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Acceleration Consistency Analysis Based GNSS Spoofing detection for Autonomous Vehicles

Acceleration Consistency Analysis Based GNSS Spoofing Detect...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Cheng Zhang Hongmei Wang Hongxing Hu Shouwei Wang Hong Liu School of Software Engineering East China Normal University Shanghai China Information Engineering College Xinjiang Institute of Engineering Xinjiang China Department of Automotive Cybersecurity China Automotive Innovation Corporation Nanjing China

Global Navigation Satellite system (GNSS) positioning technology has been widely applied in Autonomous Vehicles (AVs), but these AVs suffer from potential security threats of GNSS spoofing attacks. This work proposes a GNSS spoofing detection algorithm for AVs. The algorithm checks the consistency of two acceleration series deceived from GNSS and IMU to detect GNSS spoofing. Ideally, the two acceleration series are consistent in normal conditions and inconsistent under spoofing. However, measurement noise and outliers may cause inconsistency even in normal conditions, which will produce false alarms. Thus, the work also develops a method to analyze acceleration series in the frequency domain and find frequency components that really have benefits to consistency checking. As a result, the algorithm can obtain a high detection accuracy and a low false alarm rate simultaneously in all attack scenarios proposed in the work.

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Prediction and detection of Liver Diseases using Machine Learning

Prediction and Detection of Liver Diseases using Machine Lea...

引用

technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： R.T. Umbare Omkar Ashtekar Aishwarya Nikhal Bhagyashri Pagar Omkar Zare Information Technology JSPM’s RSCOE Pune India

Liver is a very essential organ in the human body. It is essential to recognize or diagnose the disease early. This considerably aids in the early avoidance of disease with minimal medication. Conventional methods include Liver Function Tests and test results. Early detection of liver disease is very difficult. This is because symptoms of the disease become apparent only in the later stages of the disease. This system of Machine learning facilitates early disease detection, Identifying elements that lead to fatal liver impairment. Predicting the disease in its early stages is a difficult task for doctors and scientists due to the apparently sensitive signs. Effects will become apparent only when it is too late. The initiative seeks to use machine learning techniques to address this issue and improve the victims of the disease. Because there are few signs of liver disease, it is difficult to diagnose and symptoms usually do not appear until it is too late. The aim is to study and use a classification approach to distinguish between liver disease and healthy individuals, if diseased then further classified into the level of disease and its type. Also, precautions are provided for any symptoms. Consequently, ML techniques have identified liver disease in individuals.

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Detailed review on Integrated Healthcare Prediction system Using Artificial intelligence and Machine Learning

Detailed review on Integrated Healthcare Prediction System U...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Deepa Parasar Arif Ali N. Muthuvairavan Pillai Archana Shahi Badria Sulaiman Alfurhood Kumud Pant CSE Amity University Maharashtra Department of CSE Dev Bhoomi Uttarakhand University Dehradun Uttarakhand India Computer Science and Business Systems R.M.D Engineering College Kavarapettai Chitkara Business School Chitkara University India Department of Computer Sciences College of Computer and Information Sciences Princess Nourahbint Abdulrahman University Saudi Arabia Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology’s function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

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Diabetic Retinopathy Segmentation and Classification Using U-Net with Convolutional Neural Network

Diabetic Retinopathy Segmentation and Classification Using U...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Ghazi Mohamad Ramadan Myasar Mundher Adnan Zamen Latef Naser Bura Vijay Kumar KG Parthiban College of MLT Ahl Al Bayt University Karbala Iraq The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of Biomedical Engineering Dhaanish Ahmed Institute of Technology Coimbatore India

The early detection of Diabetic Retinopathy (DR) is necessary to aid doctors in treating retina patient to increase their existence rate. Due to the major imbalance dataset issue in classification, Deep Learning (DL) based approach is proposed in this research for segmentation and classification. The MESSIDOR dataset is utilized in this research for classification of Diabetic retinopathy. The min-max normalization, CLAHE and Resize is used in this experiment for data pre-processing and fed into Convolutional Neural Network (CNN) classification. The segmentation is extracted using UNet for the semantic segmentation of red lesions, semantic segmentation is achieved due to the relation of the encode and decode side feature maps. Next, Classification using CNN, they are two classifiers, normal and Mild/ moderation to classifying the object. The obtained result show that the proposed DL using CNN model achieved a better accuracy of 99.84% on MESSIDOR dataset, the existing method such as Capsule method and Moth Search Optimization model (MSO) compared to implemented model are best accuracy.

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Mobile Based Application For Tea Leaf Disease detection

Mobile Based Application For Tea Leaf Disease Detection

引用

technology, Engineering, Management for Societal impact using Marketing, Entrepreneurship and Talent (TEMSMET), IEEE international conference on

作者： J P Manasa Nethravathi Department of ECE VVIET Mysuru India

Tea plant leaf diseases are one of the grand challenges that face in agriculture sector worldwide. In India approximately 20% to 30% of the leaves are lost due to various diseases each year. Tea leaf disease identification is challenging for limited-resources farmers if performed through optical observation of plant leaves symptoms. Therefore, it is important to catch the spread of the disease can spread of the disease in its early stage before they reach epidemic proportion otherwise the disease can spread quickly throughout the entire plantation resulting in huge losses for the farmers. To aid the farmers in the crucial task of identifying tea leaf diseases in its early stage. it is practical to have an intelligent system for detection,identification and classification system in place as a preventive measure. This proposed model is a ML powered mobile based system to automate the plant leaf disease diagnosis process. The developed system uses CNN for feature reduction and classifying five diseases categories. The user interface is developed as a mobile application, allowing farmers to capture a photo of the infected Tea plant leaves. Captured image is then uploaded into the Database and then into the algorithm by specifying image *** then displays the disease category and symptoms of disease along with the confidence *** solutionwill be user friendly and will perform in an effective and efficient manner. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants.

