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检索条件"主题词=Machine Learning Algorithms"

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Sentimental Analysis of Twitter Data using machine learning algorithms

Sentimental Analysis of Twitter Data using Machine Learning ...

引用

Forensics, Analytics, Big Data, Security (FABS), International Conference on

作者： G Prema Arokia Mary M S Hema R Maheshprabhu M Nageswara Guptha Kumaraguru College of Technology Coimbatore India Anurag University Hyderabad India Aurora’s Scientific & Technological Institute Hyderabad India Sri Venkateshwara College of Engineering Bangalore India

ISBN: (纸本)9781665420068

As social media is becoming a necessity for communication, a lot of data is available on this platform, which could be helpful for analysis. At a certain time, we use Twitter to tweet almost about similar topics, different emotions. In this article, sentimental analysis is proposed to get an idea about what people have in their minds or get people’s emotions. It segregates every tweet to its appropriate emotion. The emotion might be either positive or negative. The proposed methodology has two steps, namely preprocessing and classification. The corpus is created after all necessary preprocessing. The classification algorithms such as Logistic Regression, Linear SVC, Random Forest Classifier, Bernoulli NB, Decision Tree Classifier, Voting Classifier, and KNN Classifier are used for classification. Twitter 2020 and 2021 data has been taken for experimentation. The performance of Linear SVC shows a higher accuracy on training data, and Linear Regression shows higher accuracy in testing data.

关键词： machine learning algorithms Social networking (online) Blogs Supervised learning Static VAr compensators Training data Security

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Current Research Landscape of machine learning algorithms in Online Identity Fraud Prediction and Detection

Current Research Landscape of Machine Learning Algorithms in...

引用

IEEE International Technology Management Conference

作者： Brad Conlin Umar Ruhi Telfer School of Management University of Ottawa Ottawa Canada

ISBN: (纸本)9781665416559

Online fraud is an ever-growing problem that dates back to the beginning of e-commerce. An online fraudster can utilize many attack vectors and planes; however, identity fraud is one of the most common and detrimental to the victims. This paper will look at the current research landscape of the different machine learning algorithms and approaches used to help predict and detect online identity fraud. By adopting systematic review and meta-analysis protocols, this paper summarizes the types of machine learning algorithms used in online fraud detection and prevention, and highlights the reported effectiveness of these methods through performance measurement indicator analysis. Last, the researchers present the limitations and future research directions based on the results of this study

关键词： Radio frequency Support vector machines Measurement machine learning algorithms Technology management Systematics Protocols

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Importance Of Continuous Improvement of machine learning algorithms From A Health Care Management And Management Information Systems Perspective

Importance Of Continuous Improvement of Machine Learning Alg...

引用

International Conference on Engineering and Emerging Technologies (ICEET)

作者： Abdulhafis Abdulazeez Osuwa Hüseyin Öztoprak Cyprus International University Nicosia Cyprus

ISBN: (纸本)9781665427159

In modern times, researchers in the healthcare sector increasingly acknowledge the significance of data analysis. In the healthcare sector, data can be accessed from various means such as sensor data, Clinical data, and Omics data. Data from various wireless sensor devices and the wearable device are a form of sensor data. Data from the health records that store patient's records during treatment are a form of clinical data. A high dimensional data that contains proteome data types, transcriptome, and Genome are a form of Omics data. Raw data can be very difficult to handle manually, for this reason, the emergence of machine learning proved to be a significant tool for data analysis. The prediction and precision of healthcare data accurately are now more visible because of the various statistical techniques and advanced algorithms that machine learning employs. There are different types of algorithms in machine learning such as the hybrid model, reinforced learning, unsupervised learning, and supervised learning that are utilized for data analysis. In this paper, the description of various types of machine learning algorithms and the analysis of healthcare data are surveyed using the machine learning algorithms.

关键词： Wireless communication Wireless sensor networks machine learning algorithms Data analysis Wearable computers Supervised learning Medical services

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Distributed DoS Detection in IoT Networks Using Intelligent machine learning algorithms

Distributed DoS Detection in IoT Networks Using Intelligent ...

引用

Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), International Conference on

作者： S Binny Shamili Srimani Pendyala S. John Pimo Sagaya Aurelia P Rahul Reddy Damaraju Sri Sai Satyanarayana Kristu Jyoti College of Management and Technology Kerala India Institute of Aeronautical Engineering Hyderabad India Assistant Professor St. Xavier’s Catholic College of Engineering Nagercoil India Assistant professor Christ University Bangalore India Geethanjali Institute of Science and Technology Nellore India Scholar Chalmers University of Technology Goteborg Sweden

ISBN: (纸本)9781665482981

The threat of a Distributed Denial of Service (DDoS) attack on web-based services and applications is grave. It only takes a few minutes for one of these attacks to cripple these services, making them unavailable to anyone. The problem has further persisted with the widespread adoption of insecure Internet of Things (IoT) devices across the Internet. In addition, many currently used rule-based detection systems are weak points for attackers. We conducted a comparative analysis of ML algorithms to detect and classify DDoS attacks in this paper. These classifiers compare Nave Bayes with J48 and Random Forest with ZeroR ML as well as other machine learning algorithms. It was found that using the PCA method, the optimal number of features could be found. ML has been implemented with the help of the WEKA tool.

关键词： machine learning algorithms Denial-of-service attack Classification algorithms Internet of Things Computer crime Random forests Principal component analysis

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Dynamic Churn Prediction using machine learning algorithms - Predict your customer through customer behaviour

Dynamic Churn Prediction using Machine Learning Algorithms -...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： RajaGopal Kesiraju VLN P. Deeplakshmi School of Computing Kalasalingam Academy of Research and Education Tamilnadu

ISBN: (纸本)9781728158754;9781728192994

In current days, the customers are getting more attracted towards the quality of service (QoS) provided by the organizations. However, the current era is evidencing higher competition in providing technologically advanced QoS to the customers. Nevertheless, efficient customer relationship management systems can be advantageous for the organization for gaining more customers, maintaining customer relationships and improve customer retention by adding more profit to the organizational business. Furthermore, the machine learning models such as support vector machine algorithms can add more value to the customer retention strategies.

关键词： Support vector machines machine learning algorithms Heuristic algorithms Organizations Quality of service Predictive models Prediction algorithms

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Skin Cancer Detection Using machine learning algorithms

Skin Cancer Detection Using Machine Learning Algorithms

引用

Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： M. Ramachandro T. Daniya B. Saritha Vardhaman College of Engineering Kacharam Shamshabad Telangana India GMR Institute of Technology Rajam Andhra Pradesh India

ISBN: (纸本)9781665426923

Skin cancer is the far most common type of cancer. This can be treated effectivelyif it found early. The cancer detection in early stages is very expensive. Skin cancer is theabnormal growth of the skin cells. These are highly curable when identified and treated early. There are four types of skin cancers: Actinic Keratoses (AK), Basal cell carcinoma (BCC), Dermatofibroma and Melanoma. The late identification of cancer leads to the spread over other organs. The skin cancer can be detected from the images using convolution neural networks. ISIC image dataset and HAM10000 dataset will be used in this implementation. Transfer learning improves the performance of the model in CNN'*** trained models are used to extract features, which further used to classify types of skin cancer. The machine learning and deep learning methods used in this implementation are Random Forest, SVM, CNN and Dense net.

关键词： Support vector machines Technological innovation machine learning algorithms Transfer learning Neural networks Melanoma Feature extraction

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Comparative Study of machine learning algorithms for Twitter Sentiment Analysis

Comparative Study of Machine Learning Algorithms for Twitter...

