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Smart Garbage Segregation & Management System Using Internet of Things(IoT) & machine learning(ML) 1

Smart Garbage Segregation & Management System Using Internet...

引用

international conference on Innovation in Information Communication and Technology (ICIICT)

作者： Shamin, N. Fathimal, P. Mohamed Raghavendran, R. Prakash, Kamalesh SRM Inst Sci & Technol Dept Comp Sci & Engn Vadapalani Chennai Tamil Nadu India

ISBN: (纸本)9781728116044

The expansion in populace has prompted gigantic increment in the contamination also. It may peeve numerous relentless diseases for the people. For eliminating or alleviating the garbages and to keep up the cleanness, it requires a smart garbage managing architecture. But there is another severe problem, that to segregate the wastes that has been collected This paper proposes IoT stationed smart waste segregation and management device which detects the wastes in the dustbins with the aid of using Sensor devices and as soon as it is detected the waste substances in it will be segregated with the help of sensors and right away information is transferred to cloud database via IoT. Microcontroller is utilized as an association between the sensors and IoT module. Ultra-sonic sensor is utilized to distinguish the nearness of the waste material The moisture sensor is used to analyze and report the moisture content in the waste, and if there is moisture content available then the waste cannot be put in the dustbin. Metal sensor is used to separate the metal items and is separated to a section. image processing algorithm is used to identify the plastics and degradable items and is separated to another separate sections. The dustbin data are uploaded to the cloud using IoT in real time This helps in clearing the wastage from dustbin in an efficient and smartest way.

关键词： IoT device Sensor technology management system waste management plastic segregation waste management waste segregation system waste management system metal waste segregation IoT based system image processing machine learning

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Abnormal recognition Technology of 3D Virtual Scene image Based on Wireless Network Sensor 1

Abnormal Recognition Technology of 3D Virtual Scene Image Ba...

引用

1st IEEE international conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Li, Guangwei Hexi University Gansu Zhangye China

ISBN: (纸本)9798350316469

The performance and reliability requirements of wireless network sensors are getting higher and higher. In the complex 3D virtualization scene, abnormal recognition of sensor data becomes a challenging task. Therefore, this study aims to explore an effective technique for identifying anomalies in wireless network sensor images in 3D virtualized scenarios. In this study, deep learning techniques were used, combined with image processing and pattern recognition methods, to achieve accurate identification of anomalies in wireless network sensor images. Through training and testing on a large number of 3D virtualized scene images, the experimental results show that the proposed method has high accuracy in identifying anomalies in wireless network sensor images. The recall rate of the algorithm in this paper is 77%-85%, compared with the traditional method, the method in this paper can more accurately detect and classify various types of abnormal conditions, including signal interference, equipment failure and abnormal data. In addition, the method studied in this paper also shows good real-time performance, which can detect and identify anomalies in real time when processing large-scale sensor data. © 2023 IEEE.

关键词： 3D Virtual Scene image recognition Network Sensor Wireless Network

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Automatic Tomato Leaf Diseases Classification and recognition using Transfer learning Model with image processing Techniques 1

Automatic Tomato Leaf Diseases Classification and Recognitio...

引用

1st international conference on Smart Technologies Communication and Robotics, stCR 2021

作者： Hemalatha, A. Vijayakumar, J. Bharathiar University Department of Electronics and Instrumentation Tamilnadu Coimbatore India

ISBN: (纸本)9781665418065

In agriculture, manual approaches to recognize tomato plant diseases are often time-consuming. The diseases spread throughout the plant, affecting the quality and yield. In this research work, a transfer learning model (DenseNet201) was designed to conduct the symptom-wise recognition of three tomato diseases such as leaf mold, early blight, and late blight are compared with healthy leaves. The transfer learning model has been trained, tested, and validated. Our trained model can recognize the category of disease. Once the images are classified, the regions are segmented, and features are extracted using k-means clustering and ORB keypoints. The transfer learning model achieved an excellent recognition result with an average accuracy of 99.688%. © 2021 IEEE.

关键词： Fruits

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A Comparative Analysis of machine learning Algorithms for Credit Card Fraud Detection 1

A Comparative Analysis of Machine Learning Algorithms for Cr...

