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共 769 条记录，以下是431-440 订阅

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Illness Diagnosis Based on Ayurveda (Vat, Pitta, Kapha Dosha) Using machine learning

Illness Diagnosis Based on Ayurveda (Vat, Pitta, Kapha Dosha...

引用

Advances in Science and Technology (ICAST), international conference on

作者： Manjusha Deshmukh Maahi Khemchandani Saraswati College of Engineering Navi Mumbai India

Ayurveda's eight clinical limbs, including pulse analysis, are crucial for early disease diagnosis. the pulse (nadi) is the most crucial, and imbalances in vata, pitta, and kapha doshas can identify illness. this paper aims to analyze the relationship between Nadi Pariksha and pulse signal from a peripheral pulse analyzer. the research aims to develop pulse sensing and analysis methods using modern technologies to support Ayurvedic physicians. Prakruti, a balance in these doshas, influences physiological and psychological traits..

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A Comparative Study of machine learning and Deep learning Algorithm for Handwritten Digit Recognition

A Comparative Study of Machine Learning and Deep Learning Al...

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Mohammed Sohail Madan Lal Saini Vedant Pratap Singh Sarthak Dhir Vishal Patel Department of CSE Apex Institute of Technology Chandigarh University Punjab India

Handwritten digit recognition is a complex task in various real-world applications such as bank check processing, postal automation recognition etc. In recent time, different kind of learning algorithms are used to analyze and resolve this issue. this paper presents a comparative study on machine learning and deep learning algorithms such as Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest, LeNet-5, and YOLOv7. Publically available MNIST, DIDA, and MNIST MIX handwritten digit dataset were used in experimental work. the objective of this study is to find the best algorithm which can give an acceptable accuracy. For measuring the accuracy various parameters such as precision, recall, specificity, and F-measure were used. On the basic of experimental results, YOLOv7 has achieved detection accuracies above 98.3% and LeNet 5 has detection accuracy of 99.15%. It has been observed that deep learning algorithms have achieved higher accuracy than machine learning algorithms.

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A Survey of Sarcasm Detection Techniques in Natural Language processing

A Survey of Sarcasm Detection Techniques in Natural Language...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Bhuvanesh Singh Dilip Kumar Sharma University of St. Thomas GLA University Mathura India

Sarcasm is a linguistic style that is often employed in regular conversation and that natural language processing (NLP) systems may find difficult to recognize. the use of sarcasm in social media, online reviews, and other digital communication has increased in recent years, making it essential for NLP systems to detect sarcasm accurately. In this survey, we provide an overview of the current state of the art in sarcasm detection using NLP techniques. We discuss the various approaches to detect sarcasm, including machine learning, deep learning, and lexicon-based methods. We also review recent research on sarcasm detection in various languages and contexts, such as social media, customer reviews, and online forums. We also identify opportunities for future study and address the difficulties and limits of the present sarcasm detection techniques. the overall goal of this survey is to further this field of study by providing a thorough grasp of sarcasm detection in NLP.

关键词： Deep learning Social networking (online) Oral communication Media Linguistics Digital communication Natural language processing

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Detecting Fake News Using machine learning Based Approaches

Detecting Fake News Using Machine Learning Based Approaches

引用

international conference on Information and Computer Technologies (ICICT)

作者： Ty Edwards Ridwan Rashid Noel Department of Math and Computer Science Texas Lutheran University Seguin Texas USA

the spread of false information, commonly known as “fake news,” has become a significant problem in recent years, with the potential to mislead the public and influence important decisions. In this research, we focus on creating an automated system for fake news detection using natural language processing of news texts. We investigate different machine learning-based classification techniques to predict whether a text is a real or fake news. We utilized popular datasets from Kaggle and implemented Logistic Regression, Support Vector machine, decision tree, k-Nearest Neighbors, multinomial naïve Bayes, and Multilayer Perceptron, as well as an ensemble technique called stacking, which utilizes the other models in its prediction. We also perform a comparative analysis of the accuracies of the different techniques in fake news detection. From our experimental analysis, we found that the ensemble learner, Support Vector machine, and Multilayer Perceptron outperform the other approaches and have the highest overall accuracies.

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Artificial Narrow Intelligence Techniques in Intelligent Digital Financial Inclusion System for Digital Society

Artificial Narrow Intelligence Techniques in Intelligent Dig...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Ruchira Rawat Himanshu Rai Goyal Sachin Sharma Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

At current period of time financial services are going through a significant technological change. the changes are due to the advancement and research in Artificial Narrow Intelligent (ANI). As the financial institutes are adopting ANI as its core technology, a large digital wave can be seen in financial sector. Almost all the financial access such as normal account access, loan processing, insurance, purchasing, information related to finance etc. are now digitized leading towards digital society. It allows creation of large amount of data. Now the major challenge is to processing and analyzing that created data so the new ways and technique can be created to improve the services provided by financial institution for digital society. the three distinct innovation area of ANI can be seen by machine learning, non-traditional data and automation. the usage of AI in financial service for digital society has various impact on consumers and market include protection of consumer, consumer empowerment, financial crime, competition and stability of markets.