引用

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Yash Indulkar Abhijit Patil Information Technology Thakur College of Science & Commerce Mumbai India

ISBN: (数字)9781728185194

ISBN: (纸本)9781728185200

Sentiment Analysis is important to understand various aspects of human emotions through different modes, the modes can be, either by understanding the text or analyzing it for obtaining the desired outputs. The three algorithms considered for sentiment analysis are Logistic Regression, Multinomial Naïve Bayes & Random Forest on the Uber & Ola datasets. The number of tweets extracted from Twitter is 3000. These tweets are cleaned & tokenized using python. The main factor of this paper is Google word2Vec, as the tokenized tweets are transformed with vocabulary from Google Word2Vec. Using this immense dataset of words, helped tokenized words to create a better vocabulary and understanding. Finally, the accuracy and the Mean Cross-Validation Accuracy (MCVA) was generated for all the three algorithms which are used to check if it was giving proper results to the trained data. Visualization was created for understanding the accuracy of three algorithms, which in turn helped to select the most accurate algorithm among others. The programming language used in this for pre-processing & analysis is Python.

关键词： Vocabulary Sentiment analysis machine learning algorithms Social networking (online) Classification algorithms Internet Random forests

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Sleep Calibre Assess Model of IT Workers by machine learning algorithms

Sleep Calibre Assess Model of IT Workers by Machine Learning...

引用

International Conference on Computer and Communications Technologies (ICCCT)

作者： P. Vichitra S. Mangayarkarasi School of Computing Sciences VISTAS Pallavaram Chennai India

ISBN: (纸本)9781665414487

In this research work, an intelligible sleep calibre was judged using the Questionnaire process of IT workers from age 21 to 35years. In this recommended model, overall offset directive ordination of QBUD (Questionnaire Based on Uneven Data sets) is used to bring about an act equate of sleep aspect quality includes “Low sleep time” and “Good sleep time”. These two regulations are used to elucidate the sleep rank. To manifest the appropriateness of the present project, we establish a sleep grade representation based upon the sleep time data sets collected from 120 IT workers. In this work, we are going to classify the quality of sleep using Support Vector machine and K-Nearest Neighbour classifier to cope up with their proper duration of sleep and furnishing the probabilistically cross reasonable experiments.

关键词： Support vector machines machine learning algorithms Computational modeling machine learning Probabilistic logic Regulation Data models

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A Survey on machine learning algorithms for Detecting Fake Instagram Accounts

A Survey on Machine Learning Algorithms for Detecting Fake I...

引用

Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Karishma Anklesaria Zeel Desai Vikram Kulkarni Harish Balasubramaniam MPSTME NMIMS University Mumbai India

ISBN: (纸本)9781665438124

With the tremendous increase in internet usage since its inception, cybercrime and online scamming has become extremely abundant. This surge in online scams is only helped by the presence of social media, which serves as one of the most popular platforms for the scammers to target their victims. Social media platforms such as Instagram, Facebook, Whatsapp, Twitter, have become the primary hub for scamsters, and these platforms facilitate their dangerous activities. The fraudsters carry out such scams using fake accounts which help them hide their true identity. To prevent such scams, we need a tool that helps us differentiate between fake and legitimate profiles. The machine learning models like Artificial neural network, AdaBoost, Multi-Layer Perceptron, Random Forest and Stochastic Gradient Descent (SGD) were implemented and their performance was compared in this paper to differentiate between the fake and legitimate profiles. We compare the results of these algorithms using various parameters and find Random Forest to be the most efficient.

关键词： Freeware machine learning algorithms Social networking (online) Multimedia Web sites Blogs Stochastic processes Surges

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Application of machine learning algorithms for power systems fault detection

Application of Machine Learning algorithms for power systems...

引用

International Conference on Systems and Control (ICSC)

作者： Nouha Bouchiba Azeddine Kaddouri University of Moncton NB Canada

ISBN: (纸本)9781665407830

This paper deals of the application of deep-learning and support vector machine algorithms for the power system fault detection. The power system considered is a standard IEEE 14-bus. Faults are integrated to the power system and simulated using Sim Power Systems toolbox of Matlab. All data obtained, the voltages and the currents of each bus, are saved and used to test both algorithms. Simulation results show the superiority of the deep-learning algorithm compared with support vector machines algorithm.

关键词： Support vector machines Deep learning machine learning algorithms Simulation Prediction algorithms Control systems Classification algorithms

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