Multi-constraints based deep learning model for automated segmentation and diagnosis of coronary artery disease in X-ray angiographic images

Background: The detection of coronary artery disease (CAD) from the X-ray coronary angiography is a crucial process which is hindered by various issues such as presence of noise, insufficient contrast of the input images along with the uncertainties caused by the motion due to respiration and variation of angles of vessels. Methods: In this article, an Automated Segmentation and Diagnosis of Coronary Artery Disease (ASCARIS) model is proposed in order to overcome the prevailing challenges in detection of CAD from the X-ray images. Initially, the preprocessing of the input images was carried out by using the modified wiener filter for the removal of both internal and external noise pixels from the images. Then, the enhancement of contrast was carried out by utilizing the optimized maximum principal curvature to preserve the edge information thereby contributing to increasing the segmentation accuracy. Further, the binarization of enhanced images was executed by the means of OTSU thresholding. The segmentation of coronary arteries was performed by implementing the Attention-based Nested U-Net, in which the attention estimator was incorporated to overcome the difficulties caused by intersections and overlapped arteries. The increased segmentation accuracy was achieved by performing angle estimation. Finally, the VGG-16 based architecture was implemented to extract threefold features from the segmented image to perform classification of X-ray images into normal and abnormal classes. Results: The experimentation of the proposed ASCARIS model was carried out in the MATLAB R2020a simulation tool and the evaluation of the proposed model was compared with several existing approaches in terms of accuracy, sensitivity, specificity, revised contrast to noise ratio, mean square error, dice coefficient, Jaccard similarity, Hausdorff distance, Peak signal-to-noise ratio (PSNR), segmentation accuracy and ROC curve. Discussion: The results obtained conclude that the proposed model outperforms the existing approaches in all the evaluation metrics thereby achieving optimized classification of CAD. The proposed method removes the large number of background artifacts and obtains a better vascular structure

Influence of sports facilities and programs on sports participation at Saudi Universities

Participation in physical activity without doubt has many benefits especially to students at all levels. It is however worrisome to note that this participation by a number of factors including availability of sport facilities and sport programs. Hence, the purpose of this study was to investigate the role or influence of sport facilities and programme on the participation in physical activity among the students in Saudi Arabia. In order to do this, the study adopted a descriptive approach with data collected with the aid of an adapted questionnaire administered on 643 undergraduate students who were randomly selected from 25 universities in Saudi Arabia. The data collected were analysed using Smart PLS software. It was revealed that there were inadequate sport facilities in the universities and this greatly affected students’ sports participation at Saudi Arabia universities. In addition, findings revealed that (non)availability of sports programs had effects on the students’ sports participation at Saudi Arabia universities. Therefore, these results showed that sports facilities provided by the universities could positively encourage students to use these facilities and participate in sports activities and that the sports programs provided by the universities had significant effect on students’ participation. The study thus concluded that in order to increase the participation level of students, universities should offer them different types of programs, workshops and training, which enable them to discover their skills and participate in the sports that they believe they can enjoy the most with their colleagues. There should also be adequate provision of sport facilities for the use of the students. It is believed that through these, the students and the country will drive the maximum benefits of sports and sport participation

The role of complementary and alternative medicines in general health and immunity

The immune system is a host protection system that includes numerous biological structures and processes in an organism which protects from diseases. It has been showed that there is significant relationship between immune system function and infectious diseases both in animal models and in humans. The aim of this research is to investigate whether Complementary and Alternative Medicines (CAMs) can be useful in boosting immune system to prevent and/or treat the infectious diseases in the early stage of infection. Accordingly, the previous research on this issue is investigated and the results re provided. This study also performs an analysis on the consumers’ reviews on turmeric to find the effectiveness of turmeric intake in improving general health status of patient through WebMD data. The results of this study demonstrated that the majority of consumers are highly satisfied with the use of turmeric in improving their health conditions. It is also found that the majority of patients have used turmeric as the alternative therapies and got positive results in their treatments. In general, the results of this research provided several recommendations on the use of CAMs for infectious diseases and revealed that immune system may be boosted by CAMs and accordingly help in prevention and/or treatment of infectious diseases. However, further evaluations for the use of CAMs through consumers’ experience analysis are needed to come to robust conclusions regarding the benefits of CAM as an alternative medicine for infectious disease such as COVID-19

Sustainability performance assessment using self-organizing maps (SOM) and classification and ensembles of regression trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment

Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Several millions of people suffer from Parkinson’s disease globally. Parkinson’s affects about 1% of people over 60 and its symptoms increase with age. The voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners, but which could be analyzed using recorded speech signals. With the huge advancements of technology, the medical data has increased dramatically, and therefore, there is a need to apply data mining and machine learning methods to extract new knowledge from this data. Several classification methods were used to analyze medical data sets and diagnostic problems, such as Parkinson’s Disease (PD). In addition, to improve the performance of classification, feature selection methods have been extensively used in many fields. This paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and wrapper-based. The dataset includes 240 recodes with 46 acoustic features extracted from 3 voice recording replications for 80 patients. The experimental results showed improvements when wrapper-based features selection method was used with KNN classifier with accuracy of 88.33%. The best obtained results were compared with other studies and it was found that this study provides comparable and superior results

A new model for enhancing student portal usage in Saudi Arabia universities

Portals are gateways that provide users with the information they need from different sources and display it on a single page. It is important to see that universities utilize the resources and services provided by their student portals. With the rapid development of Information and Communication Technology (ICT), the Ministry of Education in Saudi Arabia aims to develop and improve student portals by providing high-quality teaching services through the university portal systems. This paper discusses the importance of student portal usage in Saudi Arabian universities and investigates the factors that influence the utilization of student portals as perceived by the students of the Saudi universities. Based on these factors, a model is proposed which identifies students’ expectations about the Saudi university portals. A quantitative methodology was employed to develop the model. The results revealed that 8 out of 10 factors of the model are significant and positively affect student portal usage. The enhancement of student portals based on the identified significant factors will assist the universities to increase their utilization and their provided services

The Influence of Personal and Organizational Factors on Researchers’ Attitudes towards Sustainable Research Productivity in Saudi Universities

This paper studies organisational and personal factors affecting the behaviour of academic staff in Saudi universities. It seeks to investigate the beliefs of academic staff regarding the use of ICT and other factors to enhance the productivity of their scientific research. Also, this study aims to improve the sustainability of publication in Saudi universities. ICT includes using the library system for accessing research repository databases such as Science Direct, Web of Science, Scopus, etc. and other factors. The authors also developed the Importance Performance Map Analysis (IPMA) for these factors in order to provide guidelines for Saudi universities to build university systems to manage and measure the research productivity of academic staff. In summary, this research identifies factors critical to enhancing research productivity in Saudi universities. This will help to improve the sustainability of publication in Saudi universities. By enhancing the sustainability of publication, the reputation of Saudi universities will be improved and the reputation of academic staff in Saudi universities. As well the sustainability of publication will assist the promote of Saudi academic staff. The results show that personal factors such as personal use of ICT and organisational factors such as job satisfaction, university policy, IT funding, international collaboration and the level of ICT use in the university have positive effects on scientific research productivity among academic staff at Saudi universities.The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-20H04, Malaysia Research University Network (MRUN) Vot 4L876 and the Fundamental Research Grant Scheme (FRGS) Vot 5F073 supported under Ministry of Education Malaysia for the completion of the research. The work is partially supported by the project of the Grant Agency of Excellence, University of Hradec Kralove, FIM, Czech Republic (ID: 2206-2019) and by the SPEV project, University of Hradec Kralove, FIM, Czech Republic (ID: 2103–2019). This project is also partially supported by the grant TIN2016-75850-R from the Spanish Ministry of Economy and Competitiveness with FEDER funds

CIPM: Common identification process model for database forensics field

Database Forensics (DBF) domain is a branch of digital forensics, concerned with the identification, collection, reconstruction, analysis, and documentation of database crimes. Different researchers have introduced several identification models to handle database crimes. Majority of proposed models are not specific and are redundant, which makes these models a problem because of the multidimensional nature and high diversity of database systems. Accordingly, using the metamodeling approach, the current study is aimed at proposing a unified identification model applicable to the database forensic field. The model integrates and harmonizes all exiting identification processes into a single abstract model, called Common Identification Process Model (CIPM). The model comprises six phases: 1) notifying an incident, 2) responding to the incident, 3) identification of the incident source, 4) verification of the incident, 5) isolation of the database server and 6) provision of an investigation environment. CIMP was found capable of helping the practitioners and newcomers to the forensics domain to control database crimes

Comparative analysis of network forensic tools and network forensics processes

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to examine networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools