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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Madhuparna Bhowmik Tulasi Sai Siri Chandana Bhawana Rudra NITK Surathkal Mangalore India

Fraudulent transactions have a huge impact on the economy and trust of a blockchain network. Consensus algorithms like proof of work or proof of stake can verify the validity of the transaction but not the nature of the users involved in the transactions or those who verify the transactions. This makes a blockchain network still vulnerable to fraudulent activities. One of the ways to eliminate fraud is by using machine learning techniques. machine learning can be of supervised or unsupervised nature. In this paper, we use various supervised machine learning techniques to check for fraudulent and legitimate transactions. We also provide an extensive comparative study of various supervised machine learning techniques like decision trees, Naive Bayes, logistic regression, multilayer perceptron, and so on for the above task.

关键词： Support vector machines machine learning algorithms Supervised learning Neural networks machine learning Blockchain Proof of Work

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Personality Prediction Through CV Analysis using machine learning algorithms for Automated E-Recruitment Process

Personality Prediction Through CV Analysis using Machine Lea...

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International Conference on Computer and Communications Technologies (ICCCT)

作者： G. Sudha Sasipriya K K Sri Janani S Nivethitha D Saranya S Karthick Thyagesh G Sri Sairam Engineering College Chennai India Tata Consultancy Services Chennai India

ISBN: (纸本)9781665414487

Personality of a person plays a crucial role in the organizational progress and also in the self-development process in an individual's life. One of the typical ways to predict the person's personality is either by a standard review or by scrutinizing the Curriculum Vitae of the candidate. The Conventional method of recruiting the candidates involves manual short listing of job seekers resumes according to the requirement of the company. In this work, a system that automates the task of segregating candidates based on eligibility criteria and personality evaluation in a recruitment process is proposed. To meet this requirement an online application is developed for the registration of candidate's details and analysis of candidate's personalities through an online Multiple-Choice Question (MCQ) test containing personality quizzes. Then the system analyzes professional eligibility by comparing the uploaded Curriculum Vitae trained datasets. This system employs a machine learning algorithm namely “Logistic Regression” which helps to choose fair decisions to recruit a suitable candidate. Thus, the final results of the personality quizzes will be sent to both candidates and the admin.

关键词： machine learning algorithms Oceans Resumes Manuals Predictive models Task analysis Standards

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Predicting Diabetes Mellitus Using machine learning algorithms

Predicting Diabetes Mellitus Using Machine Learning Algorith...

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International Conference on Information Systems and Computer Networks (ISCON)

作者： Juginder Pal Singh Deepesh Srivastava Manoj Kumar GLA University Mathura UP India

ISBN: (纸本)9781665447874

Diabetes mellitus is an illness caused due to the increase in degree of blood glucose. Diabetes is a continuous disorder which likelihood to cause an overall medical services crisis. The beginning of diabetes is assessed to increase drastically in the forthcoming years. This disease can be divided distinct types. Nonetheless, it has two significant clinical sorts, (T1D) and (T2D). It may cause numerous entanglements. With the speedy headway of AI, AI has been applied to various pieces of clinical prosperity. In this examination, we used estimations like Random forest, SVM to expect diabetes mellitus. The datasets comprise of a few clinical indicator (free) factors or Features and one objective (subordinate) variable or Label (Class). Free factors or Features incorporate the glucose, plasma glucose, HbAlc, age, etc.

关键词： Support vector machines Training machine learning algorithms Medical services Diabetes Glucose Plasmas

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Predictive Maintenance of an Electro-Injector through machine learning algorithms

Predictive Maintenance of an Electro-Injector through Machin...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Agostino M. Mangini A. Rinaldi M. Roccotelli M.P. Fanti Polytechnic University of Bari Italy

ISBN: (纸本)9781665442084

This work aims to define a system for measuring the "lift" of the anchor (the final part of the shutter) present inside the injector based on the use of machine learning classification algorithms. The measurement method determined is a non-invasive method, which guarantees that the internal organs of the injection system are not damaged to carry out the measurement and that it can be performed after welding the injector to prevent the "lift" from changing later. This measurement method provides for the classification of the currents circulating inside the solenoid, each of which can be associated with a specific value of the "injector lift. This approach is part of predictive maintenance techniques, a type of maintenance that tries to predict incorrect behavior of the system avoiding that critical operating conditions are ***, an analysis of the possible techniques for measuring the injector "lift" is carried out through the use of machine learning algorithms

关键词： machine learning algorithms Software packages Current measurement Welding machine learning Prediction algorithms Data models

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Influence of different machine learning algorithms on Prediction Model of Fuel Consumption of Inland Ships

Influence of different Machine learning algorithms on Predic...

