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Developing smart devices with automated Machine learning Approach: A review

Materials Today: Proceedings 2022年 51卷 826-831页

作者： Sunil A. Patel Sanjay P. Patel Yagna Bhupendra Kumar Adhyaru Santosh Maheshwari Pankaj Kumar Mukesh Soni Assistant Professor Computer Science and Engineering Department Government Engineering College Patan Gujarat India Assistant Professor Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Head Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Department of Computer Science & Engineering Noida Institute of Engineering & Technology Greater Noida India Research Scholar Computer Science and Engineering Department University Bhopal 462044 India

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

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Optimization of machining parameters in EDM using Taguchi based grey relational analysis

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Materials Today: Proceedings 2022年 82卷 43-46页

作者： Ganapathy S. Palanivendhan M. Susitra K. Balasubramanian P. Department of Mechanical Engineering Sri Sai Ram Engineering College Chennai 600044 India Centre for Automotive Materials Assistant Professor Department of Automobile Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu 603203 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai 600044 India Department of Mechanical Engineering A.V.C College of Engineering Mayiladuthurai 609305 India

The present investigation optimizes process parameters such tool size, peak current, pulse on time, and dielectric pressure in the EDM process. The material removal rate affects the machining operation's quality. The goal of the present work is to maximize material removal rate using Taguchi technique-based optimization of machining parameters. On an EDM, the experimental study for machining operations was carried out. For EN-31, machining operations are carried out using an orthogonal array with L9. The ANOVA technique was used to evaluate the experimental results. Using a technique called as grey relational analysis, the optimum conditions for material removal rate are determined. The optimum approach for a set of linked parameters is evaluated using the grey relation grade. © 2023 Elsevier Ltd. All rights reserved.

关键词： Anova EDM EN-31 GRA MRR

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An Automated Methodology for Detecting Cercospora Infestations in Eggplant using YOLOv10N

An Automated Methodology for Detecting Cercospora Infestatio...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： S. Deepan Ms. M. Revathy Department of Computer Science and Engineering KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India Assistant Professor / CSE KIT - Kalaignarkarunanidhi Institute of technology Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Cercospora leaf spot, a damaging fungal disease in eggplants, threatens agricultural productivity and farmer livelihoods. This study introduces an automated system for early detection of Cercospora infestations and affected percentages using advanced image processing and the YOLOvlON algorithm. A diverse dataset of annotated eggplant leaf images highlights healthy and infected regions. Preprocessing steps, including noise reduction and feature enhancement, ensure accurate detection. Supervised data augmentation enhances the model's adaptability to real-world conditions. The proposed system achieves high precision with competitive metrics, such as mean Average Precision (mAP), precision, and recall. Its lightweight design ensures compatibility with various devices, promising accessible solutions for farmers. Future developments aim to integrate mobile platforms, advancing sustainable and scalable plant disease management in agriculture.

关键词： Productivity Precision agriculture Performance evaluation Plant diseases Sentiment analysis Noise reduction Object detection Data augmentation Real-time systems Pragmatics

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Employing a Deep Neural Network to Identify and Categorize Leaf Diseases in Tomato Plant in Their Early Stages

Employing a Deep Neural Network to Identify and Categorize L...

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International Conference on Advances in Information Technology (ICAIT)

作者： Chandradeep Bhatt Parveen Kumar Saini Vikash Sawan Sanjay Sharma Teekam Singh Taranath N L Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of CSE Chandigarh University Gharuan Mohali Punjab Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Associate Professor School of CSE Presidency University Rajanukunte Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Tomato production makes a good contribution to the world economy as an element of agriculture. Tomato plants are vulnerable to different types of diseases that might create a negative impact on the production of the Tomato yield. Identification of this type of vulnerable disease at its early stage is important. This research work proposed a 9-layer Deep Neural Network to identify the leaf disease in the tomato plant. Deep learning technology of neural network approach was used in this method to classify the different leaf diseases in the tomato plant. The performance accuracy was obtained at 97.32% through the proposed method.

