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共 12616 条记录，以下是71-80 订阅

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An Analysis of Water Quality by Using Supervised Machine Learning Approaches 8

An Analysis of Water Quality by Using Supervised Machine Lea...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Gupta, Aditya Sharma, Ashish Agrawal, Ankush Department of Computer Engineering Applications GLA University India

ISBN: (纸本)9781839539237

Water is very important for everyone. Water is very essential for all the activities. It is a natural resource. This work is a study about water quality analysis using machine learning. The present study deals with the water potability dataset which is an open dataset taken from Kaggle data source. The potability of water means the suitability of water for human consumption. The word "potability"refers to the purity of a body of water. The overall potability of the water supply was assessed in this study using the water quality parameters listed below. pH, solids, hardness, chloramines, conductivity, sulphate, organic carbon, trihalomethanes and turbidity were all measured. In this study Decision Tree, Random Forest and XGBoost is used. Random Forest shows the best results with accuracy 82.46% and precision 80.97%. © The Institution of engineering & technology 2023.

关键词： Organic carbon

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An Enhanced Localization using RSSI with Improved Student Psychologybased Optimization (ISPO) Algorithm in Underwater Acoustic Sensor Network 8

An Enhanced Localization using RSSI with Improved Student Ps...

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Saha, Souvik Khan, Angshuman Arya, Rajeev Department of Electronics and Communication Engineering University of Engineering and Management Rajasthan Jaipur303807 India Department of Electronics and Communication Engineering National Institute of Technology Patna Bihar Patna800005 India

ISBN: (纸本)9781839539237

One of the most important goals of underwater acoustic sensor networks (UASNs) is to identify the precise position of underwater unknown sensor nodes (SNs). An observation station receives the data discovered by the underwater SNs. However, these data are meaningless if they do not contain location information. Therefore, localization is the foremost significant concern that must be fixed in UASN. This work investigates this issue, and an enhanced localization technique using RSSI with an improved student psychology-based optimization (ISPBO) approach is presented as a possible solution. In order to better assess the position of SNs, the localization error (LE) must be minimized. So, using the ISPBO in this study reduces the LE and improves localization coverage as well as energy consumption. In addition, compared to the existing algorithms, the proposed technique has improved localization coverage, localization error, and energy usage. © The Institution of engineering & technology 2023.

关键词： Sensor nodes

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Parasitisation Prediction in Malarial Cell Images using Transfer Learning- driven Vision Transformers 8

Parasitisation Prediction in Malarial Cell Images using Tran...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Sinha, Sanya Sinha, Sanjeev Department of Electronics and Communications Engineering Birla Institute of Technology Patna India Department of Civil Engineering National Institute of Technology Patna Patna India

ISBN: (纸本)9781839539237

The World Malaria Report 2022 stated that nearly 247 million cases of malaria and 619,000 deaths were reported annually as of 2021. This emphasizes on the tremendous impact the disease has, especially in third-world countries. Through efficient and timely diagnosis, the disease could be accurately identified and suitable lines-of-treatment could be initiated. The requirement for systematic diagnosis benchmarked the arrival of the computer-aided diagnosis landscape through imaging and Machine Learning (ML) techniques. Deep learning-based backbones are particularly efficient for image classification and disease detection. Through this paper, we propose a Deep Neural Network (DNN) backbone VGG16 for extracting the local and global features of blood smear cell images marked with the Giemsa stain, followed by a Vision Transformer (ViT) neural network architecture for classifying the coloured malarial cell images into the parasitized and uninfected classes of images based on the extracted features. This novel approach returned 97.72% classification accuracy with leading sensitivity, specificity, precision, recall, and area under curve metrics. © The Institution of engineering & technology 2023.

关键词： Diseases

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A Rainfall Prediction System using Machine Learning (ML) and Internet of Things (IoT) 8

A Rainfall Prediction System using Machine Learning (ML) and...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Paul, Parthib Basak, Santanu Khan, Angshuman Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur303807 India Department of Electronics and Communication Engineering University of Engineering and Management Rajasthan Jaipur303807 India

ISBN: (纸本)9781839539237

One of the most pressing scientific and technical issues on a global scale is the difficulty of accurately predicting rainfall. Serious effects can result from significant changes in rainfall patterns, such as crop destruction from erratic heavy rain and danger to human life from catastrophic floods. It needs more accurate and dependable rainfall forecast techniques to get over these obstacles. This work presents an Internet of Things (IoT) and machine learning (ML)-based rainfall forecast system. As a note, this model used a modest amount of computer resources to predict rain, which is one of its benefits. In order to provide a more thorough evaluation, the performance of the proposed strategy is compared with that of many well-used techniques. The comparison shows that the random forest model outperforms the other ML models, which classify rainfall events with a higher accuracy of 94.68% and a low MSE. © The Institution of engineering & technology 2023.