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International Conference on Transportation Information and Safety (ICTIS)

作者： Shibo Zhao Qizhi Yin Xing Chen Fuqin Zhao Kuikui Zhao Jie Zheng Ocean and Energy Power Engineering School of Naval Architecture Wuhan University of Technology Wuhan China School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China Weichai Heavy Machinery Co Ltd. Weifang China Weichai Power Co Ltd. Weifang China Chongqing Vessel Survey Center Chongqing China

ISBN: (纸本)9781665497145

An accurate prediction of ship fuel consumption is of great significance to the economy of ship operations. To study the influence of different machine learning algorithms on the prediction of ship fuel consumption, an inland river cruise ship has been considered as the research object. Based on the actual operation data obtained by the energy efficiency data acquisition system of the real ship, a prediction model of the ship's main engine fuel consumption was established using the Multi-Layer Perceptron Regression, Decision Tree Regression, Random Forest Regression, K-Nearest Neighbor, Extra Trees Regression and Support Vector Regression algorithms. Mean absolute error, mean square error, root mean square error, and coefficient of determination were used to evaluate the accuracy of the model, and the effect of different machine learning algorithms on the inland ship fuel consumption prediction model was investigated under different data volumes. The results show that the Extra Trees Regression and Random Forest Regression models perform best, and the amount of modelling data has a significant impact on the training time of the Random Forest Regression and Support Vector Regression models.

关键词： Training Energy consumption machine learning algorithms Predictive models Data models Energy efficiency Fuels

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Analysis of Different Dimensionality Reduction Techniques and machine learning algorithms for an Intrusion Detection System

Analysis of Different Dimensionality Reduction Techniques an...

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International Conference on Computing, Communication & Automation (ICCCA)

作者： T N Varunram M B Shivaprasad K H Aishwarya Anush Balraj S V Savish S Ullas Amrita Vishwa Vidyapeetham Amrita School of Engineering Bengaluru India

ISBN: (纸本)9781665447539

Intrusion Detection Systems are of paramount importance in the present-day network security environment. By analyzing and predicting the behavior of incoming data, IDS helps in classifying it as either a benign or dangerous activity. In this paper, implementation of multiple Intrusion Detection Systems is done using different machine learning algorithms like KNN, AdaBoost, SVM, Logistic Regression, Random Forest, Artificial Neural Networks and Naive Bayes. The class imbalance in the dataset, caused by innate mannerisms of network analysis, is corrected using Synthetic Minority Oversampling Technique (SMOTE). The dataset is then reduced using three different Dimensionality Reduction Techniques namely PCA, t-SNE, and UMAP. The three datasets produced by these techniques are then used to build the Intrusion Detection System, using the machine learning algorithms. The models will implement binary classification to classify the incoming attacks between benign activity and DDoS Attacks.

关键词： Dimensionality reduction Support vector machines machine learning algorithms Intrusion detection Network analyzers Network security Random forests

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Comparison Between Different machine learning algorithms

Comparison Between Different Machine Learning Algorithms

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International Conference on Image Information Processing

作者： Souvik Sarkar Sidhant Singh Rohit Kumar Debraj Chatterjee Techno Internaional New Town Kolkata India

ISBN: (纸本)9781665433624

machine learning is a major application of AI it is a phenomena by which system automatically learn and improve from real world experience in form of data on which it is trained or by observing the surroundings. machine learning is changing the planet by transforming all segments including healthcare services, education, transport, food, entertainment, and different production line and lots of more. machine learning plays a great role in changing our lives as well as industries in sector like housing and applications, cars, shopping, food ordering, etc. One of the application of ML is character recognition. Character recognition is used to convert handwritten or printed documents or image characters of document to computer codes. The basic process involves examining the text of a document and translating the characters into code that can be used for data processing. Hardware, like an optical scanner or specialized circuit card is employed to repeat or read text while software typically handles the advanced processing. Software can also take advantage of machine learning algorithms to implement more advanced methods of intelligent character recognition, like identifying languages or styles of handwriting. In this era there are various machine learning algorithms like a linear regression, Convolutional Neural Network(CNN), kNN, K-Means, Random Forest etc. In this paper we are going to compare the results between between linear regression and Convolution Neural Network(CNN) over a same data-set of 0-9 digits.

关键词： Convolutional codes Training Analytical models machine learning algorithms Image analysis Convolution Computational modeling

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Improving the performance of machine learning algorithms for TOR detection

Improving the performance of Machine Learning Algorithms for...

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International Conference on Information Networking

作者： Adityan Gurunarayanan Ankit Agrawal Ashutosh Bhatia Deepak Kumar Vishwakarma Birla Institute of Technology and Science Pilani Center for Artificial Intelligence and Robotics (CAIR) Bangalore Defence Research Development Organization (DRDO)

ISBN: (数字)9781728191010

ISBN: (纸本)9781728191027

The Onion Router (TOR) networks provide anonymity, in terms of identity and location, to the Internet users by encrypting traffic multiple times along the path and routing it via an overlay network of servers. Although TOR was initially developed as a medium to maintain users' privacy, cyber criminals and hackers take advantage of this anonymity, and as a result, many illegal activities are carried out using TOR networks. With the ever-changing landscape of Internet services, traditional traffic analysis methods are not efficient for analyzing encrypted traffic and there is a need for alternative methods for analyzing TOR traffic. In this paper, we develop a machine learning model to identify whether a given network traffic is TOR or nonTOR. We use the ISCX2016 TOR-nonTOR dataset to train our model and perform random oversampling and random undersampling to remove data imbalance. Furthermore, to improve the efficiency of our classifiers, we use k-fold cross-validation and Grid Search algorithms for hyperparameter tuning. Results show that we achieve more than 90% accuracy with random sampling and hyperparameter tuning methods.

