Data Mining - Concepts and Techniques.

There are lot of treatments that are available for various diseases. No human can possibly know about all the medicines and the diseases. So, the problem is that there isn't any place where anyone can have the details of the diseases or the medicines. What if there is a place where you can find your health problem just by entering symptoms or just scanning an ECG or you can check whether the prescribed medicine is supposed to be used the way you are told to. Then it will help us to deduce the problem and to verify the solution. The proposed idea is to create a system with artificial intelligence that can meet the requirements. The AI can predict the diseases based on the symptoms and give the list of available treatments. The System can also give the composition of the medicines and their prescribed uses. It helps them to take the correct treatment. Hence the people can have an idea about their health and can have the right protection.

A novel approach for medical assistance using trained chatbot

A vast amount of data is generated every second for microblogs, content sharing via social media sites, and social networking. Twitter is an essential popular microblog where people voice their opinions about daily issues. Recently, analyzing these opinions is the primary concern of Sentiment analysis or opinion mining. Efficiently capturing, gathering, and analyzing sentiments have been challenging for researchers. To deal with these challenges, in this research work, we propose a highly accurate approach for SA of fake news on COVID-19. The fake news dataset contains fake news on COVID-19; we started by data preprocessing (replace the missing value, noise removal, tokenization, and stemming). We applied a semantic model with term frequency and inverse document frequency weighting for data representation. In the measuring and evaluation step, we applied eight machine-learning algorithms such as Naive Bayesian, Adaboost, K-nearest neighbors, random forest, logistic regression, decision tree, neural networks, and support vector machine and four deep learning CNN, LSTM, RNN, and GRU. Afterward, based on the results, we boiled a highly efficient prediction model with python, and we trained and evaluated the classification model according to the performance measures (confusion matrix, classification rate, true positives rate...), then tested the model on a set of unclassified fake news on COVID-19, to predict the sentiment class of each fake news on COVID-19. Obtained results demonstrate a high accuracy compared to the other models. Finally, a set of recommendations is provided with future directions for this research to help researchers select an efficient sentiment analysis model on Twitter data.

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Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches.

Manly transformation in finite mixture modeling

The increase in acceptability and popularity of social media has made extracting information from the data generated on social media an emerging field of research. An important branch of this field is predicting future events using social media data. This paper is focused on predicting box-office revenue of a movie by mining people's intention to purchase a movie ticket, termed purchase intention, from trailer reviews. Movie revenue prediction is important due to risks involved in movie production despite the high cost involved in the production. Previous studies in this domain focus on the use of twitter data and IMDB reviews for the prediction of movies that have already been released. In this paper, we build a model for movie revenue prediction prior to the movie's release using YouTube trailer reviews. Our model consists of novel methods of calculating purchase intention, positive-to-negative sentiment ratio, and like-to-dislike ratio for movie revenue prediction. Our experimental results prove the superiority of our approach compared to three baseline approaches and achieved a relative absolute error of 29.65%.

Movie Revenue Prediction Based on Purchase Intention Mining Using YouTube Trailer Reviews

Twitter, an online micro-blogging and social networking service, provides registered users the ability to write in 140 characters anything they wish and hence providing them the opportunity to express their opinions and sentiments on events taking place. Politically sentimental tweets are top-trending tweets; whenever election is near, users tweet about their favorite candidates or political parties and at times give their reasons for that. In this study, we hybridize two n-gram [two n-gram models used in this study are unigram and n-gram. Therefore, in this study, where unigram is mentioned that refers to a least-order n-gram (unigram) and where n-gram is mentioned that refers to the highest-order (full sentence or tweet level) n-gram] models and applied Laplace smoothing to Naive Bayesian classifier and Katz back-off on the model. This was done in order to smoothen and address the limitation of accuracy in terms of precision and recall of n-gram models caused by the ‘zero count problem.’ Result from our baseline model shows an increase of 6.05% in average F-Harmonic accuracy in comparison with the n-gram model and 1.75% increase in comparison with the semantic-topic model proposed from a previous study on the same dataset, i.e., Obama–McCain dataset.

Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter

Artificial Intelligence has significantly gained grounds in our daily livelihood in this age of information and technology. As with any field of study, evolution takes place in terms of breakthrough or developmental research leading to advancement and friendly usability of that specific technology. Problems from different areas have been successfully solved using Artificial Intelligence algorithms. In order to use AI algorithms in solving Personalized Medicine problems such as; disease detection or prediction, accurate disease diagnosis, and treatment optimization, the choice of the algorithm influenced by its ability and applicability matters. This paper reviews the application and ability of artificial neural network (ANN), support vector machines (SVM), Naïve Bayes, and fuzzy logic in solving personalized medicine problems, and shows that the obtained results meet expectations. Also, the achievement from the previous studies encourages developers and researchers to use these algorithms in solving Medical and Personalized Medicine problems.

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Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems

There are different techniques in conducting data mining that range from clustering, association rule mining, prediction and classification. These techniques are applied using learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, and Artificial Neural Network (ANN). When conducting data mining, the choice of algorithm to use is an important decision because it depends on factors such as the nature or type of data under examination, and the target outcome of the data mining activity. In this study, we compare Naïve Bayes and Multilayer Perceptron using the classification technique as a case study on the Bank Notes dataset from the University of California Irvine (UCI) from two standpoints, which are; holdout and cross validation. Result from experiments show Multilayer Perceptron outperforms Naïve Bayes in terms of accuracy from both standpoints of holdout and cross validation.

Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset

Cars are essentially part of our everyday lives. There are different types of cars as produced by different manufacturers; therefore the buyer has a choice to make. The choice buyers or drivers have mostly depends on the price, safety, and how luxurious or spacious the car is. Data mining tasks in terms of classification or prediction are applied in a variety of domains which includes manufacturing and business. But the choice of algorithm can be confusing because some algorithms are argued to have better performance record than others, depending on the associated task and nature of dataset. This study analyzes the performance of three data mining algorithms in terms of speed and accuracy on the car evaluation dataset obtained from the University of California Irvine (UCI) dataset. Keywords— Data Mining, Decision Tree, Neural Network, Naive Bayesian

Performance Comparison of Data Mining Algorithms: A Case Study on Car Evaluation Dataset

Face detection and analysis is an important area in computer vision. Furthermore, face detection has been an active research field in the recent years following the advancement in digital image processing. The visualisation of visual entities or sub-pattern composition may become complex to visualise due to the high frequency of noise and light effect during examination. This study focuses on evaluating the ability of Haar classifier in detecting faces from three paired Min-Max values used on histogram stretching. Min-Max histogram stretching was the selected method for implementation given that it appears to be the appropriate technique from the observation carried out. Experimental results show that, 60-240 MinMax values, Haar classifier can accurately detect faces compared to the two values.

Jamilu Awwalu

Papers

Hybrid N-gram model using Naïve Bayes for classification of political sentiments on Twitter

Artificial Intelligence in Personalized Medicine Application of AI Algorithms in Solving Personalized Medicine Problems

Comparative Analysis of Algorithms in Supervised Classification: A Case study of Bank Notes Dataset

Performance Comparison of Data Mining Algorithms: A Case Study on Car Evaluation Dataset

Performance comparison of min-max normalisation on frontal face detection using haar classifiers