A Comparative Study of Artificial Neural Network and Genetic Algorithm in Search Engine Optimization

Search engine optimization applies search principles in search engines to assign a higher ranking to the most suitable webpage.  Nowadays, information searching is done ubiquitously on the World Wide Web with the help of search engines. However, the process needs to be efficient and produces accurate results at the same time. In this research, the objectives are to implement and evaluate the Artificial Neural Network and Genetic Algorithms. The accuracy result for both algorithms is compared by implementing keyword ranking, Search Engine Result Page visibility and time retrieval for document-based and e-commerce websites. To achieve them, firstly the problem and data are defined. Next, two datasets are imported from Kaggle and transformed into a more useful format. Then, the Artificial Neural Network and Genetic Algorithms are implemented on these datasets in Python using Jupyter Notebook tools. Subsequently, the accuracy of keyword ranking, Search Engine Result Page visibility and time retrieval for these datasets are observed based on the output and graph displayed. Lastly, an analysis of the results is performed. Conclusively, the Genetic Algorithm demonstrates a higher percentage of accuracy results than Artificial Neural Network algorithm in keyword ranking and SERP visibility. However, the accuracy results of time retrieval are vice versa. The results in Genetic Algorithm shows 9.0%, 9.0% and 3.0% in e-commerce dataset for keyword ranking and 4.0%, 51.0% and 1.0% in document-based dataset for SERP visibility. Next, Artificial Neural Network algorithm shows result 8.0%, 7.0% and 7.0% in e-commerce dataset and 3.0%, 50.0% and 4.0% in document-based dataset for time retrieval. Therefore, the results validated the ability of the Genetic Algorithm as one of the most applied algorithms in the search engine optimization field

Comparison of daily rainfall forecasting using multilayer perceptron neural network model

Rainfall is important in predicting weather forecast particularly to the agriculture sector and also in environment which gives great contribution towards the economy of the nation. Thus, it is important for the hydrologists to forecast daily rainfall in order to help the other people in the agriculture sector to proceed with their harvesting schedules accordingly and to make sure the results of their crops would be satisfying. This study is set to forecast the daily rainfall future value using ARIMA model and Artificial Neural Network (ANN) model. Both method is evaluated by using Mean Absolute Error (MAE), Mean Forecast Error (MFE), Root Mean Squared Error (RMSE) and coefficient of determination (R ). The results showed that ANN model has outperformed results than ARIMA model. The results also showed ANN has under-forecast the daily rainfall data by 2.21% compare to ARIMA with over-forecast of -3.34%. From this study, it shows that the ANN (6,4,1) model produces better results of MAE (8.4208), MFE (2.2188), RMSE (34.6740) and R (0.9432) compared to ARIMA model. This has proved that ANN model has outperformed ARIMA model in predicting daily rainfall values

Adolescent to Adolescent Transformation Program- Nurturing, Enhancing and Promoting Adolescents’ Healthy Habit (ATAP-NEPAH): Curbing Social Problems Among Adolescents in Kelantan Through Peer-To-Peer Health Education

The objectives of ATAP-NEPAH are to enhance and nurture healthy habits among adolescents as well as to empower adolescents in inculcating these healthy habits among them. Health education through peer-to-peer approach is used to instill the knowledge on important areas such as sexual and reproductive health, smoking, substance abuse, illegal street racing (rempit) and mental health. Specific modules were developed by experts (lecturers) in multidisciplinary fields in collaboration with Malaysian Association for Adolescent Health (MAAH), National Population and Family Development Board (NPFDB), Reproductive Health Association of Kelantan (REHAK) and Rhaudatus Sakinah Kelantan. The trained Medical Students Facilitator Team (MSFT) of USM became trainers to secondary one school students. The selected school students were trained by the medical students to become peer educators to their juniors and peers. There was improvement in the readiness level of peer educators, knowledge and attitude towards healthy habits and risky behaviors of other school students after the intervention

Using models to support the search-based testing of web applications

A web application model offers a high-level description of a web application’s behaviour. Using the web application’s model to derive test cases serves as a good starting point in testing, since the model is created based on the web application’s specification. To improve the capabilities of the derived test cases in revealing faults in the web application’s behaviour, these test cases can be improved using search-based testing. In this thesis, we propose a search-based testing approach that explores a large search space of possible combinations of a web application’s behaviour. The approach evolves an initial set of model-based test cases to produce more test cases that are more effective in finding failures in the web application’s behaviour. Firstly, we present a pseudo-Genetic Algorithm that evolves a set of model-based test cases by mutating or recombining their model-based genes to obtain a set of model-based offspring test cases. The algorithm transforms the offspring test cases into executable test cases. We incorporate the pseudo-Genetic Algorithm in a search-based testing tool called MutateIFML. We describe the model-based test representation which uses the Interaction Flow Modelling Language to model the test cases and the system under test. We define the mutation score of the population as the fitness function for MutateIFML. Secondly, we validate MutateIFML’s capability and applicability using two web applications: OpenBiblio and RosarioSIS. We discuss our results and the achievements of MutateIFML in producing effective test cases that are measured by the capability of the population in killing faults that are seeded into these web applications. The validation of the approach reveals that enhancing the single fitness function with a coverage indicator can help in guiding the selection phase of the search-based testing approach. We discover MutateIFML’s limitation in scaling up to larger more complicated web applications due to the initial manual task of fault-seeding, the absence of a suitable automated support tool for the testing framework and the complexities of MutateIFML. We then extend the single fitness function used in MutateIFML to include branch coverage. We validate the improved multi-objective fitness function with a small case study that focuses on a module of a web application to examine the viability of the multi-objective fitness function in killing additional mutant systems under test. Although the multi-objective fitness function is able to kill additional mutants, we realise that it can cause MutateIFML’s testing cost to increase substantially. Finally, we suggest possible directions for future work

Depression Prediction Using the Classification and Regression Tree (CART)

Depression is a mood disorder that involves the continuing feeling of sadness and loss of interest. The crucial life events for an individual, such as losing a job may lead to depression. However, the feelings of grief and sadness are clinically diagnosed as part of depression only if the symptoms persist for at least two weeks. Eventually, depression can last for several weeks, months, or years. Some symptoms of depression may overlap with other somatic illnesses and cause difficulty in diagnosing it. This research aims to use the developed forecast model to predict future depression cases and it uses classification and regression tree (CART) of data mining approach, to predict or classify whether an individual suffers from depression or not. The dataset that was used in this research is the depression dataset from the Dataset of Students’ Mental Health at an international university in Japan. This dataset consists of 268 numbers of instances and it has 10 attributes. In addition, to acquire the results, the machine learning software that was used is R Studio and the language that was used is R Programming. Besides that, evaluation metrics were used to evaluate the performance of the forecasted model and the evaluation metrics that were used were accuracy, precision and recall. From the research, it shows that the value for accuracy is 0.50(50%), precision is 1.00 (100%) and recall is 0.50 (50%). Following that, it shows that this forecasting model has the highest value of precision which is 1.00(100%). Furthermore, from the data, it also shows that teenagers in the age range from 18-22 are most likely to get depression and they also have the intention of suicide. Lastly, in the future, this research could be continued with more training on different datasets and more different techniques could be used. Besides that, this research could be improved by adding other algorithms to best understand the strengths and weaknesses of other techniques