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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Neema, N. Prathibha, R.J. Sonali, N.M. Jss Science and Technology University Department of Information Science and Engineering Mysuru India Jss Science and Technology University Department of Computer Science and Engineering Mysuru India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Strokes are the second leading cause of mortality and also long-term disability. A blockage in the blood flow to the brain or a broken supply line to the brain causes the sudden death of cerebrum cells;this is known as brain-stroke. There are three primary types of brain stroke viz., I) Ischemic stroke, ii) Hemorrhagic stroke and iii) Transient ischemic stroke. This paper proposes the analysis and classification of occurrence of brain stroke types using machine learning approaches. In this paper, dataset is taken from a medical institute containing 13485 records. In the pre-processing stage, duplicate records and inconsistent data are eliminated and missing data are imputed in the dataset. Later records in the dataset are classified into three types of strokes using machine learning methods. The Random Forest classifier gave the best accuracy of 95.46% with the dataset split into 80:20 ratios for training and testing data respectively. This study focuses on developing a system that helps to take precautionary measures by the patient at an earlier stage for a healthy life. © 2022 IEEE.

关键词： Classification (of information)

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IoT based Women Safety Bag

IoT based Women Safety Bag

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, M. Ashok Pradeepa, P. Muthu, A. Essaki Krishnan, R. Santhana Saravanan, K. Narayanan, K. Lakshmi SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations. © 2023 IEEE.

关键词： Crime

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Soft Set Based Clustering and Its Comparison on Categorical Data 9

Soft Set Based Clustering and Its Comparison on Categorical ...

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9th IEEE information technology International Seminar, ITIS 2023

作者： Riyadi Yanto, Iwan Tri Waishiang, Cheah Hidayat, Rahmat Wahyudi, Rofiul Suprihatin Apriani, Ani Universitas Ahmad Dahlan Information System Department Yogyakarta Indonesia Universiti Malaysia Sarawak Faculty of Computer Science & IT Samarahan Sarawak Malaysia Politeknik Negeri Padang Information Technology Department Padang Indonesia Universitas Ahmad Dahlan Islamic Banking Department Yogyakarta Indonesia Institute Teknologi Nasional Yogyakarta Yogyakarta Indonesia

ISBN: (纸本)9798350306835

Categorical data clustering is problematic since it is difficult or complex to determine how comparable the data is. Several methods, most recently centroid-based strategies, have been developed to reduce the complexity of the similarity of categorical data. These methods nevertheless result in lengthy processing durations. Another method, soft set-based clustering (SSC), based on the probability function of multivariate multinomial distributions, is suggested in this article. Soft sets are used to represent the data, and each soft set has a probability for each object. The joint cluster distribution function determines the probability for each object after the multivariate multinomial distribution function. The connected cluster would receive the highest likelihood. Benchmark data sets from UCI machine learning are used to compare the performance of the approach to the baseline techniques. The outcomes demonstrate that the suggested strategy performed better in purity, rank index, and calculation time. © 2023 IEEE.

关键词： Distribution functions

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RF-Enhanced Pavement Markings for Mobile Robot Lane Detection 19

RF-Enhanced Pavement Markings for Mobile Robot Lane Detectio...

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19th IEEE International Conference on Automation science and Engineering, CASE 2023

作者： Suo, Dajiang Li, Heyi Bhattacharyya, Rahul Melià-Seguí, Joan Sarma, Sanjay E. Massachusetts Institute of Technology The Department of Mechanical Engineering CambridgeMA United States Faculty of Computer Science Multimedia and Telecommunication Barcelona Spain

ISBN: (纸本)9798350320695

The ability to detect and keep in lanes is crucial for the safe operation of autonomous mobile robots in construction sites and their coordination with humans in autonomous ports or logistic centers. While computer vision-based lane detection algorithms perform well under normal conditions, their performance may degrade under low visibility conditions and in adverse weather. Since robots are not constrained by human perception limits, this paper proposes a radio frequency (RF) pavement marking system that builds on Radio Frequency Identification (RFID), a short-range communication technology, to provide lane-detection assistance. We present not only the hardware designs for the RFID systems on both vehicles and roads but also a filtering algorithm to mitigate the noise in the backscattered RF signals for lane detection. Experimental results show that the information on lane keeping provided by the RF pavement markings aligns with the visual channel when mobile robots move at a speed of less than 40 miles per hour. © 2023 IEEE.

