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Applying Deep learning Strategies for Optimizing Real-Time data Analysis in machine learning 3

Applying Deep Learning Strategies for Optimizing Real-Time D...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mishra, Varun Dev, Sudhanshu Singh, Akhilendra Pratap Krishna, Gautham Geetha, B. Kadam, Rutuja S. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Civil Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Pune India

ISBN: (纸本)9798350319125

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization. © 2023 IEEE.

关键词： Deep learning

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Extensive Analysis of machine learning Techniques in the Field of Heart Disease 3

Extensive Analysis of Machine Learning Techniques in the Fie...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Rustagi, Tanvi Vijarania, Meenu K.R. Mangalam University Cse Haryana Gurugram India

ISBN: (纸本)9798350342338

CAD (Coronary Artery Diseases) and CHF (Chronic Heart Failure) are two main forms of heart disease that lead to heart attacks. This disease is increasing the number of casualties worldwide. The heart is an organ that circulates filtered blood to each part of the body. The normal working of the heart is affected due to various conditions like Obesity, Hypertension, lifestyle etc. The large volume of heart diseases is avertable, but they continue to increase for the reason that protective measures are insufficient. To cure these life-threatening diseases, we need a more reliable and accurate system. machine learning procedures have been employed on numerous therapeutic datasets to industrialise the investigation of medical data. Recently, many researchers have started applying a number of machine learning techniques to assist the medical community and experts in the detection of heart-related *** technique like Support Vector machine,Artificial Neural Network and Random Forest are examples of data mining and machine learning approaches that are used to forecast heart disorders. This can help clinician to decide on the diagnosis and forecast of cardiac illness. The prime intention behind writing this research paper is to use machine learning procedures to forecast a patient's heart condition. © 2023 IEEE.

关键词： machine learning procedures Neural Network (NN) RF Supervised learning SVM

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Power data Security Defense Technology Based on Behavior Profiling and Its Application in Cloud Environment 3rd

Power Data Security Defense Technology Based on Behavior Pro...

引用

3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Ren, Qingqing Li, Haosheng Tian, Haomiao Feng, Haonan Cao, Shu Xinjiang Uygur Autonomous Region State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830000 China

ISBN: (纸本)9789819616978

This article focuses on the security challenges faced by power data in cloud environments, particularly the risk of data leakage caused by abnormal user behavior. The study proposes an innovative power data security defense technology based on behavioral profiling. This technology constructs a user behavior model through fuzzy C-means clustering algorithm, analyzes real-time access and operation characteristics of power data, forms accurate behavior portraits, and uses machine learning algorithms to identify abnormal behavior patterns. The experimental results show that this technology has extremely high accuracy in detecting normal and abnormal electricity usage behavior, with most data points having an accuracy rate of over 90%, and some even reaching 100%, effectively distinguishing between normal and abnormal behavior. The research conclusion shows that the defense strategy based on behavioral profiling provides a new perspective and efficient means for the security protection of power data, significantly improving the security protection level of power data in cloud environments, and building a solid defense line for the digital transformation of the power industry and the safe operation of smart grids. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Automated Plant Identification Through Deep learning with Particular Focus On Medicinal Plants 3

Automated Plant Identification Through Deep Learning with Pa...

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Bhutada, Sunil Reddy, Ch. Sreeja Reddy, R. Sparsha Vaishnavi, S. Kumar, Goutham Sreenidhi Institute of Science and Technology Department of Information Technology Yamnampet Hyderabad India

