The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has had a significant impact on the lives of millions of people and has forced a substantial load on healthcare organizations. Numerous deep-learning models have been utilized for diagnosing coronaviruses from chest computed tomography (CT) images. However, in light of the limited availability of datasets on COVID-19, the pre-trained deep learning networks were used. The main objective of this research is to construct and develop an automated approach for the early detection and diagnosis of COVID-19 in thoracic CT images. This paper proposes the DDTL-COV model, a deep transfer learning model based on DenseNet121, to classify patients on CT scans as either COVID or non-COVID, utilizing weights obtained from the ImageNet dataset. Two datasets were used to train the DDTL-COV model: the SARS-CoV-2 CT-scan dataset and the COVID19-CT dataset. In the SARS-CoV-2 CT dataset, the model achieved a good accuracy of 99.6%. However, on the second dataset (COVID19-CT dataset), its performance shows an accuracy rate of 89%. These results show that the model performed better than alternative methods.

The COVID-19 outbreak has been affecting the health of people all around the world. With the number of confirmed cases and deaths still rising daily, so the main aim is to detect positive cases as soon as and provide them with the necessary treatment. The utilization of imaging data including chest x-rays and computed tomography (CT) was proven that is would be beneficial for quickly diagnosing COVID-19. Since Computerized Tomography provides a huge number of images, recognizing these visual traits would be difficult and take enormous amounts of time for radiologists so automated diagnosis technologies including deep learning (DL) models are recently for COVID19 screening in CT scans. This review paper presents different researches which used deep learning approaches including various models of convolutional neural networks (CNN) used in image classification tasks well, and large training, like ResNet, VGG, AlexNet, LeNet, GoogleNet, and others for COVID-19 diagnosing and severity assessments using chest CT images. As a result, automated COVID-19 analysis on CT images is essential to save medical personnel and essential time for disease prevention

The world is now experiencing a pneumonia outbreak caused by a novel coronavirus. The huge volume of medical literature on coronavirus has useful information that may assist medical research communities in addressing specific challenges. Health care professionals may improve their policies by quickly reviewing and getting specific data regarding coronavirus from various published research and the larger struggle against infectious disease. It has developed a technique for extracting actionable knowledge that automatically gathers pertinent data from sections and paragraphs related to a particular topic. There are continuous efforts to construct intelligent systems capable of automatically extracting useful information from many unstructured texts. In this paper, the comparison between many papers based on IoT and the knowledge base for solving the problem of Covid-19 has been conducted.

Pattern recognition is a crucial part of machine learning that has recently piqued scientists' interest. The feature selection method utilized has an impact on the dataset's correctness and learning and training duration. Learning speed, comprehension and execution ease, and properly chosen features influence all high-quality outcomes. The two feature selection methods, relief-F and chi-square, are compared in this research. Each technique assesses and ranks attributes based on distinct criteria. Six of the most important features with the highest ranking have been chosen. The six features are utilized to compare the performance accuracy ratios of the four classifiers: k-nearest neighbor (KNN), naive Bayes (NB), multilayer perceptron (MLP), and random forests (RF) in terms of expression recognition. The final goal of the proposed strategy is to employ the least number of features from both feature selection methods to distinguish the four classifiers' accuracy performance. The proposed approach was trained and tested using the CK+ facial expression recognition dataset. According to the findings of the experiment, RF is the best accurate classifier on chi-square feature selection, with an accuracy of 94.23%. According to a dataset utilized in this study, the relief-F feature selection approach had the best classifier, KNN, with an accuracy of 94.93%.

The concept of storing historical data for retrieving them when needed has been conceived, and the idea was primitive to build repositories for historical data to store these data, despite the use of a specific technique for recovering these data from various storage modes. The data warehouse is the most reliable and widely used technology for scheduling, forecasting, and managing corporations. Also, it is concerned with the data storage facility that extensive collection of data. Data warehouses are called ancient modern techniques; since the early days of relational databases, the critical component of decision support, increasing focus for the database industry. Many commercial products and services are now available, and almost all of the primary database management system vendors provide them. When opposed to conventional online transaction processing applications, decision support puts slightly different demands on database technology. This paper analyzes the performance of the data warehouse architectures by studying and comparing many research works in this field. The study involves extracting, transforming, and loading the data from different recourses and the important characteristic of the architecture types. Furthermore, the tools and application service techniques used to build data warehouse architecture.

