department of Communication Engineering Vellore India

ISBN: (纸本)9781665425773

With the growing need for data, and ever-growing demand for prediction and error-correction, Kalman Filters are undoubtedly at the forefronts of real-time estimation. While these filters are designed to achieve convergence shortly after getting exposed to the data, the filters might not be able to maximize all the data it can extract from the system. In order to extract most of the information that is otherwise unusable by the Kalman Factor, an initial assumption of a pre-trained Machine Learning model that correlates a feedable parameter with the unusable data is made. The feedable parameter is then given to the Kalman Filter along with the other standard parameters which boosts the accuracy by adding another dimension to the filter. © 2022 IEEE.

关键词： Kalman filters

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Fish Feeder System Using Internet of Things 7th

Fish Feeder System Using Internet of Things

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7th International Conference on Big Data and Cloud Computing Challenges, ICBCC 2022

作者： Malla, Jyotsna Lekkala, Preetham Raghu, Rishi Jayashree, J. Vijayashree, J. Diaz, Vicente Garcia School of Computer Science and Engineering Vellore Institute of Technology TN Katpadi India Department of Computer Science University of Oviedo Oviedo Spain

ISBN: (纸本)9789819910502

Pets have become quite common in almost every household. In the present busy world, it has become quite a hassle to take good care of the pets due to the lack of time and knowledge about their eating patterns and schedules. People having fish as pet need to devote more time and attention to them owing to their complex and timely food requirement. The goal of designing an IoT-based fish feeder is to eliminate manual labour involved in feeding the fish regularly at certain intervals of time. This proposed system can offer regular feeding without the interference of the owner. This system also helps to reduce costs by preventing the overspending on extra fish feed than required. This study is based on the concept that the fish will be fed the owner is not present at home. Wireless connectivity is used in the fish feeder system. The system can be built by programming a fish feeder to feed fish at a specific time, and then commanding it to deliver the food through stepper motors. It removes the existent need of a manual aquarium maintenance. The fish feeder system can be atomised and can be easily managed using a Graphical User Interface (GUI). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphical user interfaces

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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Real Time Automated Sign Language Recognition and Transcription with Audio Feedback 56

Real Time Automated Sign Language Recognition and Transcript...

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Kavitha, J.C. Nagarajan, D. School of Computer Science & Engineering Vellore Institute of Technology Chennai India Rajalakshmi Institute of Technology Department of Mathematics Chennai India

ISBN: (纸本)9798350315875

Sign language recognition and transcription systems that operate automatically can greatly enhance communication for those people who are deaf. These innovative techniques hold the promise of breaking down barriers and fostering greater inclusivity for individuals with hearing impairments. An automated real-time system is developed that recognizes American Sign Language (ASL) gestures and responds to the user with corresponding text output along with audio feedback. The proposed system works by capturing the hand gestures, extract the features or data points using Suzuki contour algorithm and implements Convolutional Neural Networks (CNN) algorithm to classify American Sign Language gestures in real-time. This system uses lemmatization algorithm for transcribing the gestures into text and converts the text to audio feedback using text-to-speech synthesis module. The proposed system is based on computer vision and deep learning techniques for sign recognition, natural language processing for transcription, and text-to-speech synthesis for audio feedback. The proposed system performance is accessed using a MNIST dataset of ASL gestures taken from Kaggle, and its effectiveness has been demonstrated in real-world scenarios. The system achieves a high accuracy of 91% in sign recognition and transcription and produces high-quality audio feedback in real-time. The proposed system has the potential to improve communication and accessibility and for deaf and hard-of-hearina individuals in various contexts. © 2023 IEEE.