关键词： Technological innovation Finance Insurance machine learning learning (artificial intelligence) Stability analysis Fraud

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Photovoltaic Power Load Forecasting Method Based on DBO-DELM

Photovoltaic Power Load Forecasting Method Based on DBO-DELM

引用

6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Hongtao Hu Meng Yang School of Computer Science Xi'an Shiyou University Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Load forecasting is crucial to the stable operation of the power system, BP neural network and LSTM are commonly used for forecasting. However, BP neural network is prone to gradient disappearance or gradient explosion problems, while LSTM is more complex. In order to avoid the shortcomings of the above algorithms, this paper proposes to use the Dung Beetle Optimization (DBO) to optimize the parameters of the Deep Extreme learning machine (DELM) model. Firstly, the parameters of DELM are set, and the structural parameters of DELM are input into the dung beetle optimization algorithm in the form of a dictionary for iterative optimization to prevent DELM from falling into the local optimal solution; secondly, the obtained global optimal parameters are used to construct and train DELM load forecasting model; finally, the model is verified with real data from oil field photovoltaic power stations. Experimental results show that the DBO-DELM model performs better in terms of load forecast accuracy and algorithm complexity.

关键词： Photovoltaic systems Accuracy Load forecasting Computational modeling Oils signal processing algorithms Predictive models Data models Optimization Load modeling

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PCMA signal modulation identification method based on transfer learning

PCMA signal modulation identification method based on transf...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yuanke Zhao Xiaomin Ran Guangyi Liu Weikai Sun Zhengzhou University Zhengzhou China PLA Strategic Support Force Information Engineering University Zhengzhou China

PCMA (Paired carrier multiple access, PCMA) signal is a new type of digital modulation for satellite communication, which has been widely used because it can save up to 50% of the frequency band resources by mixing and superimposing the transmit and receive carriers of duplex link, and has excellent performance in interception resistance. Blind separation of PCMA signals is an important part of radio spectrum monitoring, high-precision modulation parameter information is the necessary basis for separation. In order to process the PCMA signal normally, it is necessary to distinguish the PCMA signal from the common single-channel satellite digital modulation signal, and then identify the modulation mode of the PCMA signal. the modulation identification of the PCMA signal provides the necessary guidance for parameter estimation and blind processing. In this paper, a PCMA signal modulation type recognition algorithm based on a fusion model of time-frequency analysis, residual network and transfer learning is proposed to address the problems of small data volume, difficulty in extracting feature parameters, and complexity of classifier model parameters of the current PCMA signal modulation type recognition algorithm.

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Athlete Performance Analysis: machine learning for Predicting Tennis Player Scores

Athlete Performance Analysis: Machine Learning for Predictin...

引用

international conference on Communications, Information System and Computer Engineering (CISCE)

作者： Peizhe Zhao Tianyu Luo Pengfei Bi Waterford Institute Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In recent years, machine learning and deep learning have become increasingly prevalent in the field of data classification and regression, with applications in a diverse range of disciplines. Furthermore, the field of sports analytics is also gradually becoming one of the topics of machine learning research. the application of machine learning and deep learning technologies to the analysis of real-time data from sporting events enables a more accurate understanding of the performance and status of athletes. Coaches can then combine these results to determine the current state of the athlete, which allows them to adjust the game strategy accordingly and increase the probability of winning. this study employs the example of tennis, utilising detailed data from the game “the Championships Wimbledon 2023”. the data was subjected to preliminary processing in order to identify the key factors that influence the players' scores. the data was then subjected to regression and prediction using a variety of machine learning algorithms and multilayer perceptron machines in deep learning. the results demonstrate that LightGBM and NLP are the most effective algorithms for accurately evaluating the current state of athletes with the available data.

关键词： Deep learning machine learning algorithms Accuracy Games Multilayer perceptrons Prediction algorithms Real-time systems

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Detection of Adulteration in Fruits Using machine learning 6

Detection of Adulteration in Fruits Using Machine Learning

引用

6th international conference on Wireless Communications, signal processing and Networking (IEEE WiSPNET)

作者： Brighty, S. Prince Sahaya Harini, G. Shri Vishal, N. Sri Ramakrishna Engn Coll Dept CSE Coimbatore Tamil Nadu India

ISBN: (纸本)9781665440868

Food is essential for life. the food we take should be pure, nutritious and free from any type of adulteration for proper maintenance of human health. In this paper, an IoT based food and formalin detection technique is developed to detect the presence of formalin using machine-learning approaches. Volatile compound HCHO gas sensor connected with Raspberry pi3 were used to extract the concentration of the formalin as a function of output voltage of any fruit or vegetable and different machine learning algorithms were used to classify the fruit or vegetable based on their extracted features. Supervised machine learning algorithms have been incorporated in our system to accurately predict the correct concentration of formalin at all temperatures which is also able to correctly classify between artificially added and naturally formed formalin.

关键词： Adulteration formalin naturally occurring formalin formaldehyde machine learning IoT HCHO gas sensor Raspberry Pi3 supervised machine learning algorithms

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Research on the Key Technology of Network Security Based on machine learning 6

Research on the Key Technology of Network Security Based on ...

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： He, Yong School of Preschool Education Jiangxi Teachers College China

ISBN: (纸本)9780738143705

With the continuous development of information technology, the huge number of network devices, applications, and the explosive expansion of network data have made the network environment increasingly complex, posing huge potential risks to network security. the targets of cyber attackers are no longer limited to cyber attacks on ordinary users, but they have shifted their targets to network environments with related backgrounds such as enterprises, governments, and countries. the diversification of network services has generated massive amounts of Internet data, and traditional network security technologies have been difficult to meet the current needs of network security in terms of performance and self-adaptability. Research on network security based on machine learning has achieved many results, showing strong capabilities in processing massive data, automatic learning, detection and identification, and broadening the development of ideas in the field of network security. In this paper, we combine machine learning-related technologies to improve intrusion detection performance and alarm correlation automation, and investigate key technologies such as machine learning-based network security situational awareness methods and dynamic data stream classification methods based on judgment feedback, in order to improve the detection performance, adaptive and generalization capabilities of machine learning-based network security technologies. © 2021 IEEE.

关键词： Anomaly detection

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