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Smart IoT based Approach for Accident detection and Prevention

Smart IoT based Approach for Accident Detection and Preventi...

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international conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： Pavan Chandra Vishal Chaganti Koppuravuri Sai Sukesh Surekha P Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

With an exponential rise in population and growth in technology, most people now have access to the luxury of mobility. Road Traffic is increasing day by day and so are accidents. People neglect to take proper precautions and don’t follow rules. Also, most of the time these accidents do not get notified to the concerned authority during the golden hour when the victims’ lives can be saved. This leads to a rise in the road accidents causing more number of deaths. The main aim of this research study is to build a system to reduce the number of road accidents and deaths or injuries. The proposed IoT device will take care of factors like dizziness, and emotions of the driver, reduction in the speed of the car by automatic application of the brakes, and ensuring notifications are sent to nearby hospitals in a much faster manner even if accidents occur.

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Comparative Analysis of CNN and SVM Classifiers for Covid-Affected Lung Segmentation: A Novel Approach for High Accuracy detection

Comparative Analysis of CNN and SVM Classifiers for Covid-Af...

引用

Advancement in Electronics & Communication Engineering (AECE), international conference on

作者： Manpreet Singh Centre for Interdisciplinary Research for Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Aim: The goal of this current research work is to identify the Covid affected lung portion with the help of CNN (Novel Convolutional Neural Networks) and compare the accuracy with SVM (Support Vector Machine). Materials and Methods: Two classifiers, namely CNN and SVM classifiers used to invent the accuracy value of Covid Spread in the lung portion with 10 sample sizes each to estimate this research study. Sample size value was computed using G power information with pretest power value at 0.8. Initially, models were trained using NN (Neural Network) approaches where the Covid Spread in lung portion model implements the system efficiently. Results: The accuracy rate of CNN is significantly increased by 95% than the SVM with 93% and there is a statistical significance value that is examined as 0.03 (p>0.05). Conclusion: CNN model has more accuracy rate than the SVM classifier. From the simulation, the CNN is significantly better than the SVM classifier.

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Real-Time Defect detection Monitoring system For Extrusion-based 3D Food Printing Using Internet of Things

Real-Time Defect Detection Monitoring System For Extrusion-b...

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international conference on Smart Cities, Automation & Intelligent Computing systems (ICON-SONICS)

作者： Cholid Mawardi Agus Buono Karlisa Priandana Herianto Department of Computer Science Institut Pertanian Bogor Bogor Indonesia Department of Mechanical and Industrial Engineering Universitas Gadjah Mada Yogyakarta Indonesia

Quality control remains a significant challenge in 3D food printing, in which defects during the printing process will lead to resource and time wastage. This work proposes a novel approach integrating cameras, image processing, and supervised machine learning to autonomously assess the quality of 3D food printed items. At crucial stages of the printing process, images of partially manufactured parts are captured and analyzed based on its geometry. Utilizing smartphones and the Internet of Things for efficient monitoring, the designed system aims to minimize material wastage, a prevalent concern in the creative food industry. The monitoring tool employs a 3D printer embedded with Raspberry Pi and a primary camera, with image processing carried out by the Obico plugin on Raspberry Pi to issue commands for real-time performance monitoring. The implementation of this technology not only addresses early defect detection but also enhancing food design and minimizing environmental impact, presenting promising prospects for the future of the food industry.

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Evaluation of Machine Learning Algorithms for Text Classification Tasks

Evaluation of Machine Learning Algorithms for Text Classific...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Aravindan MK X. Mercilin Raajini Yashwant S. Ingle Pradeep Kumar Jangid Ashu Katyal Preeti Naval Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities.

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Conspiracy in the Stealing of Electricity detection Through the IOT

Conspiracy in the Stealing of Electricity Detection Through ...

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Innovative Practices in technology and Management (ICIPTM), international conference on

作者： C. Mohanraj N. Pushpalatha Muthumanidevi B Amirtha Varsheni S R. Sivakumar Vandana Sharma Ahmed Alkhayyat Department of Electrical and Electronics Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Dept of Mechanical Engineering Sri Krishna College of Technology Coimbatore Tamilnadu India Amity Institute of Information Technology Amity University UP India College of Technical Engineering The Islamic University Najaf Iraq

One of the primary difficulties the distribution network needs to solve is power loss. The “power mismatch” between generation and metered consumption may be employed to describe it. This power larceny has a major influence on distribution utilities. The goal of our proposed system is to design a system to monitor the electricity used by each load and to identify and eradicate electricity theft from current lines and meters. The proposed method also addresses the issue of overloading. It also entails reporting to the Electricity Board representatives the theft that was committed via IoT. The ATmega328 microcontroller in this instance is utilised to detect energy larceny and transmit the data to the IoT module, which then transmits the data to the EB. The practical application of this proposed method results in significant electricity savings, which boosts the economy of the nation.

关键词： Meters Fault diagnosis Microcontrollers Smart cities Power cables Pressing Distribution networks

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