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1st IEEE international conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Raturi, Anshul Pal, Maahi Narang, Harshit Vats, Satvik Sharma, Vikrant Yadav, Satya Prakash Graphic Era Hill University Computer Science and Engineering Dehradun India Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350371802

The introduction and availability of credit cards in the modern world have transformed the transaction or payment process from the typical cash payment to modernized cashless payment. Due to the rapid growth and development of e-commerce platforms and improved accessibility to the Internet, a notable increase in the use of credit cards for purchases has occurred. Regrettably, this surge has also been accompanied by an upswing in fraudulent activities, demanding heightened vigilance. The phrase 'credit card fraud' describes a wide rangeof illicit actions in which individuals utilize credit card details to get money through deception. Effectively combating this multifaceted threat necessitates a deep understanding of the mechanisms involved in identifying and preventing credit card fraud. Since sophisticated techniques for preventing and detecting fraud have been developed over time, the effects of credit card fraud have decreased, and the sector has become more stable. While previous research in this field has largely depended on machine learning algorithms such as random forest and logistic regression for credit card fraud detection, this study introduces a groundbreaking approach. We present the use of convolutional Neural network (CNN) models, a method commonly used in image processing and image recognition, to evaluate heatmaps generated from credit card transaction data. © 2024 IEEE.

关键词： image recognition

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1st international conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

1st International Conference on Computational Intelligence f...

引用

1st international conference on Computational Intelligence for Engineering and Management Applications, CIEMA 2022

ISBN: (纸本)9789811984921

The proceedings contain 68 papers. The special focus in this conference is on Computational Intelligence for Engineering and Management Applications. The topics include: strokes-Related Disease Prediction Using machine learning Classifiers and Deep Belief Network Model;analysis and Detection of Fraudulence Using machine learning Practices in Healthcare Using Digital Twin;prediction and Analysis of Polycystic Ovary Syndrome Using machine learning;feature Selection for Medical Diagnosis Using machine learning: A Review;convolutional Neural Network Architectures Comparison for X-Ray image Classification for Disease Identification;Secure Shift-Invariant ED Mask-Based Encrypted Medical image Watermarking;fusion-Based Feature Extraction Technique Using Representation learning for Content-Based image Classification;a Comparative study on Challenges and Solutions on Hand Gesture recognition;design a Computer-Aided Diagnosis System to Find Out Tumor Portion in Mammogram image with Classification Technique;a Novel Soft-Computing Technique in Hydroxyapatite Coating Selection for Orthopedic Prosthesis;performance Analysis of Panoramic Dental X-Ray images Using Discrete Wavelet Transform and Unbiased Risk Estimation;efficient image Retrieval Technique with Local Edge Binary pattern Using Combined Color and Texture Features;texture and Deep Feature Extraction in Brain Tumor Segmentation Using Hybrid Ensemble Classifier;A Systematic Review on Sentiment Analysis for the Depression Detection During COVID-19 Pandemic;vehicular Adhoc Networks: A Review;the Data Vortex Switch Architectures—A Review;survey on Genomic Prediction in Biomedical Using Artificial Intelligence;a Brief Review on Right to Recall Voting System Based on Performance Using machine learning and Blockchain Technology;sentiment Analysis Techniques: A Review;network Traffic Classification Techniques: A Review;remote Production Monitoring System.

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The design of fruit automated sorting system

引用

1st international conference on Computer and Computing Technologies in Agriculture (CCTA 2007)

作者： Ge, Pingju Wu, Qiulan Sun, Yongxiang Shandong Agr Univ Coll Informat Sci & Engn Tai An 271018 Shandong Peoples R China

ISBN: (纸本)9780387772509

The article introduced the flow and design scheme of fruit automated sorting system, the computer technology and automatic technology is used in agriculture production and management. In this system, the application of some technology, such as the detection and control technology, the digital image processing technology, the computer peripheral device and interface technology, pattern recognition technology etc, is used in the fruit automated sorting system.

关键词： the fruit automated sorting system automation the digital image processing pattern recognition

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Facial Attractiveness Classification using Deep learning 1

Facial Attractiveness Classification using Deep Learning

引用

1st international conference of the Indonesian-Association-for-pattern-recognition (INAPR)

作者： Anderson, Ricky Gema, Aryo Pradipta Suharjito Isa, Sani M. Bina Nusantara Univ Sch Comp Sci Dept Comp Sci Jakarta 11480 Indonesia Bina Nusantara Univ BINUS Grad Program Dept Comp Sci Comp Sci Jakarta 11480 Indonesia

ISBN: (纸本)9781538694220

Facial attractiveness classification application has many various usabilities, including photo editing, photo beautification, photo grading and dataset labeling. While face attractiveness classification seems to be related to personal preference, building a robust attractiveness classifier is not impossible. There are several studies that have developed a classification system of facial attractiveness using a convolutional neural network and provide satisfactory results. The use of image-net pre-trained convolutional neural network has been largely used by face-related research, yet none of them are related to facial attractiveness. This study aims to compare the famous deep learning architecture, such as VGG, Inception, and ResNet models. This research also aims to analyze the effect of using the Viola-Jones algorithm, as a preprocessing method, to the classification result of the built model. Viola-Jones algorithm will detect faces in image data, and 2 types of cropping will be done to extract the face region from the image, namely loose-crop and tight-crop. This research produces the highest accuracy value of 82.52% by using ResNet50 model and loose-crop preprocessing method.

