In Saudi Arabia, a big segment of students is using government bus transportation; there are many students who have different problems with the current system of the bus transportation. So, this is a very important topic to look at, study and try to solve the problems related to it. Also there is a lack of official means of communication between the students / families, bus driver or the bus management. Every parent wants an easy, comfortable and safe transport system for their school/ university going ward. The proposed system can provide parents with the timely monitoring or tracking transport system, for security of their children or ward. The system can provide shortest path for the driver, making an attendance and sending notification to students when the bus near to them, also sending notification to parents when their ward pick and drop the bus. The students or parents can get the contact information of the driver and bus management. The proposed system will shift from manual to digital, more secure transport system. Flutter or Android will be used to build this system. Firebase will be used to store the data, Google Map and GPS will be used for routing or tracking purposes.
15/11/2020
In Qassim University, the majority of activities are automatic and carried out using computer applications. This explains the efficiency of its information system and its permanent maintenance. Often, we face various technical problems at equipment levels, services, etc. The Incident Management task is still performed manually and using less efficient methods with a traditional communication infrastructure. Moreover, the lack of an automatic process generates various problems such as loss of time, overload of work that is done by a single agent, difficult communication between agents, etc. To solve this problem, it is necessary to propose a tool within the university to have regular monitoring of computer equipment. Our project aims to design and develop an Incident Management application using Knowledge base to facilitate and optimize the quality and performance of the services provided by the Technical Support department by using knowledgebase. The use of knowledge base allows us to save up the time to solve by seeing frequent problems and solutions from the knowledgebase. At the end of the project, we came up to build CoC_Helpdesk website for two types of users. Helpdesk: a helpdesk will receive an incident from a customer where he will enter the information relating to this incident. If the solution found it in the knowledgebase, the helpdesk will give the solution to claim and close the incident. Otherwise, if the solution not effective for the customer he will send a request to resolve this incident to the technical. Technical: the technician will receive notification of a request from helpdesk to resolve the incident, he will see details of the problem to be resolved he will either solve it with his expertise or search for a solution in any way and save it in the knowledge base to make it available to the helpdesk.
15/11/2020
The sign language is principal way to communicate with deafs people which is often far superior to their ability to read and write, it is a non-phonetic language that deaf people use to communicate with each other. There are many studies of different sign languages for example American, Indian, Bengali, Chinese and others, but each country has its own sign language. We did extensive research about Arabic studies for sign lan- guage and we found many problems about Arabic studies and there are no studies about Saudi Sign Language (SSL). This research presents Saudi Sign Language Recognition System (SSLRS) by using the Saudi dictionary issued by the Saudi Association for the Deaf and Hard of Hearing in 2018. This research is not dependent on use any gloves or visual markings to accomplish the recognition job. As an alternative, it deals with images of bare hands which allows the user to interact with the system in a natural way. We build dataset of 27,301 for 40 classes then develop model to recognition was accuracy 97.69% for training data, accuracy 99.47% for validation data and 88.67% for recognition rate. Therefore, we develop two systems prototype one application for mobile app another desktop application.
15/11/2020
Blood is essential to life also, the number of blood cells plays a significant role in observing an individual’s health status. Any disorder refers to various diseases, especially for white blood cells WBC which is the basic part of the immune system. Classification WBC subtypes is very useful for diagnosing disease, infection etc. Deep learning technologies can reduce the limita- tions in Medical settings and for the time it takes in classification tasks. Also, a lot of clinic experience is required for a doctor to detect the amount of WBC in human blood and classify it. The basic idea of the proposed study is to employ CNN models for classifying WBC into four subtypes (Eosinophil, Lymphocyte, Monocyte, Neutrophil). In this study, the transfer learning was employed for two techniques as follow: using VGG-16 and Mobile-net models for feature extraction and train the SVM and QDA classifiers with the extracted features. Also,we used VGG-16 and Mobile-net for feature extraction and classification using fine-tuning technique. For the first technique, the best performance in the classification of white blood cells is given by the hybrid model VGG-16 with SVM with the accuracy of 98.44%. For the second technique, the best result of 99.81% accuracy obtained from VGG-16 fine-tuned model.The experimental results showed that the use of the convolutional neural network models contributed to improv- ing the classification success of white blood cell subtypes.
15/09/2020
Because of the large number of social media interactions and messages and the huge mail they have today, we see the spam emails appear to be very high for most users The project aims to provide a mechanism to analyze spam messages that reach the university mail to extract the most important information and characteristics of those messages that will help in devising ways to reduce such messages and increase the level of awareness among university employees and staff employees.
15/08/2020
Children with down syndrome suffer from mental insufficiency, which affects their normal exercise of life and education. Children with typical development can interact with technology fairly easy, but when it comes to children with down syndrome it is not very simple. Researches show that early intervention of children with disabilities is crucial to improve the education process. The educational game is one of the techniques that benefit children with down syn- drome in their education. The lack of educational games in the Arabic language that focuses on children with down syndrome has been noticed. Therefore, this project aimed to develop the counting skill for children with down syndrome. The design implemented while taking into consideration the design code that suits and fulfill the needs of children with down syndrome to improve their count skill. The Arabic language was used inside the game visually and audibly, and a reward was given that aims to encourage the child during play.