ISBN: (纸本)9798350348347

Ecology, pharmacology, agriculture, and conservation require species identification. Identification is difficult when there are several identical species. Flora-with's 400,000 species need skilled identification. Dichotomous keys might take days to identify a species, especially in biodiversity hotspots. Non-scientists may struggle with this identifying process. Manual plant identification is difficult and slow, hence an automated system that extracts descriptive elements from raw visual plant data to identify and output the plant species is needed. The initiative suggests using leaves to identify plants instead than flowers, seeds, bark, or seeds. The project aims to create cloud-based item recognition software with a computer vision-based search engine to identify plants faster. Deep learning, a machine learning branch, has excelled in image recognition and classification. Deep learning is crucial to automated medicinal plant identification. Traditional medicine and botany require medicinal plant identification. Manual identification is slow and error-prone. Deep learning-based medicinal plant identification solves these problems. Deep learning algorithms can spot patterns and make predictions from enormous data sets by learning sophisticated relationships between input properties and output labels. Deep learning algorithms can examine leaf shape, texture, and colour to accurately identify plants from a large dataset of plant pictures. Deep learning models are scalable and may be readily integrated into mobile and internet applications, making automated medicinal plant identification solutions user-friendly and accessible. Deep learning in automated medicinal plant identification is a promising and growing field that could transform plant identification and traditional medicine research. This dissertation uses machine learning/deep learning (convolutional neural networks) to recognize plants in 2D pictures. © 2023 IEEE.

关键词： learning algorithms

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Digit recognition by the Implementation of Supervised learning Using a Convolutional Neural Network 3

Digit Recognition by the Implementation of Supervised Learni...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Paliwal, Mrinal Kumar Chawla, Sunil Soni, Punit Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350342338

The recognition of handwritten numbers is a significant challenge in the field of computer vision, as it finds applications in various domains including banking, medical diagnostics, and handwriting recognition. In recent years, there has been a notable enhancement in our technological capabilities due to the progress made in internet accessibility and technology. Deep learning algorithms have emerged as valuable computational tools for addressing several challenges, including the task of digit recognition. The utilization of convolutional neural networks in the task of digit recognition is driven by their ability to acquire hierarchical representations from picture data. This scholarly article provides a comprehensive examination of the application of Convolutional Neural Networks (CNNs) in the field of machine learning for the purpose of digit recognition. This research examines the architectural components of Convolutional Neural Networks (CNNs), the training procedures employed, and the various methodologies employed for the purpose of digit recognition. The paper also provides an overview of the different datasets used for training and testing CNNs for digit recognition and a discussion of the results obtained from various studies. © 2023 IEEE.

关键词： Convolutional Neural Network Digit recognition MNIST database Neural Network Supervised learning

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Graptolite Image Classification Based on Feature Transfer and Mixup data Enhancement 3

Graptolite Image Classification Based on Feature Transfer an...

引用

3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Xu, Xiaoxiao Li, Chaofeng Liu, Honglin Institute of Logistics Science & Engineering Shanghai Maritime University Shanghai China Sinopec Petroleum Exploration and Production Research Institute Beijing China

ISBN: (纸本)9781510667563

Aiming at the problem of special shape of graptolites, insufficient training samples and manual identification of graptolites consumes a lot of time and energy, a new Graptolite image classification based on feature transfer and mixup data enhancement is proposed. First, we expand graptolite dataset by using traditional data enhancement and mixup to generate mixed samples, then use the deep learning model pretrained on the large-scale ImageNet dataset to construct a new transfer learning model through feature transfer. Finally, tenfold cross validation method is used to test the recognition accuracy of the model. The experimental results show that the classification accuracy of the proposed method on the graptolite dataset is 88.65%. Compared with the traditional data enhancement method and the pretrained model trained by ImageNet directly, the classification accuracy of graptolites is improved by 1.19%, which shows that the method has better recognition effect. © 2023 SPIE.

关键词： Image classification

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An Automatic Traffic Sign recognition and Classification Model Using Neural Networks 3rd

An Automatic Traffic Sign Recognition and Classification Mod...

引用

3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Padhy, Rajalaxmi Samal, Alisha Dash, Sanjit Kumar Mishra, Jibitesh Odisha University of Technology and Research Bhubaneswar India

ISBN: (纸本)9789819939312

The significance of traffic symbol recognition technologies, which have played a key role in street security, has been the subject of much interest to researchers. To accomplish their assessment, specialists employed Artificial Intelligence, deep learning, and image processing tools. Convolutional Neural Networks (CNN) are deep learning-based designs that have sparked a new and ongoing research into traffic symbol classifications and recognition frameworks. The objective of this paper is to establish a CNN model that is suitable for insertion purposes and has a high level of order exactness. For the series of street symbols, we used an upgraded LeNet-5 model. The German Traffic Sign recognition Benchmark (GTSRB) information base will function as the framework for our model architecture, which outperformed existing models. GTSRB will have 99.84 percent accuracy. We decided to use a camera to verify the proposed model for an implanted application because of its softness and reduced number of boundaries (0.38 million) based on the improved LeNet-5 structure. The outcomes are advantageous, demonstrating the effectiveness of the discussed strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Activity recognition in Video Frames for Enhancing Human-Robot Collaboration: A machine learning Perspective 3

