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On Road Vehicle Detection and Tracking in Various Weather Conditions

Journal of The Institution of Engineers (India): Series B 2022年 1-10页

作者： Anandhalli, Mallikarjun Baligar, Pavana Baligar, Vishwanath P. Hiremath, Prakash S. Department of Electronics and Communication Engineering KLS Gogte Institute of Technology Belagavi Belagavi India Department of Information Science and Engineering Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Laxmeshwar India Department of Computer Science and Engineering KLE Technological University Hubballi India Department of Master of Computer Application KLE Technological University Hubballi India

The paper aims in presenting distinctive pattern in identification of vehicles using basic and sequential algorithms. Vehicle detection and classification are crucial in traffic control and management decision-making. Vehicle characteristics are critical elements for identifying vehicles. For vehicle detection, the programmer additionally employs thresholds and corner detection techniques. This technique shall be used in order to extract the interest points from an image, while background subtraction method can remove unwanted points. Within a region of object, many corner points will be compacted, which is regarded an initial prerequisite. Thickly surrounded ends are clustered;which provides clue of points related to each vehicle, which is an important part in vehicle identification. The non-vehicle region is separated when the grouping is completed. The corner points are traced using the LK algorithm to ensure that they remain stable. A 93.95% success rate is achieved. © 2022, The Institution of Engineers (India).

关键词： Decision making

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Neural Network Implementation for Battery Failure Detection in Electric Vehicles

Neural Network Implementation for Battery Failure Detection ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Vijitha, S. Hebri, Dheeraj Singh, Sangeeta Manohara, M. Ishrat, Mohammad Joseph, D Raja Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai600117 India Srinivas Institute of Technology Department of Master of Computer Application Karnataka Mangaluru574143 India JSS Academy of Technical Education Department of Electrical Engineering Uttar Pradesh Noida201301 India Mohan Babu University School of Engineering Andhra Pradesh Tirupati517102 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Hindustan Institute of Technology and Science School of Aeronautical Sciences Tamil Nadu Chennai603103 India

ISBN: (纸本)9798350319200

Soil destruction and global warming have recently come to the forefront of discussion in industrialised nations as governments strive to accommodate the rising demands of their citizens. The demand for zero-emission electric cars has increased as a result of international competitiveness and technological advancements (EVs). Concerns about the high voltage of increasing numbers of electric cars are shared by an increasing number of individuals. Since the system of batteries may be at responsible for over 30% of EV accidents, it is vital to investigate how problems with LIBs are recognized. Many different kinds of problems make it hard to fix EV's LIB. Fast and precise diagnosis of battery pack problems is crucial for the immediate and ongoing safety of EV operation. Utilizing models of neural networks like multiple hidden layers (MLP) or nonlinear activation functions, this research provides a mechanism for identifying and fixing problems with electric vehicle batteries (RBF). To generate information for the BFD system, battery simulations are done in MATLAB. Accuracy may be improved by performing pre-processing steps on the information once it has been generated. After training, the two models are put to the test to see how well they perform. There are both positive and negative measures that may be used to determine which model is the best. © 2023 IEEE.

关键词： Accuracy Battery Electric Vehicle Fault Model Neural Network

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Land Use and Land Cover Classification Using Recurrent Neural Networks with Shared Layered Architecture 12

Land Use and Land Cover Classification Using Recurrent Neura...