关键词： Internet of things

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DEEP LEARNING FOR MACRO-NUTRIENT DEFICIENCY IDENTIFICATION IN THE GROUNDNUT PLANTS 8

DEEP LEARNING FOR MACRO-NUTRIENT DEFICIENCY IDENTIFICATION I...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Venkatesh, Kummari Naik, K. Jairam Department of Computer Science and Engineering National Institute of Technology Raipur Raipur492010 India

ISBN: (纸本)9781839539237

Nutrient deficiency is one of the leading causes of the decline in groundnut crop yield. Also, it's very difficult to the farmers to identify the deficient of specific nutrient in the plant just be seeing the symptoms of leaf. To diagnose nutrient deficiencies, the collected leaf samples are often tested manually using laboratory equipment. But it is a time taking process that requires considerable effort and money. In this study, the deficiencies of nutrients like nitrogen, phosphorus, and potassium in the groundnut plant were identified using plant leaf images dataset. Convolution neural networks (CNN) are utilized to build deep learning models that could detect nutrient deficits in groundnut plant. A light weight CNN was proposed to identify a nutrient deficiency in groundnut plants. The proposed methodology was found to be more effective. The model's accuracy is 94.64%. The Matthew Correlation Coefficient (MCC), f1-score, recall, and precision values are 92.11%, 92.73%, 95.23%, and 94.58%, respectively. © The Institution of engineering & technology 2023.

关键词： Nutrients

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IDENTIFICATION OF FAKE CURRENCY 8

IDENTIFICATION OF FAKE CURRENCY

引用

8th international conference on computing in engineering and technology, iccet 2023

作者： Reddy, K. Sivanagi Jyothika, B. Shravya, R. AathmajaSai, G. Department Electronics and Communication Engineering India Sridevi Women's Engineering College Telangana India

ISBN: (纸本)9781839539237

Currency fraud now possess a serious risk to both people's livelihoods and the nation's economy. Due to improvements, in printing and scanning technology, currency duplication is still a growing worry for governments. Although banks have many note-detecting systems;but are expensive and frequently wrong. This problem has the potential to be considerably solved by the disciplines of machine vision, neural networks, and image processing. Although there are fake cash detectors, they are only used in banks and corporate offices, leaving the general public and small companies at risk. In order To detect and invalidate fraudulent Indian currency, we will develop a software-based system in this project using cutting-edge image processing and computer vision techniques. First, we will analyze the many security characteristics of Indian currency. This currency authentication system was created entirely in the Jupyter Notebook environment using the Python programming language. © The Institution of engineering & technology 2023.

关键词： Computer vision

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AGE AND GENDER PREDICTION USING MACHINE LEARNING 8

AGE AND GENDER PREDICTION USING MACHINE LEARNING

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8th international conference on computing in engineering and technology, iccet 2023

作者： Reddy, K.Sivanagi Kumari, Aruna D. Nikhitha, K. Nousheen Department Electronics and Communication Engineering India Sridevi Women's Engineering College Telangana India

ISBN: (纸本)9781839539237

Because they are essential to the creation of intelligent systems that can quickly learn and categorise objects, pattern recognition & automated categorization constitute two of the computer science topics that are most actively researched. A crucial role is played by biometrics, that are often employed in systems like this for security reasons. Facial recognition is now being prioritised as the main biometric method. This research aims to develop a system that employs neural networks based on convolution to identify an individual's gender and age from merely a picture or video feed of a person's face. The IMDB, WIKI, and ADIENCE datasets were used to build and evaluate several CNN network models with different topologies. The findings demonstrated that CNN had achieved substantial advancements. The phrases "facial recognition software,""biometrics,""convolution neural networks,""age estimate,""gender predictions,"and "Internet Movie Database"are all pertinent. © The Institution of engineering & technology 2023.

