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Vision based umpire detection method for event extraction in cricket video based on HOG and color image segmentation 1

Vision based umpire detection method for event extraction in...

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1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable technologies, ICAECT 2021

作者： Nandyal, Suvarna Kattimani, Suvarna L. Department of Computer Science and Engineering P. D. A. College of Engineering Karnataka Kalaburgi585102 India Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G.Halakatti College of Engg. and Tech. Karnataka Vijayapur586103 India

ISBN: (纸本)9781728157900

As of late, there has been expanded interest in Video Summarization and Automatic Highlights age. One of the most watched and a played sport is cricket, especially in South Asian Countries. In cricket sport video, Umpire has the force for settling on huge choices about functions in the field. In this work, the manually chosen Recorded Video of Some International Twenty-Twenty cricket match video (T20*20) is used as dataset for Umpire identification inside the sport of cricket. The proposed dataset is assessed as a starter help for creating frameworks to consequently produce cricket features. With the growing increase of the utilization of technology in sports, this paper points basically at the Umpire Identification utilizing Area-Of-interest based Histogram of Oriented Gradients (AOI-HOG) Feature Extraction strategy for Human Identification. It expects to give Color Image Segmentation technique utilizing pixel wise color information to support vector machine (SVM) classification for Umpire Identification while protecting the arrangement of highlights. Test proof shows that the proposed technique accomplishes the Accuracy of 94.57%. Proposed one efforts towards successful segmentation results and computational conduct, and lessens the time and expands the norm of color image segmentation. © 2021 IEEE

关键词： Support vector machines

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Comparative Analysis of ResNet, EfficientNet, and MobileNetV2 for Detecting Skin Conditions in Deep Learning Models

Comparative Analysis of ResNet, EfficientNet, and MobileNetV...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Rajkumar Shreenath Raghunath Siriyala Ananya Sharma D. Naga Sujith Kumar Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Vellore Campus Tamil Nadu India B.Tech-Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology VIT University Vellore Campus Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Comparing the effectiveness of Skin Condition Detection using ResNet, EfficientNet and MobileNetV2 deep learning models. The 3 skin conditions that have been considered are Acrochordon, Lichenoid Keratosis and Vascular Lesions. The ResNet Model, EfficientNet Model and MobileNetV2 Model have been assessed against one another. ResNet and EfficientNet both predicted the skin condition with an accuracy of 92.1875% which is greater than MobileNetV2, which predicted the skin condition with 90.625% accuracy. Through this research, it has been concluded that ResNet and EfficientNet are significantly better than MobileNetV2 in terms of training models to accurately detect skin conditions.

关键词： Deep learning Training Analytical models Accuracy Quantization (signal) Green communications Skin Reliability Lesions Tuning

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An Innovative Method for Mobile Network Payment Security System based on hybrid LSTM-Ensemble Learning-based Model

An Innovative Method for Mobile Network Payment Security Sys...

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Advancement in technology (ICONAT), International Conference for

作者： N Sujatha S Pramod S Aravindh balagopala Ramesh babu S. Rukmani Devi M. Ananthi Department of Mathematics B. M. S. College of Engineering Bengaluru Karnataka India Department of Mathematics Nitte Meenakshi Institute of Technology Yelahanka Bangalore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamilnadu India Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Tamilnadu India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. Mobile phones have practically become people’s constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer’s biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTM-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTM and Ensemble Learning.

关键词： Training Accuracy Cellular phones biological system modeling Fingerprint recognition Vectors Mobile handsets Security Ensemble learning Long short term memory

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Special issue in honor of V.I. Tatarskii (1929–2020)

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Waves in Random and Complex Media 2023年第5-6期33卷 1169-1170页

作者： Akira Ishimaru Gary brown Valery Zavorotny Saba Mudaliar a Department of Electrical Engineering University of Washington Seattle WA USA b Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA c University of Colorado Boulder/CIRES Boulder CO USA d Air Force Research Laboratory Sensors Directorate Wright-Patterson AFB OH USA

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High-Precision Positioning in GNSS-Challenged Environments: A LiDAR-based Multi-Sensor Fusion Approach with 3D Digital Maps Registration

TechRxiv

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techRxiv 2023年

作者： Mounier, Eslam Elhabiby, Mohamed Korenberg, Michael Noureldin, Aboelmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada The Department of Computer and Systems Engineering Ain Shams University Cairo11535 Egypt Micro Engineering Tech. Inc. Canada The Public Works Department Ain Shams University Cairo11535 Egypt The Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada

Autonomous vehicles (Avs) necessitate highly accurate and robust positioning capabilities. This calls for developing innovative algorithms that leverage the diversity of sensors and systems available in AVs to fulfill such demands across various environmental and operational conditions. This paper presents a multi-sensor positioning and navigation system that leverages cost-effective commercial-grade sensors for GNSS-challenging urban and indoor environments. The system fuses onboard motion sensor data with LiDAR point clouds registered to high-accuracy 3D digital maps to achieve sustained decimeter-level positioning. Key contributions include accurate LiDAR scans geo-referencing with motion compensation, efficient map-to-map registration, and an effective decentralized fusion. Real-world driving data from downtown Kingston, Ontario, Canada, and a high-accuracy 3D city geodatabase were used to examine the proposed methods’ performance and benefits. Results demonstrate the efficacy of the proposed technique, achieving accurate positioning with an average RMSE of 20cm horizontally and 13cm vertically, and a sustainable positioning sub-meter level of positioning accuracy 100% of the time. The proposed method was also able to sustain high precision positioning in such GNSS-denied environments with position errors of less than 50cm for 96.8% of the time and less than 30cm for 91% of the time. The performance achieved demonstrates that the proposed system is a practical option for the positioning and navigation of self-driving cars and has the potential for cooperative mapping and updating 3D city maps. © 2023, CC bY.

关键词： Autonomous vehicles

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Predicting the Quantity of Municipal Solid Waste using XGboost Model 3

Predicting the Quantity of Municipal Solid Waste using XGBoo...

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3rd International Conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Jayaraman, Vaishnavi Parthasarathy, Saravanan Lakshminarayanan, Arun Raj Singh, Hiran Kumar B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9780738146270

The quantity of Municipal Solid Waste (MSW) gets intensified, based on various factors such as population growth, monetary status and consumption patterns. The insufficiency of elementary trash data is a critical problem for managing the MSW. In this study, the goal is to forecast the MSW generation of Northern Ireland. A precise model was developed to estimate the total amount of waste produced for every quarter of the year, by employing the Machine Learning techniques. The seasonal ARIMA (s-ARIMA) and Extreme Gradient boosting (XGboost) models were employed to estimate the amount of waste produced. On comparing both the models, XGboost performed better. Thus, the parameters of the XGboost were tuned to yield the optimal outcome. The XGboost with the tuned hyperparameters achieved an optimum result with the higher coefficient of determination (R2) value as 0.5325 and lower RMSE value of 13215.97. The prediction of the MSW weight would help the decision-makers in treating and disposing solid waste appropriately. © 2021 IEEE.

关键词： Decision making

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Particle Swarm Optimization for Efficient brain Tumor Classification Using InceptionV3 Deep Learning Model

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Akhila G. Prabaharan K. Pandiiyan S. L. Swarna Hussain. A U. Sakthivelu Department of Artificial Intelligence and Machine Learning B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science with Artificial Intelligence SRM Arts and Science College Chengalpattu India Department of Computer Science Science and Engineering Sona College of Technology Salem Tamil Nadu India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a novel framework for brain tumor classification utilizing a combination of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed by SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying brain tumors. This research contributes to the advancement of medical image analysis and provides a valuable tool for supporting clinicians in the early diagnosis and treatment of brain tumors.

关键词： Deep learning Accuracy brain tumors Predictive models brain modeling Planning Reliability Particle swarm optimization Optimization Residual neural networks

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5G Multi-bS Positioning: A Decentralized Fusion Scheme

arXiv

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arXiv 2023年

作者： Saleh, Sharief bader, Qamar Elhabiby, Mohamed Noureldin, Aboelmagd The Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada Lab Department of Electrical and Computer Engineering Royal Military Collage of Canada KingstonONK7K 7B4 Canada Micro Engineering Tech. Inc the Public Works Department Ain Shams University Cairo11566 Egypt

Fifth-generation (5G) networks are expected to provide high-precision positioning estimation utilizing mmWave signals in urban and downtown areas. In such areas, 5G base stations (bSs) will be densely deployed, allowing for line-of-sight (LOS) communications between the user equipment (UE) and multiple bSs at the same time. Having access to a plethora of measurement sources grants the need for optimal integration between the bSs to have an accurate and precise positioning solution. Traditionally, 5G multi-bS fusion is conducted via an extended Kalman filter (EKF), that directly utilizes range and angle measurements in a centralized integration scheme. Such measurements have a non-linear relationship with the positioning states of the filter, giving rise to linearization errors. Counter to the common belief, an unscented Kalman filter (UKF) will fail to totally eradicate such linearization errors. In this paper, we argue that a decentralized integration between 5G bSs would fully avoid linearization errors within the filter and significantly enhance the positioning performance. This is done by fusing position measurements instead of directly fusing range and angle measurements, which inherently leads to a linear measurement model by design. The proposed decentralized KF method was evaluated in a quasi-real simulation setup provided by Siradel using a real trajectory in Downtown Toronto. The experiments compared the performance of decentralized KF integration to that of centralized EKF and UKF integration schemes. It was shown that the proposed method outperformed both UKF and EKF implementations in multiple scenarios as it significantly decreased the RMS and maximum 2D positioning errors, achieving decimeter-level accuracy for 93.9% of the time. © 2023, CC bY.

关键词： 5G mobile communication systems

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Innovative techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits

Innovative Techniques in the Construction of Very Large Scal...

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Communications and Information technologies (GCCIT), Global Conference on

作者： V. Saravanan G. Sasikala P. Gomathi D. Kirubakaran A. Rajalingam Jubert R. Pagunuran Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE V.S.B. Engineering College Department of Computer Science and Engineering R.M.K Engineering College Engineering Department College of Engineering and Technology University of Technology and Applied Sciences Shinas Sultanate of Oman

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems.

关键词： Performance evaluation Design methodology Machine learning Very large scale integration Thermal management Routing Topology Timing Leakage currents Signal integrity

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Individual Privacy Protection in Cybersecurity Using Data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M. Arun bhavadharani K b. R. Supreeth Sumit Pundir K Pradeepa K. Abdul Razak Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamil Nadu India Department of Computer Science Oxford Degree and PG College Bangalore India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu Department of Mathematics K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises.

关键词： Support vector machines Data privacy Data analysis Accuracy Scalability Telecommunication traffic Data collection Privacy breach Protection computer security

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