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Synthesis of KNN Algorithm in FPGA Technology 1

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6th international conference on Microelectronics, Electromagnetics, and Telecommunications, ICMEET 2021

作者： Payal, Ravi Singh, Amit Prakash CDAC Noida India GGSIPU New Delhi India

ISBN: (数字)9789811685507

ISBN: (纸本)9789811685491

machine learning is coming everywhere, and currently in every field, it is contributing in terms of different applications. machine learning is a type of artificial intelligence (AI), which enables a program to learn, without being explicitly programmed and without human intervention. It is broadly divided into two types, namely supervised and unsupervised learning. Different types of machine learning algorithms are there but slowly and slowly wrt hardware design also it is coming. KNN algorithm is one of the most important algorithms, and it is typically used for pattern recognition, data mining, image recognition, text categorization, and predictive analysis. Here, we tried to implement KNN algorithm by using HDL. It is divided into two parts. First one is to calculating distance between testing point and training datasheet points, and the second part consists of sorting the distances calculated and defining the respective class/group of the testing point. the code is also mapped to FPGA library for the synthesis process. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Classification of Human Indoor Activities with Resource Constrained Network Architectures on Audio data 5

Classification of Human Indoor Activities with Resource Cons...

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IEEE 5th international conference and Workshop Obuda on Electrical and Power Engineering (IEEE CANDO-EPE)

作者： Anand, Joseph Koch, Marcel Schlenke, Fabian Kohlmorgen, Fabian Wohrle, Hendrik Dortmund Univ Appl Sci & Arts Dept Informat Technol Dortmund Germany

ISBN: (纸本)9798350346190

Many functions and processes in Smart Homes can be improved by incorporating knowledge about the activities of the inhabitants. the inhabitant activities can be recognized by classifying typical sounds related to the activities using audio classification methods. In this paper, we present and approach for the classification of activity-based audio data, taking into account that in many cases the data processing has to be deployed on a resource-constrained device. An indoor dataset is created by recording 11 distinct human actions that are typically perfomed in a living room and kitchen setup. A Convolutional Neural Network (CNN) is used for classification that achieves an accuracy of up to 98%. In addition, network optimization methods like depthwise separable convolutions, pruning, channel scaling and quantization is used to reduce the memory footprint and computational requirements to obtain an optimized network with 93% fewer Million Floating point Operations (MFLOPs) and less than 2% drop in accuracy is achieved for use in resource-constrained devices.

关键词： Audio Classification IoT machine learning Human Activity recognition Edge Computing

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SSDL: Self-Supervised Domain learning for Improved Face recognition 25

SSDL: Self-Supervised Domain Learning for Improved Face Reco...

引用

25th international conference on pattern recognition (ICPR)

作者： Arachchilage, Samadhi Wickrama Izquierdo, Ebroul Queen Mary Univ London Multimedia & Vis Grp Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9781728188089

Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual's face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. the domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an "easyto-hard" scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.

关键词： Knowledge management

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Human Action recognition Based on a Sequential Deep learning Model 10

Human Action Recognition Based on a Sequential Deep Learning...

引用

Joint 10th international conference on Informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and pattern recognition, icIVPR 2021

作者： Sikder, Niloy Ahad, Md Atiqur Rahman Nahid, Abdullah-Al Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Electronics and Communication Engineering Discipline Khulna University Khulna Bangladesh

ISBN: (纸本)9781665449212

Human Action recognition (HAR) is an application-oriented field that utilizes numerous machine learning methods to identify diverse human actions or movements to provide an appropriate or suitable response. A HAR method's success largely depends on the performance of the algorithms for data processing and activity prediction working in the background. this article inscribes an easy action recognition method based on a sequential Deep learning model. Instead of using intricated signal processing, feature extraction, and feature selection techniques, we used a special type of matrix formulated directly from raw data. the described method has been tested on three different HAR datasets - namely UCI HAR, WISDM, and KU-HAR - and the maximum classification accuracies achieved on them were 98.99%, 96.53%, and 96.67%, respectively. Other evaluation metrics yield similar outcomes, which asserts the method's reliability. Detailed descriptions and discussions of each step of the methodology, reported results, and limitations have been provided with necessary illustrations. Contribution-this paper proposes a new human action classification method by constructing circulant matrices directly from raw time-domain activity signals and employing an optimized deep learning architecture. © 2021 IEEE

关键词： Time domain analysis

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Medical Image Processing and Deep learning to Diagnose COVID-19 with CT Images 5

Medical Image Processing and Deep Learning to Diagnose COVID...