关键词： Deep learning Facial Attractiveness Face recognition

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1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

1st International Conference on Emerging Technology in Model...

引用

1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

ISBN: (纸本)9789811374029

The proceedings contain 70 papers. The special focus in this conference is on Emerging Technology in Modelling and Graphics. The topics include: An Efficient Descriptor for Gait recognition Using Spatio-Temporal Cues;supervised Classification Algorithms in machine learning: A Survey and Review;breast Cancer Diagnosis Using image processing and machine learning;Using Convolutions and image processing Techniques to Segment LungsÂ from CT Data;analyzing Code-Switching Rules for Englishâ€"Hindi Code-Mixed Text;detection of Pulsars Using an Artificial Neural Network;modification of Existing Face images Based on Textual Description Through Local Geometrical Transformation;a Neural Network Framework to Generate Caption from images;remote Sensing and Advanced Encryption standard Using 256-Bit Key;grasp-Pose Prediction for Hand-Held Objects;an Automated Segmentation Approach from Colored Retinal images for Feature Extraction;a Multi-level Polygonal Approximation-Based Shape Encoding Framework for Automated Shape Retrieval;a Hand Gesture recognition Model Using Fuzzy Directional Encoding ofÂ Polygonal Approximation;signature-Based Data Reporting in Wireless Sensor Networks;wine Quality Analysis Using machine learning;generalized Smart Traffic Regulation Framework with Dynamic Adaptation and Prediction Logic Using Computer Vision;a Short Review on Applications of Big Data Analytics;a Survey of Music Recommendation Systems with a Proposed Music Recommendation System;interactive Systems for Fashion Clothing Recommendation;embedded Implementation of Early started Hybrid Denoising Technique for Medical images with Optimized Loop;stress Profile Analysis in n-FinFET Devices;epileptic Seizure recognition Using Deep Neural Network;an Annotation System to Annotate Healthcare Information from Tweets;broad Neural Network for Change Detection in Aerial images;topic Modeling for Text Classification.

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A Hybrid Approach for Hyper Spectral image Segmentation Using SMLR and PSO Optimization 1st

A Hybrid Approach for Hyper Spectral Image Segmentation Usin...

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1st international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Karchi, Rashmi P. Nagesh, B. K. Bharathiar Univ Deaprtment Comp Sci Coimbatore 641046 Tamil Nadu India SIT Dept Comp Sci & Engn Mangalore 574143 Karnataka India

ISBN: (纸本)9789811048593;9789811048586

A hybrid approach for hyperspectral image segmentation is presented in this paper. The contribution of the proposed work is in two folds. First, learning of the class posterior probability distributions with Quadratic Programming or joint probability distribution by employing sparse multinomial logistic regression (SMLR) model. Secondly, estimation of the dependencies using spatial information and edge information by minimum spanning forest rooted on markers by acquiring the information from the first step to segment the hyper spectral image using a Markov Random field segments. The particle swarm optimization (PSO) is performed based on the SMLR posterior probabilities to reduce the large number of training data set. The performance of the proposed approach is illustrated in a number of experimental comparisons with recently introduced hyperspectral image analysis methods using both simulated and real hyper spectral data sets of Mars.

关键词： Feature selection Hyperspectral image classification Particle swarm optimization Spatio-spectral analysis

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Data Characterization for Reliable AI in Medicine 5th

Data Characterization for Reliable AI in Medicine

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5th international conference on Recent Trends in image processing and pattern recognition (RTIP2R)

作者： Rajaraman, Sivaramakrishnan Zamzmi, Ghada Yang, Feng Xue, Zhiyun Antani, Sameer K. NIH Bethesda MD 20894 USA

ISBN: (纸本)9783031235986;9783031235993

Research in Artificial Intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.

关键词： Data characteristics Artificial intelligence machine learning Deep learning Medical imaging Data-driven design Reliability Generalizability Robustness

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