Although the number of stomach tumor patients reduced obviously during last decades in western countries, but this illness is still one of the main causes of death in developing countries. The aim of this research is to detect the area of a tumor in a stomach images based on fuzzy clustering. The proposed methodology consists of three stages. The stomach images are divided into four quarters and then features elicited from each quarter in the first stage by utilizing seven moments invariant. Fuzzy C-Mean clustering (FCM) was employed in the second stage for each quarter to collect the features of each quarter into clusters. Manhattan distance was calculated in the third stage among all clusters' centers in all quarters to disclosure of the quarter that contains a tumor based on the centroid value of the cluster in this quarter, which is far from the centers of the remaining quarters. From the calculations conducted on several images' quarters, the experimental outcomes show that the centroid value of the cluster in each quarter was greater than 0.9 if this quarter did not contain a tumor while the value of the centroid value for the cluster containing a tumor was less than 0.4.For examples, in a quarter no.1 for STOMACH_1 medical image, the centroid value of the cluster was 0.973 while the value of the cluster centroid in quarter no.3 was 0.280. For this reason the tumor area was found in quarter no.(3) of the medical image STOMACH_1. Also, the centroid value of the cluster in a quarter no.2 was 0.948 for STOMACH_2 while, the value of the cluster centroid in quarter no.4 was 0.397. For this reason the tumor area was found in a quarter no.4 of the medical image STOMACH_2.

Cancer represents a kind of disease that is widespread throughout the world. Actually, there are several kinds of cancer. However, lung cancer represents the most prevalent cancer form and can lead to death with late healthcare. Therefore, it is essential to initialize therapy via diagnosing lung cancer for decreasing the death chance. Classification is one of the fundamental issues in the knowledge discovery fields and scientific decisions. There are many types of techniques used for constructing classifiers and cancer diagnosis. Recently, deep learning becomes a powerful and popular classification technique for many areas of medical data diagnosis in the healthcare systems. In this paper, an effective and accurate deep neural network (DNN) based lung cancer diagnosis implemented in the healthcare system has been proposed which includes three main phases; pre-processing, generating strong rules, and classification. The input data are pre-processed in the first phase. Because these data are entered from databases, so there are missing data that should be replaced with zero values. Then, data are normalized for speeding up the learning phase. After that, the class association rule is used to enhance the classification performance by generating frequent patterns inducible from the dataset which include features that are significant to the class attribute. Finally, DNN is utilized in the process of classification for obtaining a sample diagnosis estimate. DNN based diagnosis system was tested and evaluated on the lung cancer dataset which has 25 attributes and 1000 instances. The obtained results demonstrated that the proposed system achieved a high performance compared to other existing lung cancer diagnosis systems with 95% accuracy, 97% specificity, and 95% sensitivity.

Geographic Information Systems (GIS) are obtaining a significant role in handling strategic applications in which data are organized as records of multiple layers in a database. Furthermore, GIS provide multi-functions like data collection, analysis, and presentation. Geographic information systems have assured their competence in diverse fields of study via handling various problems for numerous applications. However, handling a large volume of data in the GIS remains an important issue. The biggest obstacle is designing a spatial decision-making framework focused on GIS that manages a broad range of specific data to achieve the right performance. It is very useful to support decision-makers by providing GIS-based decision support systems that significantly reduce the cost involved when moving between two locations. Therefore, in this paper, an advanced decision support system is built for identifying the best route between two locations according to various criteria such as distance, travel time, the safety of the road, and other features. The proposed model includes several stages; Google Maps downloading, preprocessing, integrating with the database, and identifying the best route by utilizing advanced algorithms of artificial intelligence. Furthermore, the Open Street Maps (OSM) database is utilized in this model and implemented using the Quantum Geographic Information Systems (QGIS) platform. One of the main merits of this model is to be faster by removing the influence of non-processed data like null values and unlinked roads on offline google maps levels. The outcomes of this proposed model display the best route which connects the source with the destination, and a table including the entire information for this route.

In the Past a Few Years, smart devices like smart phones and tablets have radically change in many aspect, started from Entertainment to Shopping services to transfer Money and Banking, the next is Health Services. With the development in information technology And the big development in cloud computing Here smart phones have entered heavily in all aspects of health care. Now with the revolution of the smart devices (smart phones or tablets) and it's applications, there is many applications and tools are available started from attachments that allow to diagnose an infections and Now remotely and continuously monitor each heartbeat , blood pressure readings, the rate and depth of breathing, body temperature, oxygen concentration in the blood, glucose, brain waves, activity, mood, so the end result will be can reduce using of doctor ,also reduce the cost , and give us speed up and give power to patients , so make it possible for Patient to use portable devices (smart phones or tablet) to access their medical information, and achieve the goal to put information technology to work in health care and make the integration of health information technology into primary care .So the using information technology give us the good solution that won't replace physicians. Health Information technology give the providers of health care to give better manage patient care. By making the health information are available electronically anytime and anywhere is needed, Health Information technology can help us to improve the quality Of Health , so can decrease the cost. Now, after all these advantages should shed light on the side of personal privacy And Hacking side that must have been tested all application and tools, All of these tools must be accurate and needs to be tested. So that must provide the highest level of security and privacy for the patients. After that. The application does not arrive the goal with 100% percent, according to the limitation that mentioned later.