Activity Recognition in Video Frames for Enhancing Human-Rob...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Chaubey, Ved Prakash Sharma, Shamneesh Kumar, Aman Malik, Arun Malik, Praveen Batra, Isha UpGrad Education Private Limited Delivery and Student Success UpGrad Campus Karnataka Bangalore India School of Computer Science & Engineering Lovely Professional University Punjab India School of Electronics and Communication Engineering Lovely Professional University Punjab India

ISBN: (纸本)9798350330724

Human activity recognition is a prominent area of investigation within computer vision research. Its potential applications span diverse fields such as security monitoring, healthcare, and human-computer interaction. The identification and classification of human activity through the utilization of machine learning techniques has yielded a plethora of proposed models within this dynamic field of scientific inquiry. By employing diverse kinetic models that are linked to the learning of spatial or temporal features, the characteristics of a collection of image or video data are extracted. The primary aim of this model is to identify and classify ongoing activities. This objective has been successfully achieved by various deep-layer trained models that have been implemented in this field. To fulfill the specified criteria, the proposed approach utilizes the Long Recurrent Convolutional Network (LRCN) and Long Short-Term Memory (LSTM) convolutional neural network architectures. The implementation of Vision-Based Human Activity recognition utilizes the 3D Convolutional Neural Network (CNN) model. The model undergoes training utilizing the UCF-50 dataset. The classification of the photos by the model is contingent upon the action being performed. The model will receive videos as input, which will undergo appropriate pre-processing before being classified based on the activity. The vision-based human activity identification system offers several advantages, including but not limited to accuracy, cost-effectiveness, and user-friendliness. © 2023 IEEE.

关键词： Neural network models

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Research on Wordle’s Popularity, Pass Rate, and Difficulty Based on Deep learning 3rd

Research on Wordle’s Popularity, Pass Rate, and Difficulty ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Jian, Shuoqu Gu, Nan Huo, Sihan Zhao, Runhao Xu, Dan National University of Defense Technology Changsha China Sun-Yat-Sen University Guangzhou China

ISBN: (纸本)9789819624676

Wordle is a very popular word game, which is not only attracted many players, but researchers’ interests. However, the prediction, pass rate, and the distinction of difficulty are rare be studied. In this paper, we study the game popularity of Wordle by using LSTM to predict the popularity of the game based on previous scenarios. We use BP-neural network to predict the pass rate of Wordle. Moreover, we evaluate the game difficulty by TOP-SIS model, and train a classifier that can make classification on the Wordle puzzles. In experiment part, we use the data from to predict the popularity of the game on March 1, 2023, and analyze the difficulty of four game levels, predict the difficulty of some levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Seasonal-Wise Occupational Accident Analysis Using Deep learning Paradigms 3rd

Seasonal-Wise Occupational Accident Analysis Using Deep Lear...

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3rd international conference on machine learning, Internet of Things and Big data, ICMIB 2023

作者： Nandhini, N. Anitha, A. School of Information Technology and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819939312

In recent years, occupational accidents causes a huge loss of human life and the development of the economy of the country. Many techniques are evolved for automating the safety precautions for employees in the industrial sectors such as mining, metals, construction, chemical, and electrical sections. However, the automation cannot be accurate as the data analysis is based on real-life data. Since the real-life data are imbalanced and uncertain, it is necessary to identify better tools to overcome these issues. Thus the proposed model utilizes SMOTE (Synthetic Minority Over-sampling Technique) for data balancing, whereas a rough set is used for identifying the significant features that help to maintain data consistency. The consistent data is then applied to the Deep Neural Network (DNN) for the classification process. The performance of the proposed model is checked against the evaluation metrics and compared with the existing deep learning models to exhibit the efficiency of the proposed model. Thus the findings of the proposed model may improve the abilities of safety professionals in the industrial sector to develop safety intervention activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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