15/08/2020
Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most harm- ful insects for palm trees around the globe during the last two decades. The identification is difficult, because great experience and knowledge is needed for accurate identification of this insects. To automate the process of identification several attempts have been made by re- searchers to develop computer vision based algorithm to accurately identify the RPW insects. The new intelligent system is particularly useful for lay people who have no professional knowl- edge to recognize these insect species. The basic idea of the proposed study is to develop a method that can use computer vision image classification process to identify Red Palm Weevil and distinguish it from other insects like ants found in palm tree habitant. In this study, we focused on developing an algorithm that will help us to identify red palm weevil (RPW). The proposed method incorporates computer vision image classification techniques based on image enhancement and segmentation using Otsu’s algorithm. the feature extraction techniques used to classify the RPW based on color and shape and Neural Network (NN). Experimental test results for 913 dataset images, show 95 % accuracy of classification and convergence of the trained Neural Network (NN) using MATLAB simulation. GUI (Graphical User Interfacing) was developed to see real time image classification performance of the system. The system classified the RPW from other insects and gave us better classification performance.
15/08/2020
Android platform takes 85% of the global OS market share, where it runs multiple types of devices, smart phones, wearables and all other different shapes. As a result, android be- comes one of the top cyber criminals targets to spread their malwares. There are traditional approaches used to detect malwares through code and behavior analysis, but recently new alter- native approaches have been introduced. In this project, we use one of the recent approaches, i.e. Image-based analysis and the machine learning techniques. Also, since android applications (APK) consists of multiple file, this project aims to provide a comparative study on using a single or more of these files in order to determine which file has an impact in characterizing a malware. The process of detecting a malicious APK starts with visualizing one or more of the APK’s files into gray-scale image, then extracting the image features using GIST descriptor, and finally training using machine learning Random Forrest classifier to detect the malicious APK.
15/07/2020
Today, agriculture areas in Saudi Arabia, where the water level decreasing resistance in farms growing weeds. Currently the farmers using manually watering for all kind of weeds even if there is no need of water. This is a slow and laborious process when done manually which has led to automated precision weed control as a viable option for such weed watering. Therefore, automatic system is require to decide the present of different weeds. Automatic weed classifi- cation required the ability to identify the accurate class of weed for watering in the given field. That identify the exactly need of a region, this article aims to describe the development of real time automatic water sprayer system for selective weed. In order to improve the agricultural sprayer process, and to make it possible for one sprayer to cover the whole area automatically, the control system for Automatic agricultural irrigation system can recognize using wavelet transform. The system can detect areas where there is no need, little or more water required through the connected camera with a high accuracy. A MATLAB program is developed as software based interface between the system and camera. The software based results will be implement on hardware for real time weed classification.
15/07/2020
Blood is very important for keeping humans alive. One of the biggest challenges facing blood banks is the difficulty in finding blood donors. So, blood bank systems should be effective in finding potential donors. In this project, we have proposed a knowledge-based system that in- creases the chance to find possible blood donors. Random Forest is the Machine Learning (ML) algorithm that will be used to develop a classifier to categorize people into two groups: peo- ple who are more likely to donate blood and people who are less likely to donate blood. The classification will be based on factors like people’s values, and their culture. These factors are influence people’s behaviors so, it will help knowing donors from non-donors. Our proposed system aims to improve the efficiency of blood bank systems and reduce costs by contacting potential donors rather than contacting someone who is not willing to donate.
15/07/2020
In the present era, due to technological advances, the problem of energy consumption has become one of the most important problems for its environmental and economic impact. Energy consumption is affected by different factors. Educational buildings are one of the highest energy consuming institutions. Therefore, one has to direct the individual and society to reach the ideal usage of energy. One of the possible methods to do that is to prediction energy consumption. This study proposes an energy consumption prediction model using deep learning algorithm. To evaluate its performance, College of Computer (CoC) at Qassim University was selected to analyze the elements in the college that affect high energy consumption and collect their energy consumption data to build the model. Data were collected from the Saudi Electricity Company of daily for 13 years. This research applied Long short term memory (LSTM) technique for medium-term prediction of energy consumption. The performance of the proposed model has been measured by evaluation metrics to show performance and achieved low Root mean square error (RMSE) which means higher accuracy of the model compared to relative studies. Consequently, this research provides a recommendation for educational organizations to reach optimal energy consumption.
15/07/2020
Pneumonia is a disease that affects the lungs .It causes 15% of all deaths in children under five years old. Also, it is difficult to diagnose and require an expert radiologist of chest X-ray to avoid the misdiagnosis. Chest X-ray is the most commonly and cheaply way that is used to detect pneumonia. The Lack of experts in poor countries causes a long wait for diagnosis Pneu- monia, which increase the mortality. The project aims to use Convolutional Neural Networks (CNN) to automatically diagnose pneumonia by reading chest x-rays and classify the result to normal case or Pneumonia case, and this will help to quickly and easily diagnose the disease. The proposed work is to improve the accuracy of Vgg-16 model for detecting pneumonia by applying a shuffle technique on our dataset before train the model.
15/06/2020