Neural Networks Approach In Diagnosing Classes Of Anaemia

Hundreds of haematology forms are directed to Haematology unit every day from various departments from physicians that need the right diagnosis in patient’s blood. The processing may take several days depending on the workload and available resources. A combination of various factors has to be considered before a haematologist can diagnose classes of anaemia and is normally performed in several stages. The process can actually be performed using neural network approach, as it is capable in pattern recognition. Knowing the relevant factors that influence anaemia classification, a model of neural network can be produced if it is trained with sufficient data sets. Hence, this thesis presents the neural network model for anaemia classification and identifies parameter that affects its performance using backpropagation. The model is then implemented and the performance of the neural network is assessed. The model was able to diagnose classes of anaemia with 7 1.5 6% generalization. Finally, the model was compared with Radial Basis Function and Regression model to show that Multilayer Perceptron outperforms the other two models

What, how and when to use knowledge in neural network application

Neural network is one of the well-known artificial intelligence techniques. Neural Networks attempt to bring computers a little closer to the brain's capabilities by imitating certain aspects of information processing in the brain,"i a highly simplified way.These disciplines formulate a formula to form a brain like function, called artificial neuron. Artificial neuron comprises of large number of computational processing elements called units , nodes or cells. Neuron connected to each other with an associated weight. The weight represents information being used by the network to solve a problem. In understanding neural network one may think of three main questions: What is knowledge in neural network?; How to use this knowledge?; and When to use this knowledge? Knowledge is simply the weights that connect each neuron in neural networks. These weights are assigned randomly or generated using other procedures such as Nguyen-Widrow initialization algorithm. After training, the weights are stored and recalled in application phase. Thus, one needs to understand these three questions in applying the neural network methods. The aim of the study is to describe the general methodology that normally applies in any neural network based application. The methodology comprises five steps namely variable selection, data collection, data preprocessing, training &validation, and testing

Feng Shui Garden adviser System (FengShuiGAS)

This paper explores an approach to building an adaptive expert system prototype in an environment of human-computer collaboration. Components of an adaptive system are identified, with an emphasis on the mechanisms that enable adaptive behavior to occur.An adaptive expert system is necessary in order to communicate with the user and also adapts to user’sneeds. The adaptive expert system in this particular project is implemented on a Feng Shui garden design domain.A frame-based data representation and rule-based approach is applied to this project. In this research, the Feng Shui aspiration is adapted to users’ assessment and choice based on their preferences. This experimental expert system prototype displays low level adaptive capabilities that show sufficient promise to warrant further research

Neural network in handwritten recognition system: A survey

Neural network is a branch of Artificial Intelligence that imitates the biological processing function of the brain. Neural network has been implemented in various applications. One of the applications is handwritten recognition system. Handwritten is the art of an individual, which is controlled by the function of the brain. Every individual has his or her own style of writing. Hence, reading the handwriting is sonmetimes quite difficult. Many researches have been done in this area and yet still continuing. This paper presents a survey on the application of neural networks in handwritten recognition system. Several methodologies including feature extractions, neural network models and algorithms are highlighted

Comparative study of apriori-variant algorithms

Big Data era is currently generating tremendous amount of data in various fields such as finance, social media, transportation and medicine. Handling and processing this “big data” demand powerful data mining methods and analysis tools that can turn data into useful knowledge. One of data mining methods is frequent itemset mining that has been implemented in real world applications, such as identifying buying patterns in grocery and online customers’ behavior.Apriori is a classical algorithm in frequent itemset mining, that able to discover large number or itemset with a certain threshold value. However, the algorithm suffers from scanning time problem while generating candidates of frequent itemsets.This study presents a comparative study between several Apriori-variant algorithms and examines their scanning time.We performed experiments using several sets of different transactional data.The result shows that the improved Apriori algorithm manage to produce itemsets faster than the original Apriori algorithm

Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks

MATHVISION PROTOTYPE USING PREDICTIVE ANALYTICS

Malaysia is currently going towards Industrial Revolution (IR) 4.0 which makes Science, Technology, Engineering and Mathematics (STEM) subjects become more crucial. IR 4.0 covers a lot of aspects especially in digital transformation in manufacturing, and this certainly requires strong mathematical knowledge. To achieve this goal, students need to have a good foundation in Mathematics subject. However, due to the increased number of students nowadays, teachers are facing challenges to track students’ progress efficiently. In this study, a predictive model has been developed that aims to assist Mathematics teachers in monitoring their students. The prototype, called MathVision, can track students’ progress effectively in each topic and subtopic of Mathematics subject and predict the grades that students will obtain based on the history result. A total of 207 instances was collected among Form 5 students from a government school to represent the samples for the modelling task. The Multiclass Decision Forest algorithm appeared to be the best predictive model with 95.16% accuracy, as compared to Boosted Decision Tree, Logistic Regression, and Neural Network. Flutter framework and Firebase services were used for front-end and back-end system respectively, and Microsoft Power BI was used for data visualization. The result of prototype testing showed that MathVision could predict students’ grade for Quiz 2 based on Quiz 1 performance. MathVision is also capable for real-time prediction that guarantees an immediate response time which can help Mathematics teachers to support students who need further assistance in this subject based on the prediction given. For MathVision’s future improvement, the number of instances needs to increase, and more significant variables need to be added

WORD SENSE DISAMBIGUATION USING FUZZY SEMANTIC-BASED STRING SIMILARITY MODEL

Sentences are the language of human communication. This communication medium is so fluid that words and meaning can have many interpretations by readers. Besides, a document that consists of thousands of sentences would be tough for the reader to understand the content. In this case, computer power is required to analyse the gigantic batch size of the text. However, there are several arguments that actively discuss regarding the output generated by a computer toward the meaning of the passage in terms of accuracy. One of the reasons for this issue is the existing of the ambiguous word with multiple meanings in a sentence. The passage might be incorrectly translated due to wrong sense selection during the early phase of sentence translation. Translating sentence in this paper means either the sentence has a negative or positive meaning. Thus, this research discusses on how to disambiguate the term in a sentence by referring to the Wordnet repository by proposing the use of fuzzy semantic-based similarity model. The proposed model promising to return a good result for detecting the similarity of two sentences that has been proven in the past research. At the end of this paper, preliminary result which shows the flow of how the proposed framework working is discussed

PREDICTION OF LIFE EXPECTANCY FOR ASIAN POPULATION USING MACHINE LEARNING ALGORITHMS

Predicting life expectancy has become more important nowadays as life has become more vulnerable due to many factors, including social, economic, environmental, education, lifestyle, and health condition. A lot of studies on life expectancy have been carried out. However, studies focusing on the Asian population are limited. This study presents machine learning algorithms for life expectancy based on the Asian population dataset. Comparisons are made between tree classifier models, namely, J48, Random Tree, and Random Forest. Cross validations with 10 and 20 folds are used. Results show that the highest accuracy is obtained with Random Forest with 84% accuracy with 10-fold cross-validation. This study further identifies the most significant factors that influence life expectancy prediction, which includes socioeconomic factors and educational status, health conditions and infectious disease