Cloud computing, data mining, and big online data are discussed in this paper as hybridization possibilities. The method of analyzing and visualizing vast volumes of data is known as the visualization of data mining. The effect of computing conventions and algorithms on detailed storage and data communication requirements has been studied. When researching these approaches to data storage in big data, the data analytical viewpoint is often explored. These terminology and aspects have been used to address methodological development as well as problem statements. This will assist in the investigation of computational capacity as well as new knowledge in this area. The patterns of using big data were compared in many articles. In this paper, we research Big Data Mining Approaches in Cloud Systems and address cloud-compatible problems and computing techniques to promote Big Data Mining in Cloud Systems.

Facial expression recognition could be implemented via analyzing facial changes in actions that form the facial expression. This paper provides a comparative approach for facial expression recognition, based on Chi-Square feature selection method. This method is used to determine the most distinguished features of face images (six features) using six classification algorithms which are (K- Nearest Neighbor, Decision tree (J48), Radial Base Function, Support Vector Machine, Multi-Layer Perceptron and Random Forest). These classifiers are used for the mission of expression recognition. The ultimate purpose of the provided method is to distinguish the most influential and accurate performance algorithm based on the smallest number of features. This is done via analyzing and appraising the classifiers’ performance. The provided approach has been applied on CK+ dataset. The experimental results show that Random Forest is proven to be the most accurate classifier with the ratio of 94.23%.

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century has been no different. Today there is the unprecedented concentration on the quality of health services. A strong health-care system distributes quality services to all people, where and whenever they need it. Vaccination is one of the most important health-care services for the prevention and control of immuno-preventable diseases and, therefore it is essential to keep the vaccine card up-to-date and accessible to enable its benefits. This paper suggests a solution to remind the child’s parents to make an appointment for the next upcoming vaccination. The proposed system sends reminder short messages service (SMS) and an email message to remind the child's parents based on the vaccine schedule recommended in the Kurdistan region. The proposed system aimed at improving completion of the infant primary immunization by Automatic vaccine updates, reminders for free, and track vaccines for multiple babies from the health-care clinical center.

Every era introduces different challenges to healthcare organizations, and the start of the twenty-first century has been no different. Today there is the unprecedented concentration on the quality of health services. A strong health-care system distributes quality services to all people, where and whenever they need it. Vaccination is one of the most important health-care services for the prevention and control of immuno-preventable diseases and, therefore it is essential to keep the vaccine card up-to-date and accessible to enable its benefits. This paper suggests a solution to remind the child’s parents to make an appointment for the next upcoming vaccination. The proposed system sends reminder short messages service (SMS) and an email message to remind the child's parents based on the vaccine schedule recommended in the Kurdistan region. The proposed system aimed at improving completion of the infant primary immunization by Automatic vaccine updates, reminders for free, and track vaccines for multiple babies from the health-care clinical center.

Networks of computers are everywhere, and it implemented in all sectors, including an IT, industrial, managerial sector, and need to be accessed remotely. Remote administration offers access to a remote device. As more departments of Information Technology centralize and reorganize to keep costs down, many remote sites are left with no IT support on-site. Remote computer administration is increasingly common due to the major cost advantages; many activities can be completed, and not needs to be personally accessed by the administrator. In this paper, the remotecontrol administration system has designed and implemented based on client/ server architecture. The main goal of the proposed system allows clients to request help from remote servers over the Network.

One of the most important subject which many researchers depending on it by applying many algorithms and methods is Cloud Computing. Some of these methods were used to enhance performance, speed, and advantage of task level parallelism and some of these methods used to deal with big data and scheduling. Many others decrease the computation’s quantity in the process of implementation; specially decrease the space of the memory. Parallel data processing is one of the common applications of infrastructure, which is classified as a service in cloud computing. The purpose of this paper is to review parallel processing in cloud. However, the results and methods are inconsistent; therefore, the scheduling concepts give easy method to use the resources and process the data in parallel and decreasing the overall implementation time of processing algorithms. Overall, this review give us and open new doors for using the suitable technique in parallel data processing filed. As a result our work show according to many factors which strategies is better.