关键词： Deep learning

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Brain Abnormality Detection and Analysis by Using MRI of Brain Through Multiple Sclerosis Lesions Detection and Analysis 1

Brain Abnormality Detection and Analysis by Using MRI of Bra...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Ali Ibrahim, S. Jafar Rayudu, K.M. Anitha, Setti Anitha, K. Arun Nambi, P. Erfan, S.K. School of Computer Science and Engineering Department of Internet of Things Vellore Institute of Technology Tamilnadu Vellore632014 India QIS College of Engineering and Technology AP Ongole India

ISBN: (纸本)9798350345957

Progressive MS lesion formation is a primary cause of cognitive decline and physical impairment, making MS one of the major disorders. The rate of disease progression and the efficacy of treatments depend critically on having a quick and accurate method for assessing the number and size of MS lesions in the brain. The position, size, morphological variation, intensity resemblance with normal brain tissues, and inter-subject anatomical variance of MS lesions in brain MRI make precise diagnosis, characterisation, and quantification exceedingly challenging. In this paper, we explored a technique for detecting and segmenting MS lesions in which the fundamental processes are background creation and binarization with a global threshold. To link the normal tissues close to the margins, we perform a three-phase level set and then add a third-phase segmented region using a contour of the brain. To create an adaptable setting, we must first eliminate lesions from the brain, saving only the brain's largest linked area and the corpus callosum. Selecting the threshold using entropy and standard deviation via the binarization approach is followed by non-gamut image enhancement. The proposed method detects lesions in the same locations as a radiologist and with the same precision in terms of size and number. As a result of its flexibility, the suggested method has a range of applications in clinical practise for identifying MS lesions in MRI. When compared to more contemporary methods, the proposed method has a smaller margin of error and higher accuracy. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Price Estimation of Used Cars Using Machine Learning Algorithms 3rd

Price Estimation of Used Cars Using Machine Learning Algorit...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

作者： Valarmathi, B. Gupta, N. Srinivasa Santhi, K. Chellatamilan, T. Kavitha, A. Raahil, Armaan Padmavathy, N. Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced Sciences Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031289743

In this study, machine learning (ML) techniques are employed to predict used car prices. Several features are used to calculate the price of used cars, but in this paper, we find efficient ways to find the most precise car prices. Despite the fact that there are websites offering this service, they could not employ the most precise prediction system. It is also possible to predict a used car's true market value using a variety of models and techniques. It's important to understand their genuine market value before buying or selling. Both buyers and sellers will be benefitted from these accurate predictions. Support Vector regression, Random Forest regression, and CatBoost regression techniques are used in the proposed system. In the existing method [13], mean absolute error for decision tree regression was 0.6711, which was the least among other algorithms like Linear regression, Lasso regression, Ridge regression, Bayesian Ridge regression, and etc., they used. In the proposed system, mean absolute error (MAE) for Support Vector regression, CatBoost regression and Random Forest regression techniques are 0.1459, 0.1371 and 0.1284 respectively. The prices of second hand/used cars are predicted using the CatBoost regression, Support Vector regression, and Random Forest regression techniques. The accuracy of these algorithms are 86.28%, 85.40% and 87.16%. Among these three algorithms, Random Forest regression gives the least MAE of 0.1317 and the highest accuracy of 87.16%. © 2023, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Forecasting

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Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

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computers, Materials & Continua 2023年第3期74卷 6703-6717页

作者： K.Priyadarsini Arun Kumar Sivaraman Abdul Quadir Md Areej Malibari Department of Data Science and Business Systems School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChennai603203India School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Department of Industrial and Systems Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

With the rapid evolution of Internet technology,fog computing has taken a major role in managing large amounts of *** major concerns in this domain are security and ***,attaining a reliable level of confidentiality in the fog computing environment is a pivotal *** different types of data stored in the fog,the 3D point and mesh fog data are increasingly popular in recent days,due to the growth of 3D modelling and 3D printing ***,in this research,we propose a novel scheme for preserving the privacy of 3D point and mesh fog *** Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness,deterministic nature,sensitivity to initial conditions,ergodicity,*** boost encryption efficiency significantly,in this work,we propose a novel Chaotic Cat *** sequence generated by this map is used to transform the coordinates of the fog *** improved range of the proposed map is depicted using bifurcation *** quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate *** also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical *** experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

关键词： Chaotic cat map fog computing encryption 3D point fog 3D mesh

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