关键词： Mobile robots

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Multilayer Gaussian Feature Extraction Algorithm for Sky Image Classification 4th

Multilayer Gaussian Feature Extraction Algorithm for Sky Ima...

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4th International Conference on Advances in Computational science and Engineering, ICACSE 2023

作者： Petwan, Montha Ku-Mahamud, Ku Ruhana Saengpetch, Piyawat Zamin, Norshuhani Faculty of Science and Technology Suratthani Rajabhat University Khun Taleay Suratthani Muang Thailand School of Computing Universiti Utara Malaysia Kedah Sintok Malaysia Department of Information and Communications Technology College of Engineering Shibaura Institute of Technology Tokyo Japan College of Computer Studies De La Salle University Manila Philippines

ISBN: (纸本)9789819729760

The classification of sky images holds significant importance in the field of meteorology as it enables the accurate prediction of weather patterns and climatic conditions. The multi-view features of super pixels have been employed in many algorithms for detection and recognition of various cloud types. However, these algorithms neglect the local structure of region and the mean information of pixel features. These aspects are crucial in capturing the discriminative information necessary for complex data distributions in the sky images. This paper proposes the Multilayer Gaussian (MLG) algorithm to extract the features of sky images using the Gaussian smoothness standard deviation techniques. This method involves capturing the average information within each local patch by calculating the mean of the pixel values. Hence, the informative feature that facilitates the distinction of cloud formations in sky image primarily resides in the average information included within each localized region. Moreover, it provides a comprehensive representation of the local patch and exhibits resistance to noise. The Kiel and SWIMCAT datasets were used to evaluate the proposed MLG algorithm based on the metrics number of extracted features, similarity values, precision, recall, and f-measure using the decision tree and kernel support vector machine. The experimental results show that the performance of MLG surpassed all benchmark feature extraction algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Mobility-Aware Power Control and User Scheduling for Downlink V2I Networks

Mobility-Aware Power Control and User Scheduling for Downlin...

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IEEE Conference on Vehicular technology (VTC)

作者： Yujie Wang Momiao Zhou Yanshi Sun Kan Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Faculty of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations.

关键词： Wireless communication Vehicular and wireless technologies Vehicle-to-infrastructure Power control Transforms Interference Programming Downlink Optimization Signal to noise ratio

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Automatic License Plate Recognition system for Vehicles Using a CNN

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computers, Materials & Continua 2022年第4期71卷 35-50页

作者： Parneet Kaur Yogesh Kumar Shakeel Ahmed Abdulaziz Alhumam Ruchi Singla Muhammad Fazal Ijaz Department of Computer Science Chandigarh Group of CollegesLandran140307India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-AhsaSaudi Arabia Department of Research InnovationsSponsored Projects&EntrepreneurshipChandigarh Group of CollegesLandran140307India Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Automatic License Plate Recognition(ALPR)systems are important in Intelligent Transportation Services(ITS)as they help ensure effective law enforcement and *** systems play a significant role in border surveillance,ensuring safeguards,and handling vehicle-related *** most effective approach for implementing ALPR systems utilizes deep learning via a convolutional neural network(CNN).A CNN works on an input image by assigning significance to various features of the image and differentiating them from each *** are popular for license plate character ***,little has been reported on the results of these systems with regard to unusual varieties of license plates or their success at *** present an efficient ALPR system that uses a CNN for character recognition.A combination of pre-processing and morphological operations was applied to enhance input image quality,which aids system *** system has various features,such as the ability to recognize multi-line,skewed,and multifont license *** also works efficiently in night mode and can be used for different vehicle *** overall accuracy of 98.13%was achieved using the proposed CNN technique.