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5th international conference on pattern recognition and Image Analysis, IPRIA 2021

作者： Sebdani, Abbas Mazrouei Mostafavi, Amir Ragheb Isfahani Higher Education Institue Department of Biomedical Engineering Isfahan Iran University of Science and Arts Department of Biomedical Engineering Yazd Iran

ISBN: (纸本)9781665426596

In this article two classification algorithms of computed tomography images with the purpose of detection of COVID-19 based on the local binary pattern, the GLCM features and the other extraction of statistical has been proposed to classify the chest images into two classes, COVID-19 patient or non-COVID-19 person. For this purpose, 746 images from the lung of healthy people and with the positive symptoms of COVID-19 disorder from the public data were collected and the collection of the features that was extracted in gray level was the input for artificial neural network (ANN) and support vector machine classifier (SVM), and the result of models shows that the highest accuracy is for SVM 98.5% and 97.2% accuracy by using the ANN. © 2021 IEEE.

关键词： COVID-19

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Classification of fine-grained skin lesions based on ES-ResNet50V2 5

Classification of fine-grained skin lesions based on ES-ResN...

引用

5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Ge, Yusheng Liu, Tianyuan Gu, Qiuyu Dong, Chaoran Henan University of Technology Henan Zhengzhou450001 China Xinxiang Medical University Henan Xinxiang453003 China

ISBN: (纸本)9781510691667

To address the current challenges of heavy workload and low efficiency in skin lesion diagnosis, as well as the uneven distribution of sample numbers in skin lesion classification images, this paper proposes the ES-ResNet50V2 algorithm. Compared with the traditional ResNet50V2 algorithm, it removes the last fully connected layer, and adds SE and ECA modules after the feature extraction phase to enhance the model's perception of key features. the SE module recalibrates channel weights through global average pooling and two fully connected layers, while the ECA module uses one-dimensional convolution to efficiently achieve channel attention. In addition, a weighted sampler is introduced to solve the problem of uneven sample distribution. the accuracy of the ES-ResNet50V2 model proposed in this paper is about 9.45% higher than that of the ResNet50V classification. It is very important to popularize the automatic skin lesion recognition tool. © 2025 SPIE.

关键词： Skin Performance modeling data modeling Education and training Statistical modeling Image segmentation Convolution Feature extraction Tumor growth modeling machine learning

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Strength and Verticality of Nordic Dry Deep Mixing Columns—A Case Study in Norway 5th

Strength and Verticality of Nordic Dry Deep Mixing Columns—...

引用

5th international conference on Geotechnics for Sustainable Infrastructure Development, GEOTEC 2023

作者： Hov, Solve Berner, Markus Nikolai Rønning, Sigbjørn Bache, Bjørn Kristian Fiskvik Norwegian Geotechnical Institute Trondheim Norway Norwegian University of Science and Technology Trondheim Norway

ISBN: (纸本)9789819997213

this paper presents a case study in Norway where dry deep mixing was used extensively in one project to improve soft clays in several areas spanning 19 km in distance. Using the same execution parameters, same binder content (50 kg/m3) and the same binder type (cement, quicklime and lime kiln dust) throughout the project, this enabled studies of how the strength development relates to different clay properties. Elevation-based linear correlation analyses and machine learning techniques showed that the strength development was not correlated with sensitivity but had a weak correlation with water content and consistency limits. the verticality of 30 deep mixing columns was also investigated using a high-accuracy gyroscope-inclination sensor. the results show that the construction error requirements were fulfilled down to around 5 m depth, where under large deviation occurs, mainly due to bending of the drilling rod. the largest deviation at 15 m depth was around 1.6 m. these construction errors call for a certain conservatism in design practice. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data mining

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A Review on Visualization of Educational data in Online learning 19th

A Review on Visualization of Educational Data in Online Lear...

