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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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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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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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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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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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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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Skills Extraction from Entities in Arabic CVs 5

Skills Extraction from Entities in Arabic CVs

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5th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2023

作者： Sadiq, Mohamed Abdul Karim Shanmugam, Thirumurugan Al-Fannah, Nasser Mohammed Kausar, Firdous College of Computing and Information Science University of Technology and Applied Sciences Suhar Oman Fisk University Department of Mathematics and Computer Science NashvilleTN United States

ISBN: (纸本)9798350323832

Named Entity Recognition in low resource languages such as Arabic is relatively less accurate. Despite this, many applications attempt query-document matching, which necessitates entity resolution. We explore certain possible solutions in this regard to enhance the matching process. A system is proposed to build a sub graph of terms in the Knowledge Graph. These terms specifically deal with skills in the Arabic documents related to the Human Resources domain. The accuracy measures of skills extraction using two different Language Models viz., SparkNLP and Hatmi are presented. The results are promising and have scope for improvement in entity resolution. © 2023 IEEE.

关键词： Knowledge graph

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A Joint DOA and Range Estimation Technique for Underwater Transportation system based on Compressive Sensing

A Joint DOA and Range Estimation Technique for Underwater Tr...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Song, Hai-Yan Yang, Chang-Yi Zhang, Yu-Jie Tang, Jia-Xin Gao, Jia- Qi Heilongjiang Institute of Technology School of Electrical and Information Engineering Harbin China National Penghu University of Science and Technology Department of Computer Science and Information Engineering Magong Taiwan Petro China Tarim Oil Field Company Korla China

ISBN: (纸本)9798350324174

Obstacle detection can help human to reduce risk on underwater transportation activities. In our work, we use compressive sensing technique to estimate the Direction of Arrival (DOA) and range for spatial targets especially in underwater transportation system. According to underwater acoustic propagation ray theory, the signal model for horizontal L-Shaped array is first constructed. Then, the joint DOA and range estimation is converted into an optimizing procedure, which can be finally solved by an efficient toolbox for Disciplined Convex Programming (CVX). The performance of our proposed method is discussed in the computer simulation results. © 2023 IEEE.

关键词： Compressed sensing

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A Comparative Analysis of SMS Spam Detection employing Machine Learning Methods 6

A Comparative Analysis of SMS Spam Detection employing Machi...

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6th International Conference on Computing Methodologies and Communication, ICCMC 2022

作者： Aliza, Humaira Yasmin Nagary, Kazi Aahala Ahmed, Eshtiak Puspita, Kazi Mumtahina Rimi, Khadiza Akter Khater, Ankit Faisal, Fahad Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Tampere University Faculty of Information Technology and Communication Sciences Tampere Finland Jadavpur University Department of Computer Science and Engineering India

ISBN: (纸本)9781665410281

In recent times, the increment of mobile phone usage has resulted in a huge number of spam messages. Spammers continuously apply more and more new tricks that cause managing or preventing spam messages a challenging task. The aim of this study is to detect spam message to prevent different cybercrimes as spam messages have become a security threat nowadays. In this paper, studies on SMS spam problems to perform a better accuracy using several different techniques such as Support Vector Machine, K-Nearest Neighbor, Naïve Bayes, Random Forest, Logistic Regression and some more are performed. The result indicated that Support Vector Machine achieved the highest accuracy of 99%, indicating it might be useful as an effective machine learning system for future research. © 2022 IEEE.

关键词： Support vector machines

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Examining Trusted Communication in Networked systems Through Cryptographic Security Algorithms

Examining Trusted Communication in Networked Systems Through...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Pandey, Vijay Kumar Jain, Nikita Raghavendra, R. Vivekananda Global University Department of Mechanical Engineering Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper examines the concept of today's reliance on verbal exchange in networked structures through the usage of cryptographic safety algorithms. Community security has emerged as a trendy vital tool to defend confidential records and ensure the integrity of contemporary essential data and offerings. Cryptographic protection algorithms are extensively used to defend communications against malicious attacks, tampering, and eavesdropping. This paper provides a top-level view of modern best practices for shielding statistics via cryptographic algorithms and examines the contemporary cryptographically secure communique techniques. Furthermore, implementations of several ultra-modern popular cryptographic algorithms are discussed, which include AES, RSA, DH, and SHA-2. ultimately, the paper examines the blessings and disadvantages of today's cryptographic safety algorithms and how those algorithms can bolster the safety and trustworthiness of modern-day networked structures. Its technical abstract gives an exam on present-day cryptographic security algorithms to decorate the trustworthiness of state-of-the-art communique in a networked system. In particular, digital certificates and asymmetric encryption techniques are discussed as the two essential techniques for imposing cryptographic security in computer networks. This look explores using a proposed authentication protocol that mixes a digital certificate's authority and an asymmetric key pair for verifying the identity of trendy communicators in the network. Moreover, this paper addresses capacity security troubles that may get up when cryptographic safety algorithms are, in any other case, unprotected. The realization affords a precis state-of-the-art the take look at's fundamental findings. It gives standard pointers on maintaining the safety and trustworthiness of brand-new networked systems and conversations. © 2024 IEEE.

关键词： algorithms Community confidential Cryptographic integrity

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