关键词： Deep learning intelligent transportation services morphological convolutional neural network accuracy

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Protecting Vulnerable Road users using IoT-CNN for Safety Measures 5

Protecting Vulnerable Road users using IoT-CNN for Safety Me...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Karpagalakshmi, R.C. Sampoornam, M. Maria Vinya, Viyyapu Lokeshwari Monikapreethi, S.K. Yuvaraj, S. Srinivasan, C. Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Ranga Reddy India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379945

The increasing number of road accidents involving Vulnerable Road Users (VRUs) highlights the urgent need for innovative safety solutions. This study proposes a novel approach that leverages IoT and Convolutional Neural Network (CNN) technologies to enhance VRU safety. By deploying IoT sensors on vehicles and infrastructure, the proposed model can accurately track VRU locations and environmental conditions in real-time. A CNN-based model is developed to robustly detect and classify VRUs under various traffic scenarios, with continuous improvement through learning. The integration of IoT and CNN enables real-time communication and timely warnings, facilitating proactive interventions to prevent accidents. Experimental evaluations demonstrate the effectiveness of the proposed system in improving VRU safety. This research offers a promising solution for reducing road accidents and creating safer urban environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification Emoticon Meaning using Facial Expression via Inception V3 as Well as KNN Methods 4

Identification Emoticon Meaning using Facial Expression via ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Geeth, M.C.S. Varma, M. Krishna Satya Bhagya Sri, G. Mythili, R. Naser, Sarmad Jaafar Hassan, Enas Kumaraguru College of Technology Department of Computer Applications Coimbatore India S R K R Engineering College Department of It Bhimavaram India Hyderabad India Srm Institute of Science and Technology Department of Information Technology Chennai Ramapuram India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Recognising emotions in people is essential to understanding human behaviour since it allows for correct perception and suitable reactions to people's emotional states. In order to get a deeper understanding of complicated human behaviour, researchers meticulously examine a variety of physical indications across modalities, such as facial expressions, verbal signals, and body language. The ability of spontaneous multi-modal emotion identification to collect and understand emotions in real time is shown by the current spike in interest in this field, highlighting the importance of combining audio input with facial expressions. In order to tackle the difficulties associated with speech emotion identification, this research makes use of the RAVDESS dataset and applies deep learning techniques, such as inception V3 and KNN models. The librosa package is used to extract spectrograms, and KNN models are employed to recognise speech emotions. The study also combines methods from video-based emotion recognition with point-wise convolution for frame analysis. The main goal of this project is to improve real-time emotion identification, which has potential uses in the fields of software engineering, gaming, education, and personalised online experiences. Metrics like accuracy, precision, and confusion matrix may be used to do a thorough assessment of the suggested multimodal emotion detection system. © 2024 IEEE.

关键词： Emotion Recognition

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Iterative Process Fusion Method in Uncovering Pathways of Vascular Structures Based on Hyperspectral Imaging 23

Iterative Process Fusion Method in Uncovering Pathways of Va...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Zhang, Yi-Xiu Li, Hsiao-Chi Hsu, Jia-Lien Jiang, Liang-Ting Fu Jen Catholic University Department of Computer Science and Information Engineering Taiwan National Taipei University of Technology Department of Electrical Engineering Taiwan Fu Jen Catholic University Hospital Taiwan

ISBN: (纸本)9798331528041

This study utilizes a hyperspectral imaging system for in vitro imaging of blood vessels. Image preprocessing techniques, including Savitzky-Golay filtering, median pooling, gamma correction, CLAHE, and bottom hat transformation, are utilized to smoothen the images and improve the contrast between blood vessels and skin. The study introduces an iterative process that integrates an Automatic Target Generation Procedure (ATGP) for blood vessel extraction, an Orthogonal Subspace Projection (OSP), and an Edge-Preserving Filter for target detection and boundary extraction. The process will visually demonstrate the pathways of the vascular structures from the images. The iterative process was applied to compare the original and enhanced images. The results showed that the original images' average IOU and Dice coefficients were 0.11 and 0.19, respectively, while the enhanced images showed improvements to 0.29 and 0.44, respectively. Furthermore, when evaluated using the skeleton similarity metrics, the curve similarity accuracy of the original and enhanced images was 0.71 and 0.89, respectively. These results confirm the significant effectiveness of the image enhancement and iterative process fusion method (IEPF) in improving vascular segmentation performance. © 2024 IEEE.

关键词： Hyperspectral imaging

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