Multi-objective evolutionary algorithms of spiking neural networks

Spiking neural network (SNN) is considered as the third generation of artificial neural networks. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Among the many important issues that need to be explored in ESNN are determining the optimal pre-synaptic neurons and parameters values for a given data set. Moreover, previous studies have not investigated the performance of the multi-objective approach with ESNN. In this study, the aim is to find the optimal pre-synaptic neurons and parameter values for ESNN simultaneously by proposing several integrations between ESNN and differential evolution (DE). The proposed algorithms applied to address these problems include DE with evolving spiking neural network (DE-ESNN) and DE for parameter tuning with evolving spiking neural network (DEPT-ESNN). This study also utilized the approach of multi-objective (MOO) with ESNN for better learning structure and classification accuracy. Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Multi- Objective Differential Evolution with Evolving Spiking Neural Network (MODE-ESNN), Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (HSMODE-ESNN) and Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) were applied to improve ESNN structure and accuracy rates. The hybrid methods were tested by using seven benchmark data sets from the machine learning repository. The performance was evaluated using different criteria such as accuracy (ACC), geometric mean (GM), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and average site performance (ASP) using k-fold cross validation. Evaluation analysis shows that the proposed methods demonstrated better classification performance as compared to the standard ESNN especially in the case of imbalanced data sets. The findings revealed that the MEHSMODE-ESNN method statistically outperformed all the other methods using the different data sets and evaluation criteria. It is concluded that multi objective proposed methods have been evinced as the best proposed methods for most of the data sets used in this study. The findings have proven that the proposed algorithms attained the optimal presynaptic neurons and parameters values and MOO approach was applicable for the ESNN

Suicide and self-harm prediction based on social media data using machine learning algorithms

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigo

Dengue Prediction Using Deep Learning With Long Short-Term Memory

Dengue is a severe infectious disease on the rise in Malaysia, and there is a demand for artificial intelligence to support the health system. However, the application of deep learning, specifically Long Short-Term Memory (LSTM) time series forecasting, has not been explored by many in dengue prediction studies. However, considering the availability of daily weather data being collected, the ability of LSTM to capture long term dependencies can be leveraged in forecasting dengue cases. Therefore, this study investigates the performance and viability of LSTM time series forecasting on predicting dengue cases. An LSTM model is developed and evaluated to be compared to a Support Vector Regression (SVR) model by utilising the availability of a dengue dataset consisting of weather and climate data. The results indicated LSTM time series forecasting performed better than SVR, with R2 and MAE scoring 0.75 and 8.76. In short, LSTM has shown better performance and, in addition, capturing trends in the rise and fall of dengue cases. Altogether, this research could contribute to the fight against the increase of dengue cases without relying on forecasted weather data but instead, history

Detection of COVID-19 in Computed Tomography (CT) Scan Images using Deep Learning

Coronavirus disease (COVID-19) isa fresh genus found in 2019 that was not previously known in humans. On the other hand, Deep learning is one of the most important fields of medical imaging science at present.In this article, a model of deep learning is being trained for the COVID-19 detection in CT Scan images. This study is implemented using Python programming language. To build and train the Convolution Neural Network (CNN) model, Python Deep Learning libraries such as Keras and TensorFlow 2.0 have been utilized. As for the dataset, the open source dataset of COVID-19 chest computed tomography (CT) images were used. These image where been confirmed by the senior radiologist who performed Diagnosis of and treatment of patients with COVID-19. There were total of 745 images belonging to two classes were sampled. 348 positive (+) COVID-19 images and 397 negative (-) COVID-19 images. Based on the training process, the model was able to detect 79 per cent accuracy on the test set. The performance of the model, Convolution Neural Network were evaluated by comparing with Logistic Regression model. Findings from the research proves that Convolution Neural Network are reliable by producing higher accuracy rate of 79% while Logistic Regression produce a rate of 54%. However, in the future more reliable and quality image datasets should be used along with the metadata of the patients to train the model

Facial recognition using deep learning

In this article, the researcher presented the results of recognition of four emotional states (happy, sad, angry, and disgust) based on facial expressions. A deep learning method with a Convolutional Neural Network algorithm for recognizing problems has been proven very effective way to overcome the recognition problem. A comparative study is carried out using MUAD3D dataset from Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak for evaluating accuracy performance of this dataset. More discussion is provided to prove the effectiveness of the Convolutional Neural Network in recognition problems

