Despite the description of schedule type which can be easily found at any OpenMP reference material, little is known about the effect of different schedule type and chunk size on the parallel performance of shared memory multicore processor. Literature shows that performance analysis on different multicore platform overlooked the effect of different schedule type and chunk size, where often it was not explicitly specified. Hence, default assignment of the loop iterations among threads is assumed. By default, static schedule is used and size of chunk which is the ratio of total number of iteration to the number of threads is implemented. This research analyses the effect of different schedule type and chunk size on speedup achieved of different shared memory multicore platform under regular workload. Apart from that, the performance gain obtained after turning on/off certain multicore technologies and after turning on/off selected number of active cores per processor is also analysed. Results shows that different multicore technology exhibit different speedup value under different combination of schedule type and chunk size. Apart from that, it also observe that different multicore platform is better than the other in terms of speedup as the number of cores are increased.

Vision impaired individuals suffer from the lack of surrounding information which makes moving around very hard for them. In this work, a smart belt is designed

Road crashes have been viewed as one of the major issues leading to numerous economic losses, health problems, and fatalities, which are often due to driver actions (DA). Predicting effective DA for road crashes is crucial for developing effective intelligent transportation systems. The research community focused on transportation safety has made significant advancements in utilizing machine learning models to examine crash incidents in recent years. The application of various machine learning (ML) models has been widespread, but the specific focus on assessing DA has received relatively little attention. The article aims to propose a hybrid genetic algorithm combined with artficial neural network (GN-ANN) ML model to predict risky DA related to road accidents considering effective sampling strategies. This article also proposes a novel sampling strategy that combines Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Synthetic Minority Oversampling Technique (SMOTE)-Tomek Link named DBSTLink, where DBSCAN and SMOTE-Tomek Links are integrated to purify datasets from noise and outliers using DBSCAN and to balance class distribution by oversampling minority classes and deleting overlaps with SMOTE-Tomek Links to enhance classifier accuracy. This method is then compared with other sampling strategies like SMOTE, SMOTE Tomek Link, and DBSM (DBSCAN with SMOTE). The objective of this study is to strengthen the existing knowledge of crash probability by examining the influence of various data balancing with the proposed balancing approach on forecast F1-score, Matthew’s correlation coefficient (MCC), and G-mean. The results demonstrate that DBSTLink gives higher performance than other measures. The proposed hybrid GA-ANN machine learning model achieved an accuracy of 99%, an F1-score of 98%, and a recall of 99%. Additionally, it achieved a G-mean of 98% and an MCC of 96%. The research found the important attributes of DA that are responsible for road crashes.

The purpose of this research was to develop a service model for detecting anxiety and insomnia within a secure cloud environment and to determine whether there is a potential link between these conditions, such that an individual with anxiety may also have insomnia or vice versa. In this research, we aim to address the problem of detecting Insomnia and Anxiety disorder by constructing a new hybrid model. A survey was conducted in Bangladesh using both online and offline methods. A total of 3013 responses were gathered from different areas of the country using well-established medical questionnaires - HAM-A (Hamilton Anxiety Rating Scale) consisting of 14 questions to assess anxiety levels and ISI-7 (Insomnia Severity Index) consisting of 7 questions to evaluate insomnia. To predict mental illness in the country, five renowned machine learning algorithms were used: K-nearest neighbours, support vector machine (SVM), random forest (RF), Naive Bayes and linear discriminate analysis. Before and after applying the algorithms, statistical analysis and pre-processing of the dataset were carried out. The study also calculated the statistical correlation to determine the likelihood of someone experiencing insomnia if they had an anxiety disorder and vice versa. The SVM algorithm is the most accurate in detecting anxiety, with a 93.5% success rate and also in detecting insomnia, with a 94.36% success rate. This paper proposes a new hybrid model that can detect anxiety disorders with an accuracy of 98.67% and insomnia with an accuracy of 95.36%. Furthermore, this paper discovers that a person with an anxiety disorder has a 90.52% chance of also having insomnia, while a person with insomnia has a 93% chance of having an anxiety disorder. This study discovered that SVM offers higher accuracy in handling these types of datasets. Furthermore, our proposed model offers even greater accuracy than SVM. Additionally, individuals who experience anxiety are at a high risk of developing insomnia and vice versa.