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12th International Conference on computer Communication and Informatics, ICCCI 2022

作者： Vignesh, T. Kanimozhi, K.V. Sathish, R. Kumar, R. Prasanna Jeyavathana, R. Beaulah Ezhumalai, P. Srm Institute of Science and Technology Department of Master of Computer Application Chennai Ramapuram India Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Chennai India J.N.N Institute of Engineering Department of Computer Science and Engineering Chennai India Department of Computer Science and Engineering Amrita School of Engineering Amritha Vishwa Vidyapeetham Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India R.M.D Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665480352

In the computerized world the uniqueness of information security is fundamental. Biometric frameworks utilizing individual physiological or conduct ascribes which are turning out to be ordinarily normal in giving IT insurance and getting to powerless places, for example, air terminals, organizations, medical care, the military, and so forth A multimodal biometric framework that offers an individual personality base on different physical or interior attributes is liked to permit a biometric framework to work effectively in assorted applications and conditions. To additionally further develop security, multi-modular biometric confirmation could be utilized by defeating the requirements looked by unimodal biometric validation frameworks. The proposed work clarifies facial, unique finger impression and iris-based calculations, particularly in multimodal biometrics. In this first work a coded multi-work acknowledgment of the face under unusual lighting conditions has been executed. Face picture is taken as a contribution to this proposed work. Strategies dependent on normalization or enlightenment is proposed to accomplish great outcomes under lighting changes. The Gabor highlights, LBP elements and step similarity qualities are then gotten from pre-handled facial pictures. The removed attributes are combined with Z score level combination and primary handling. The facial pictures are perceived by the ANFIS classifier as indicated by the combined keys. The trial discoveries show that the proposed technique is showing improvement over the current framework. It doesn't, in any case, yield compelling shading face picture acknowledgment execution. Therefore, the determined attributes are consolidated at the level of the match to make the multi-biometric design and perform keyage utilizing a combination subject to thickness. LVQ is in this way used to coordinate with the example. The discoveries closed, comparative with current K -Closest Neighbor calculations (KNN), Support

关键词： Support vector machines

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IoT to Digital Twin: A Futuristic Smart Farming

IoT to Digital Twin: A Futuristic Smart Farming

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2024 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2024

作者： Akshay, M.J. Premasudha, B.G. Hegde, Seema B. Jawaharlal Nehru New College of Engineering Visvesvaraya Technological University Department of Information Science and Engineering Belagavi Shivamogga India Siddaganga Institute of Technology Visvesvaraya Technological University Department of Master of Computer Applications Belagavi Tumakuru India Siddaganga Institute of Technology Visvesvaraya Technological University Department of Electronics and Communication Belagavi Tumakuru India

ISBN: (纸本)9798350364040

The global population is rising and there is a need for smartness in agriculture. With the advancement in technology, it is possible to handle agricultural activities in a smart manner. The Internet of Things, Digital Twins, Artificial Intelligence, Machine Learning and Big data can be used for agriculture. Improving the farming efficiency and quality is a major challenge. Most of the day-to-day activities of the farms can be automated and monitored by the farmers. Moreover, there is a need for real-time analysis of the collected data, which can be more informative and realistic and helps in prediction of the future and decision making. The digital twins can be introduced to agriculture, which can help in various activities and making predictions. The digital twins and IoT can be a useful combination for agricultural activities. The various applications, parameters used, and the modeling adopted for digital twins for agriculture is highlighted in this article. © 2024 IEEE.

关键词： Decision making

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SafeRoute: Pioneering Online Ride Booking with Pega & ML-Powered Safety Measures

SafeRoute: Pioneering Online Ride Booking with Pega & ML-Pow...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： D. Rajalakshmi P.S. Immaculate S. Senthil Pandi A. Rehash Rushmi Pavitra V. Vijayalakshmi S. Sharanya Data Science and Business Systems SRM Institute of Science and Technology Chennai India Master of Business Administration Sri Sai Ram Institute of Technology Chennai India Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The advent of online ride booking services has revolutionized the transportation industry, providing users with convenient and efficient means of commuting. However, ensuring the safety of passengers and drivers remains a paramount concern. This project introduces an innovative solution that leverages Pega, a leading business process management platform, coupled with machine learning algorithms to enhance the safety measures within an online ride booking service. The proposed system integrates seamlessly into the existing online ride booking infrastructure and introduces advanced safety mechanisms based on realtime data analysis. Machine learning models are employed to predict and mitigate potential risks, ensuring a secure and reliable experience for both passengers and drivers.