关键词： Biometrics

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Modelling and Analysis of Relay Attack Devices for Passive-Entry-Passive-Start Wireless Systems 8

Modelling and Analysis of Relay Attack Devices for Passive-E...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Rath, Sowjanya Badar, Altaf Q. H. Bharadwaj, V. Kiran Department of Electrical Engineering National Institute of Technology Telangana Warangal India Department of Electronics & Communication Engineering National Institute of Technology West Bengal Durgapur India

ISBN: (纸本)9781839539237

Passive-entry-passive-start (PEPS) is a wireless communication system that enables a driver to enter and start their vehicle without the need of a physical key. In PEPS system, the vehicle sends low-power LF signal to communicate with the key-fob. If the key is in the vicinity of the automobile, it sends a RF signal which the vehicle senses and unlocks the door. In this work, we show how vulnerable these systems can be to external attacks using relay devices. In a relay attack, the objective is to amplify the vehicle signal to transmit to longer distances such that it can communicate with the key-fob and unlock the door, without the knowledge of the owner. This work includes a model of the relay devices developed using MATLAB, as well as a detailed explanation of an attack scenario. A distance-based test case has also been included to conclude the attack. In the introduction section, a few popular techniques which can be used to mitigate relay attacks have been reviewed. © The Institution of engineering & technology 2023.

关键词： Vehicles

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Fuzzy Logic Based Proportional Control of Temperature of Solar Furnace 8

Fuzzy Logic Based Proportional Control of Temperature of Sol...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Kumar, Sanjeev Choudhary, Jayanti School of Engineering & IT Arka Jain University Jharkhand832108 India Department of Electrical Engineering National Institute of Technology Bihar Patna800005 India

ISBN: (纸本)9781839539237

This paper reports a strategy for the control of steady-state temperature in a solar furnace with a fixed set point. The solar furnace uses concentrated solar heat energy to raise the temperature for material testing and treatment purpose. The material under test is allowed to attain a temperature equal to the set point. The fluctuation in solar intensity throughout the day has a substantial impact on system dynamics at all times which makes the temperature control a difficult task. As of now, the solar furnace system has been basically modelled as a first order transfer function. The real-time variation in solar intensity keeps changing the gain of the transfer function which makes the temperature control a necessary action. In this work, the simulation for the solar furnace system has been done by considering the variation in solar intensity. The consequent variation in temperature has been controlled by Fuzzy Logic that produces a proportional control of the system to attain the setpoint. The solar furnace configuration has been referred to The Plataforma Solar de Almería (PSA), which is a facility for solar energy investigation based in Almería, Spain. © The Institution of engineering & technology 2023.

关键词： Fuzzy logic

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A Comprehensive Study on Machine Learning Methods for Predicting Heart Disease: A Comparative Analysis 8

A Comprehensive Study on Machine Learning Methods for Predic...

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8th international conference on computing in engineering and technology, iccet 2023

作者： Das, Trinanjan Sinha, Bam Bahadur Department of Data Science and Artificial Intelligence Indian Institute of Information Technology Jharkhand Ranchi834010 India Department of Computer Science and Engineering Indian Institute of Information Technology Jharkhand Ranchi834010 India

ISBN: (纸本)9781839539237

The objective of the research is to create a robust and precise machine learning model capable of accurately predicting the presence of heart disease in patients based on various clinical attributes. The study employed supervised machine learning techniques, including Support Vector Machine (SVM), Naive Bayes, Decision Tree, K-Nearest Neighbour, Logistic Regression, and Artificial Neural Network, as well as ensemble techniques to enhance the performance of the model. The Statlog Heart Disease dataset, consisting of 14 attributes and 270 instances, was utilized for model development and testing. The results demonstrated that the Support Vector Machine (SVM) and Voting Classifier, among the ensemble techniques, exhibited the highest classification accuracy. The developed model has the potential to assist healthcare professionals in identifying the risk of heart disease in patients, facilitating early diagnosis and treatment, and potentially reducing mortality rates. In conclusion, this paper emphasizes the significant role of machine learning techniques in the early detection and diagnosis of heart disease, offering an accurate and efficient method to identify individuals at risk. © The Institution of engineering & technology 2023.

关键词： Decision trees

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