Incorporating Heuristic Evaluation (HE) in the Evaluation of Visual Design of the Eco-Tourism Smartphone App

Heuristic Evaluation (HE) has proven to be important in the development of different computer systems but has not been incorporated in the development of eco-tourism smartphone applications. This results inusability issues that significantly affect user experience (UX) as discussed in literature. This study reports the HE in the design and development of Niranur Agro Farm (NAF) eco-tourism smartphone applications, which could improve UX. Eight experts participated in this study, utilizing the SMART mobile usability heuristic developed for mobile application and the severity rating scale to determine usability issues. The HE findings indicated that 22 usability issues were identified. One issue was rated 4 (catastrophe), four issues were rated 3 (major problem), twelve issues were rated 2 (minor problem) and five issues were rated 1 (cosmetics). Although there are issues rated as 4 and 3, the majority of the issues were considered to be minor (1 and 2 on the scale). Results indicated that it is crucial to incorporate HE into the design and development of the eco-tourism smartphone app to minimize the usability issues faced by users. It further validated that utilizing a specific heuristic for smartphone applications would ensure that all usability issues are correctly categorized and remedied

Development Of Undergraduate Students Course Recommender System

Recommender systems can be utilized to their full potential by suggesting to users the information they need, tailored to their interests and personality. However, researchers in the past have faced a common challenge when developing a recommender system: the "Cold-Start" problem. This issue arises when the system cannot suggest any information to the user due to a lack of data from that user. Individual interests and personalities are strongly influenced by the choices made regarding future careers, including course selection prior to entering university. To address the "Cold-Start" problem, a Course Recommender system has been proposed. The collection of data from users before developing this system is vital to ensure its efficient and effective function. The Agile methodology has been employed in the development of the Course Recommender system, encompassing the discovery of requirements, design to fulfill those requirements, prototyping, and evaluation. Holland's RIASEC personality test is utilized to gather users' interests and personality traits, which then serve as a guide to determine the courses that suit the users best. In a sample of 15 respondents chosen from 105, nine claimed that they were matched with a course of interest, while six did not feel the same way. Hence, the incorporation of Holland's RIASEC personality traits has indeed proven beneficial in selecting courses that best suit the users, thereby enhancing the effectiveness and efficiency of the developed Course Recommender system

Suicide and self-harm prediction based on social media data using machine learning algorithms

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor

Development of hybrid convolutional neural network and autoregressive integrated moving average on computed tomography image classification

One of the deadliest diseases in humans is lung cancer. Radiologists and experienced doctors spend much more time investigating the pulmonary nodules due to the high similarities between malignant and benign nodules. Recently, the computer-assisted diagnosis (CAD) tool for nodule detection can provide a second opinion for the doctor to diagnose lung cancer. Although machine learning technologies are extensively employed to identify lung cancer, the process of these methods is complex. The numerous researches have sought to automate the diagnosis of pulmonary nodules using convolutional neural networks (CNN) to aid radiologists in the lung screening process. However, CNN still confronts some challenges, including a significant number of false positives and limited performance in detecting lung cancer from computed tomography (CT) images. In this work, we proposed a hybrid of CNN and auto-regressive integrated moving average (ARIMA) for lung nodule classification using CT images to address the classification issue. The proposed hybrid CNN-ARIMA can classify CT images successfully with test accuracy, average sensitivity, average precision, average specificity, average F1-Score, and area under the curve (AUC) of 99.61%, 99.71%, 99.43%, 99.71%, 99.57%, and 1.000, respectively

Design And Development Of Manga Mobile Application Recommender System

Reading an e-book has become common in modern society due to its convenience. Manga, another sort of e-book, has developed a devoted following over the years. As a result, when selling products online, most businesses have a recommendation system in place. The majority of websites, however, are not made with the customer in mind; rather, the corporations' force add-ons sell to clients by making recommendations. As a result, our research primarily focused on a machine learning strategy known as a clustering-based method to address this constraint. The user's preferences have been taken into account by the clustering-based method. The agile methodology has been used in our study. A mobile application has been used to assess how effective this strategy is. Following evaluation, the utility of the system is assessed at 85.71 percent, followed by satisfaction at 93.20 percent, ease of use at 88.53 percent, ease of learning at 95.83 percent, and usefulness at 85.71 percent. Overall, the system performed well in the usability assessment with a score of 85.08 percent