关键词： Electric potential Machine learning algorithms Transportation industry Soft sensors Transportation Traffic control Safety Security Reliability Recruitment

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Revolutionizing Plant Disease Management Through Image Processing technology

Revolutionizing Plant Disease Management Through Image Proce...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Gaurav Kumar, Ankit Singhal, Manoj Singh, Kamred Udham Kumar, Lalan Singh, Teekam Gla University Department of Computer Engineering & Applications UP Mathura India Gl Bajaj Institute of Technology and Management Department of Information Technology India School of Computing Graphic Era Hill University Dehradun India Gl Bajaj Institute of Technology and Management Department of Master of Computer Application India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350323917

Agricultural productivity plays a vital role in the economy, and disease detection in plants is crucial for the agriculture sector as plant diseases are widespread. Failure to take appropriate care can lead to serious consequences for plant quality, quantity, or productivity. For instance, a disease called little leaf disease has been found in pine trees in the United States, which can severely affect plant health. Automated methods for detecting plant diseases are useful as they reduce the need for manual inspection in large crop fields, and they can identify disease symptoms as soon as they appear on plant leaves. This paper presents an algorithm for image segmentation for the automatic identification and classification of plant leaf diseases. It also discusses various disease classification methods that can be used for plant leaf disease detection. Genetic algorithms are used for image segmentation, which is a crucial aspect of disease detection in plant leaves. The paper focuses on identifying the Powdery Mildew disease in organic grape products, which can cause significant damage if not detected in time. The accurate identification and classification of diseases from leaf images are crucial for preventing significant losses in output and agricultural inputs. The proposed process can help improve the accuracy and efficiency of disease detection in plants and lead to better agricultural productivity. © 2023 IEEE.

关键词： Fuzzy logic

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Hybrid Deep Learning Framework for Femur Bone Osteoporosis Classification: Leveraging Tuna Jellyfish Optimization and Segmentation Techniques

Hybrid Deep Learning Framework for Femur Bone Osteoporosis C...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： T G Halesh T.A Ashok Kumar P. Sathish School of Science and Computer Studies CMR University Bangalore India Department of Master of Computer Applications Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Osteoporosis weakens bones, making them more fragile and prone to fractures. This condition can result from changes in bone strength or a decline in bone mineral density and mass. Accurate classification of osteoporosis is crucial for diagnosing skeletal disorders, particularly in aging individuals. This study introduces a novel approach to classify femur bones from X-ray images using a hybrid segmentation method and an enhanced Tuna Jelly Fish Optimizer (TJFO) with Deep Convolutional Neural Networks (Deep CNN). The hybrid method integrates traditional image processing and deep learning for precise femur isolation, while TJFO fine-tunes the Deep CNN’s hyperparameters for improved classification. The model’s performance was evaluated using metrics like Accuracy, True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), and Negative Predictive Value (NPV), showing significant improvements in femur bone classification with balanced results across all metrics.

关键词： Measurement Deep learning Osteoporosis Accuracy Jellyfish Predictive models Software systems Convolutional neural networks X-ray imaging Optimization

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Privacy-Enhanced Data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

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computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication *** to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is *** storage capacity increases,additional storage solutions are *** leveraging deduplication,you can fundamentally solve the cost ***,deduplication poses privacy concerns due to the structure *** this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve *** the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is *** addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing *** proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： Computational intelligence cloud multimedia data deduplication

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Fake News Detection Review: Concepts, Tools, Research GAPS, and Possible solutions

Fake News Detection Review: Concepts, Tools, Research GAPS, ...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Sapana Yakkundi Rudragoud Patil Swetha Indudhar Goudar Dept. of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi Karnataka India Department. of Master of Computer Applications KLS Gogte Institute of Technology Belagavi Karnataka India

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news.

关键词： Accuracy Protocols Limiting Reviews Social networking (online) Voting Real-time systems Data models Internet Fake news

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