关键词： machine learning algorithms Web and internet services Classification algorithms Decision trees Cryptography Random forests Tuning

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A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms

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Ecotoxicology and environmental safety 2025年 300卷 118418页

作者： Yuen Zhong Yu Bao Hong Cheng Chaoqun Liu Shengzhu Huang Hualong Qiu Honglin Huang Jiajun Ren Hailiu Jin Caitong He Long Tian Yu Zhang Bangzhu Luo Tao Liang Mujun Li Zengnan Mo Longman Li Xiaobo Yang School of Public Health Guangxi Medical University Nanning Guangxi China Center for Genomic and Personalized Medicine Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine Guangxi Medical University Nanning Guangxi China. School of Public Health Guangxi Medical University Nanning Guangxi China. Department of science and education Maternal & Child Health Hospital of Yulin Yulin Guangxi China. Department of science and education Maternal & Child Health Hospital of Qinzhou Qinzhou Guangxi China. Department of health care Maternal & Child Health Hospital of Liuzhou Liuzhou Guangxi China. Department of Medical Services Section Maternal & Child Health Hospital of Guigang Guigang Guangxi China. Department of Pediatrics Maternal & Child Health Hospital of Wuzhou Wuzhou Guangxi China. Department of Reproductive Center First Affiliated Hospital of Guangxi Medical University Nanning China. Center for Genomic and Personalized Medicine Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine Guangxi Medical University Nanning Guangxi China Department of Urology and Nephrology First Affiliated Hospital of Guangxi Medical University Nanning China. Center for Genomic and Personalized Medicine Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine Guangxi Medical University Nanning Guangxi China. Electronic address: lilongman@***. Center for Genomic and Personalized Medicine Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine Guangxi Medical University Nanning Guangxi China. Electronic address: yangx@***.

The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal function on gestational anemia remain inconclusive. The concentrations of 20 blood essential trace and nonessential metals and 7 serum kidney function indicators were measured among 2000 pregnant women from the Guangxi Birth Cohort Study. Maternal hemoglobin < 110 g/L and hematocrit < 0.33 were defined as gestational anemia. We utilized twelve machine learning (ML) algorithms to independently screen for effective metal mixtures, assess their combined impacts and dose-response relationships on gestational anemia, and estimate the mediating role of kidney function. In the total population, manganese (Mn), copper (Cu), rubidium (Rb), and iron (Fe) were identified as significant metals with independent effects on gestational anemia by seven ML algorithms. The results of the Mn-Cu-Rb-Fe metal mixture ML models revealed that Rb (P = 0.008) and Fe (P < 0.001) were linearly and nonlinearly negatively associated with gestational anemia, respectively, whereas Cu (P = 0.069) showed a borderline positive association. The results for the Mn-Cu-Rb-Fe metal mixtures from both the first and second trimesters were consistent with their significance in the total population. Moreover, the protective effect of Rb increased while that of Fe decreased as pregnancy progressed; simultaneously, the risk effect of Cu diminished. In the third trimester, linear positive combined effects of tin (Sn) (P = 0.002) and cadmium (Cd) (P = 0.004) on gestational anemia were observed in the Sn-Rb-vanadium-Cd-Fe metal mixture ML models. Furthermore, we found that creatinine mediated the association between Fe and gestational anemia in the second trimester (mediated proportion = 3.7 %, P = 0.032). Hence, exposure to Mn-Cu-Rb-Fe mixtures inversely correlated with gestational anemia, which is mediated by creatinine. Sustained Rb and Fe supplementation may avert ane

关键词： Gestational anemia machine learning algorithms Mediation analysis Metal mixtures Renal function

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An Assessment of the machine learning algorithms Used in Agriculture

An Assessment of the Machine Learning Algorithms Used in Agr...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Munavvara Tahaseen Nageswara Rao Moparthi Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

ISBN: (纸本)9781665435253

Agriculture is considered as the backbone of India. Agriculture plays a vital role for meeting the food demands of growing population. To improve the crop yield, effective measures are to be taken by identifying the plant diseases at an early stage and taking timely action. In this paper different machine learning (ML) algorithms for plant leaf disease detection, crop yield prediction, weeds and pests detection are reviewed. The use of biosensors and hyper spectral data for agriculture are also analyzed in this paper.

关键词： Training machine learning algorithms Plants (biology) Transfer learning Sociology Crops Agriculture

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