检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

Smart Water Taps Using IoT Sensors and Deep Reinforcement Le...

引用

International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mageshkumar Naarayanasamy Varadarajan Viyyapu Lokeshwari Vinya R. Jenin Prabhu G. Swathi N. Mohankumar S. Velmurugan Lead Software Engineer Capital One Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Assistant Professor Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakundhala Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Professor Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Professor Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.

关键词： Water heating Water conservation User interfaces Deep reinforcement learning Real-time systems Sensor systems Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Cloud-Driven Wearables using Naive Bayes Algorithm and Advanced Sensors in Smart Fabrics

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

引用

Innovation in Technology (INOCON), IEEE International Conference for

作者： M.D. Anto Praveena Nukala Sujata Gupta V. Bini Marin J Gnana Jeslin R. Meenakshi M. Muthulekshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Assistant Professor Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Assistant Professor Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Professor Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Adjunct Professor Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

IoT Enabled Wearable Technology Jacket for Tracking Patient ...

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Utilizing CNN-Based architecture for Automated Differentiation between Gram-Positive and Gram-Negative Bacteria

引用

Procedia computer Science 2025年 258卷 3900-3909页

作者： Debashree Borthakur Smriti Priya Medhi Ranbir Chowdhury Subrata Sinha Bipin Kumar Sharma Jayanta Aich Research Scholar Dept of Microbiology Tripura University Tripura India Assistant Professor (Senior) Dept of Bio Sciences Assam Don Bosco University Assam India Assistant Professor (Senior) Dept of Computer Science and Engineering Assam Don Bosco University Assam India Undergraduate student Dept of Computer Science and Engineering Assam Don Bosco University Assam India Associate Professor Dept of Computational Sciences Brainware University Kolkata India Centre for Multidisciplinary Research and Innovations Brainware University Barasat Kolkata Assistant Professor (Stage III) Dept of Microbiology Tripura University Tripura India Associate Professor Department of Computational Sciences Brainware University Kolkata India

Objectives: This study explores the integration of advanced machine learning methods, specifically convolutional neural networks (CNN), with bacterial image classification to enhance the reliability and efficiency of bacterial classification systems. We present a deep learning approach for the automatic classification of Gram-stained bacterial images into Gram-positive and Gram-negative categories. Methods: Gram staining is a widely used microbiological technique that distinguishes bacteria based on cell wall composition, classifying them as either Gram-positive or Gram-negative. We propose a CNN-based architecture designed to automate this classification process. The model is trained on a large, annotated dataset of Gram-stained bacterial images, leveraging the power of deep learning to achieve state-of-the-art performance in terms of both accuracy and processing speed. Results: Our results show that deep learning can significantly improve the accuracy and efficiency of Gram-stained bacterial image classification, paving the way for automated microbiological analysis in clinical and research settings. Our proposed architecture has classified the gram-positive and gram-negative images of bacterial cells, achieving an accuracy of 95.74% and precision of 96.97%. Novelty: Historically, Gram-stained bacterial image classification has been performed manually by microbiologists. This research pioneers the use of deep learning techniques for the automated classification of these images, achieving substantial improvements in both accuracy and speed. This innovation paves the way for the integration of automated image analysis into microbiological diagnostics and research.

关键词： Gram staining Bacterial Image Classification Deep Learning Convolutional neural network (CNN) Microbiology Gram-positive Gram-negative Automated analysis

来源：评论

学校读者我要写书评

暂无评论

Innovations in Teaching Vibrations and Control - Reinforcing Learning by Combining Portable Lab Equipment and Virtual Labs

引用

IFAC-PapersOnLine 2024年第28期58卷 66-71页

作者： Britt Walker Fahim Dorsey Vanessa Young Connor Talley Coskun Tekes Tris Utschig Ayse Tekes Undergraduate students (Walker Dorsey and Young) and Ph.D. student (Talley) Department of Mechanical Engineering Kennesaw State University Marietta GA USA Assistant Professor of Electrical and Computer Engineering (C. Tekes) Professor of Mechanical Engineering (Utschig) Kennesaw State University Marietta GA USA Associate Professor of Mechanical Engineering Kennesaw State University Marietta GA USA

Most educational laboratory equipment utilized in vibrations and control labs is heavy and bulky, limiting its use within lab spaces. Further, mechanical engineering students typically gain hands-on experience only in labs that are offered separately from fundamental courses, unlike other engineering programs. Thus, the extent of student learning from labs, given their limited time, remains a concern considering students’ learning opportunities. This study presents the design and development of two portable and low-cost lab equipment devices: a compliant mechanism with unbalanced rotating masses and a DC motor with beam control under disturbances. While the former is utilized to demonstrate fundamentals of vibrations, derivation of the mathematical model, simulation, and comparison of theory and experimental data, the latter can be used for demonstration of disturbance control. Virtual labs are developed for both mechanisms in MATLAB Simscape.

关键词： Lab equipment design virtual lab simulations fundamentals of vibrations

来源：评论

学校读者我要写书评

暂无评论

Developing smart devices with automated Machine learning Approach: A review

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

引用

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

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prognosticating Liver Debility Using Classification Approaches of Machine Learning

Prognosticating Liver Debility Using Classification Approach...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Relevance of ISO/SAE 21434 in Vehicular Architecture Development

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：