引用

19th international conference on Web-Based learning / 5th international Symposium on Emerging Technologies for Education ICWL/SETE)

作者： Dewan, M. Ali Akber Pachon, Walter Moreno Lin, Fuhua Athabasca Univ Sch Comp & Informat Syst Edmonton AB Canada

ISBN: (纸本)9783030669058;9783030669065

Higher educational institutions capture huge amounts of educational data, especially in online learning. data mining techniques have shown promises to interpret these data using different patterns. However, understanding the mining patterns and extracting meaningful information from the data require reasonable skills and knowledge for the users. Information visualization, due to its potential to display large amount of data, may fill this gap. In this paper, we present a short review of such visualization systems that focus on extracting meaningful information from the educational data. Visualizations have been used in different applications dealing with educational data, especially for monitoring student performance, understanding learning style, analyzing course and program status, and dropout prediction. In this paper, we reviewed the existing visualization systems, their design considerations, and their strengths and weaknesses to analyze educational data in the context of online learning. Research findings indicate that although some progress has been achieved in educational data mining and visualizations, designing and developing effective and easy to understand visualizations and having the functionalities of interactivity and time-series analysis are still challenging. this review provides insight into how to build a learner and instructor focused effective visualization system for an online learning environment.

关键词： Information visualization Educational data mining pattern recognition learning management system Visual analytics

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Construction and Application of E-commerce Recommendation Model Based on machine learning 24

Construction and Application of E-commerce Recommendation Mo...

引用

2024 5th international conference on Big data Economy and Information Management, BDEIM 2024

作者： Gao, Jing Shandong Vocational College of Science and Technology Shandong Weifang China

ISBN: (纸本)9798400711862

With the increasingly fierce market competition, e-commerce enterprises are facing many challenges, such as improving product quality, optimizing price strategy and deeply understanding customer needs. Among them, personalized recommendation based on user’s historical behavior data has become an important means for many e-commerce companies to enhance user experience, enhance user stickiness and promote sales growth. However, in the face of the increasing data scale, the current e-commerce recommendation model shows a significant lag in feature recognition, recommendation quality and operational efficiency, which is difficult to meet the application needs of the current e-commerce platform. In this regard, this paper will analyze the application status of e-commerce recommendation model, comprehensively explore the application feasibility of machine learning technology in the field of recommendation system, and propose a set of recommendation model construction scheme based on machine learning to realize personalized recommendation and improve the accuracy and user satisfaction of recommendation system. Practice has proved that, compared with the control model, the hybrid e-commerce recommendation model based on machine learning has outstanding performance in three groups of indicators: accuracy, recall and mAP value, which provides new ideas and methods for the function optimization of recommendation system under e-commerce platform. © 2024 Copyright held by the owner/author(s).

关键词： e-commerce recommendation system hybrid model machine learning personalized recommendation

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A Review on Comparison of machine learning Algorithms for Text Classification 5

A Review on Comparison of Machine Learning Algorithms for Te...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Dhingra, Mallika Dhabliya, Dharmesh Dubey, M.K. Gupta, Ankur Reddy, Dhoma Harshavardhan Manipal University Jaipur Department of Mathematics and Statistics Rajasthan Jaipur India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vaish College of Engineering Department of Computer Science and Engineering Haryana Rohtak India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Maharashtra Mumbai India

ISBN: (纸本)9798350398267

the majority of the data is preserved as text (about 75%), hence It is believed that text mining has a significant commercial potential. Unstructured texts continue to be the most readily available source of knowledge, despite the fact that knowledge may be accessed in many other places. Text classification that assigns documents to predetermined categories. machine learning approaches can categories texts more accurately. the goal of this work is to introduce text classification, give a description of the text classification technique, a general review of the classifiers, and a comparison of a few of the current classifiers. It is based on performance, time complexity, and other factors. On the basis of speed, accuracy, benefits, and drawbacks of existing classification methods such as Decision Tree, Naive Bayes, Support Vector machine, and k-Nearest Neighbours are compared. © 2022 IEEE.

关键词： Support vector machines

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