关键词： Accuracy Artificial neural networks Production Transforms Pressing Agriculture Real-time systems Safety Diseases Testing

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A Machine Learning Model for Disease Prediction and Remote Patient Monitoring 22

A Machine Learning Model for Disease Prediction and Remote P...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Kirtida Tejas Naik Bindu Garg Computer engineering PHD Scholar Bharati vidyapeeth deemed to be university college of engineering India Computer Engineering Professor and Head of department computer science Barati vidyapeeth(Deemed to be university college of engineering pune) India

ISBN: (纸本)9781450399937

People's health reports, including diagnostic information and medical prescriptions, are delivered in the form of test-based case notes, making it impossible to determine a person's prior health issues or medications taken until he or she returns to the hospital later on. Storing all of a person's health information in the cloud as a soft copy, on the other hand, alleviates this issue. To accomplish this, each and every hospital, dispensary, and laboratory must have an internet connection for the registration of patient data. A unique Health Id will identify each patient, and all of the patient's data will be stored in the cloud, where the specific patient can only access it. In order to prevent and treat illness, it is critical to perform an accurate and timely analysis on any health-related problem. The ability to diagnose disease by obtaining all information from a linked Health ID combined with Machine Learning techniques will improve the system's ability to detect diseases. We believe that our diagnostic model can operate like a doctor in the earlier diagnosis of this disease, allowing for timely treatment and the preservation of life.

关键词： Disease Prediction Remote Health Monitoring AI Machine Learning

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IoT Enabled Wearable Technology Jacket for Tracking Patient Health and Safety System

IoT Enabled Wearable Technology Jacket for Tracking Patient ...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： M. Santhanalakshmi S. Dhanalakshmi M. Radhika G. Kavitha G. Elavel Visuvanathan C. Srinivasan Assistant Professor Department of Computer Science Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women Chennai Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Muthukumaran Institute of Technology Chennai Tamil Nadu India Assistant Professor Department of Information Technology R.M.D. Engineering College Kavaraipettai Tamil Nadu India Professor Department of Computer Science and Engineering Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Adjunct Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

Over the years, there have been ongoing efforts to use clothing as a vehicle for distributing digital capabilities. Information technology (IT) is being incorporated into garments to enable people to work more quickly. Because of their increasing availability, accessibility, and, most significantly, cost, wireless body area networks (WBANs) will undoubtedly be used in healthcare to enhance medical data utilization. Whether it has to do with post-operative recovery or Monitoring, applications for their function in the medical field are already being looked into. Wearable technology allows users to complete activities more efficiently by enhancing one or more of their senses with artificial information. The system proposes a method that displays critical information for doctors on a particular cloud for data analysis. In this concept, sensors are attached to someone's coat to collect real-time data. This data is analyzed, uploaded to the cloud using IoT Medium, and notified in the case of an alarming condition. The data access to real-time data, a website built into this module, and an alert notification system to warn the hospital and the family if the numbers are abnormal. A live stream is recorded using the camera, while the GPS module tracks the location. The doctor can take the proper step based on the patient's health.

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Prognosticating Liver Debility Using Classification Approaches of Machine Learning

Prognosticating Liver Debility Using Classification Approach...

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International Conference on Recent Trends in Machine Learning, IOT, Smart Cities and Applications, ICMISC 2020

作者： Akshara, Revelly Reddy, Sandhi Kranthi Assistant Professor Department of Computer Science and Engineering Vignan Institute of Technology and Science Yadadri Bhuvanagiri DistrictTelangana India

ISBN: (纸本)9789811572333

In the field of Machine Learning in Healthcare, one of the most compelling factors is the prognosis of illness by examining the characteristics which have the most impact on its recognition. One of the deadliest diseases is a liver disease that accounts for about two million deaths annually worldwide. Prognosticating it in an early stage is helpful to get diagnosed at the right time as this may lead to a complete recovery in some patients. We can effectively prognosticate liver disease using supervised learning techniques specifically classification methods of machine learning. In this paper, different classification methods are applied to the Indian Liver Patient Records Dataset downloaded from *** to prognosticate liver disease. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Relevance of ISO/SAE 21434 in Vehicular Architecture Development

Relevance of ISO/SAE 21434 in Vehicular Architecture Develop...

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IEEE International Conference on Systems, Man and Cybernetics

作者： V Kethareswaran Sanjay Moulik Doctoral Researcher in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India Assistant Professor in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India

Research in terms of security in vehicular architecture has been a focal point of deliberation in the automotive sector. The Automotive industry development has continuously evolved the scenario of adding more integration to networked vehicles or semi-autonomous vehicles. The end product of such manufacturing practices is a vehicle becoming a networked computer on wheels. In spite of providing numerous advantages, these developments in the automotive industry are also bringing more challenges in terms of security. Hence, the industry has been forced to emphasise the principle of Security by Design. Efforts in this direction have resulted in the formulation of the ISO/SAE 21434 standard. In this work, we focus on summarising the ISO/SAE 21434 standard and analysing its relevance for automotive architecture development. Related works in the standard's approach of Security by Design are summarised. Additionally, we propose an application work of the standard in the context of automotive Architecture development as future work.

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Integrated Secured Digitized Home Using IoT-Based Sensors

Integrated Secured Digitized Home Using IoT-Based Sensors

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International Conference on Automation, Signal Processing, Instrumentation and Control, i-CASIC 2020

作者： Jayashree, D. Pandithurai, O. Afra, M. Dharani Sri, R. Dedeepya, C. Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Asso. Prof. Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India UG students Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9789811582202

This paper aims to provide a complete voice-controlled home by automating the operations of the home appliances via the user’s mobile phone. The mobile application in the person’s mobile phone is connected to the Raspberry Pi 3 board which serves as an assemblage between the user and the home appliances. The home appliances that serve as the "proof-of-concept" in this paper include a bulb, fan, and air conditioner. The user logs in into his mobile application, gives in a voice command which is comprehended using natural language processing (NLP) and is directed to the Raspberry Pi 3 board. The mobile application on the phone is the central console that decides which appliance is the one to be operated. The Raspberry Pi 3 board will send an acknowledgment for the successful reception of the command and subsequently perform the command on the corresponding home appliance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Elicitation of Intracerebral Hemorrhage Using Deep Learning

Elicitation of Intracerebral Hemorrhage Using Deep Learning

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International Conference on Signal and Information Processing (IConSIP)

作者： Saraswati Patil Sangita M. Jaybhaye Ashwini Mamulwar Ayush Rambhad Mihir Prajapati Suresh Mante Department of Computer Engineering Assistant professor Vishwakarma Institute of Technology Pune India Department of Computer Engineering Associate Professor Vishwakarma Institute of Technology Pune India Department of Computer Engineering Btech Student Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9781728168869

The paper presents modeling and prediction for Intracerebral hemorrhage. The elicitation of intracerebral bleeding from veins and arteries that damages the internal stability and mental control of an individual could help in life saving situations, thus reducing the risk of intracerebral hemorrhage to a great extent. Vascular bleeding involves a lot of fragilities, and therefore if not treated and diagnosed soon, this can cause life-threatening situations. Deep learning algorithms shows indubitable results for medical image recognition and classification in the healthcare sector. Neuro physicians face difficulties in diagnosing hemorrhage in the beginning stages of intracranial bleeding, thereby adding to possibilities of misdiagnosis. This challenge faced by Neuro physicians worldwide is considered for the research work in the present paper. It provides a solution model for elicitation of ICH within a CT scan with the support of CNN. An AI-based Web platform is developed to classify CT scans and predict ICH type using the model.

关键词： Deep learning Visualization Computed tomography Computational modeling Veins Medical services Predictive models

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