Electronic Authentication (or “e-Authentication”) is one of the key topics in the field of Cybersecurity. It is the electronic verification process for identifying an entity. As person using a mobile/computer, a mobile/computer itself or a mobile/computer program. Authentication is an ensuring way to guarantee that the user who attempts to access the system is in fact the user who is authorized to do so, which also performs secret pathway. Currently, the use of e-authentication application is increased. Security is an important issue for handling such services. Such as current system provide security card-based facility to au- thenticate user, but this is not much more secure and will not be available for any time or situation. The aim of our system is to provide user secure login systems more compliable and reliable. This project will be useful to eliminate many problems inherent in traditional login techniques in E-government.
10/11/2022
There is no doubt that money is the most important thing that a person possesses at the present time. That is why a lot of people look for saving methods and products that will prevent them from spending or losing their money. One of these methods is called Rotating saving and credit association (RoSCA), which is a form of peer-to-peer lending known as Jameya in Saudi Arabia and middle east. It is old and widespread saving approach where a group of people act together as financial institution, people usually choose RoSCA instead of saving product offered by the bank because banks either require high credit score or have high minimum fund. Of course, traditional RoSCA is not without its flaws, not only that a lot of these saving group act in informal way, but it also fully depends on trust and other members’ commitment, and offer so little of payment options. By creating this Application, we seek to digitalize and formal- ize RoSCA groups, help in increasing the household saving rate, increase the number of saving products in the market, and close the gender and rural saving-gap. Which is parallel with the vision of 2030 and the objectives of the financial sector development program (FSDP).
10/11/2022
Recently, security has become a critical need for any home or building. The Internet of Things (IoT) provides much convenience to people’s homes. In the past, most doors opened using traditional methods, such as keys, access cards, or passwords. However, it led to troubling accidents and problems such as losing the access card or the key. This project developed a facial recognition system based on (IoT) technologies to overcome these problems. The proposed study aims to build a real-time face recognition model using Raspberry Pi 4. The system achieves heightened security by sending a notification to the user’s mobile when an unauthorized person tries to access the building.
09/11/2022
Universities have been making classroom timetables for decades. More researchers are becoming interested in making the process of generating timetables easier and less troubling. We built a system to automate the process of distributing sections to class- rooms using the programming language Python, and created a graphical user interface to make it easier to work with the system. The system can generate the classroom schedule very quickly and in two different ways, either by days, or by classrooms, informing the administrator about the sections that have not been been distributed, and taking into account the intersection of lecture times.
09/11/2022
Saudi Arabia has been suffering from high temperatures produced by the country’s envi- ronment, which has resulted in increased electricity demand, notably in air conditioning, due to the country’s dry desert nature, thereby affecting the residents’ daily lives. This research aims to apply integrated building automation systems to control lighting, cooling, heating and ventilation in the Kingdom of Saudi Arabia and provide remote control and real-time monitor- ing of building energy consumption. This study suggests a solution in which sensors evaluate indoor air and thermal comfort, such as temperature, humidity, PMV, and PPD, while another sensor detects human presence. Based on this, the AC will switch on or off . The smartphone app will then alert the residents of the house.
11/10/2022
At the present time, with the expansion of the fields of learning and knowledge, the need to raise the educational level and develop the skills and capabilities of both employees and students through training sessions has been increased .Therefore, it may face difficulties for the users to find the suitable sessions, and to overcome these difficulties this study works to save time and effort of The user by using the recommendations system and linking it to training sessions. The recommendations system for this study will use the Content-Based filtering technique by cosine similarity to suggest the best appropriate courses based on the user’s skills. This system will suggest personals recommendation for training sessions based on the users preferences.To achieve that the training sessions of Doroob platform has been used to implement the system of this study and sequence matrix to test the accuracy, and the proposed system can provide recommendations with a precision 0.94 which provides the user with highly accurate suggestions.
14/07/2022
Air pollution has become one of the largest issues that cause harmful human and environ- mental health due to harmful chemicals and other toxic materials which result from external interactions such as factory and vehicle smoke, or from internal interactions such as cooking smoke or detergents. Indoor air quality monitoring is important for the health and comfort of people who spend most of their time indoors. Most people are unaware that indoor air quality could be poor, until studies have shown that indoor pollutant levels can be two to five times greater than outdoor levels. There are some existing systems that monitor indoor air quality that only detect one parameter for monitoring of air pollution, some of them have a high cost, it doesn’t provide a monitoring process in real-time, and not easy to use because it is intended for professionals. In recent years, mobile technology, specially the internet of things has had a positive impact on how we manage our health. The Internet of things (IoT) is a technique that connects devices with the internet. In this project, an Indoor Air Quality Monitoring System (IAQM) is proposed based on IoT technology. The concept of the proposed system is to deploy sensors that measure air quality parameters which are connected to ESP32 microcontroller in a restaurant environment. According to our knowledge, this system is the first system in the kingdom of Saudi Arabia that may help competent authorities to monitor the indoor air quality in restaurant environments remotely. The cloud was used to process and store the collected data, and display it as a numerical and graphical representation on mobile applications. The proposed system is built in four phases. In the first phase, the requirements of the IAQM system were analyzed by visiting the restaurants and knowing the needs of competent authorities. In the second phase, the monitoring device and user interface are designed. In the third phase, the system was implemented in a restaurant’s environment in real-time and tested under certain conditions. In the final phase, the system was verified based on the required requirements and tested through a number of standards that were relied on in testing cases inside the restau- rant’s kitchen. Through this system, competent authorities can easily monitor the air quality in restaurants by using the application.
05/07/2022
Due to advances in machine learning algorithms, they can help translate descriptions into visual elements with the advent of generative adversarial networks. Image generation from na- ture has become one of the primary applications of modern conditional generative models. It is a flexible and intuitive way to create a conditional image with significant advances in recent years with regard to realism, visual, Diversity, and Semantic Alignment. However, the field still faces many challenges that require more research efforts such as enabling the generation of high-resolution images with multiple objects, and developing appropriate and reliable evalua- tion metrics that correlate with human judgment. A text-to-image generation (T2I) model aims to generate photo-realistic images which are semantically consistent with the text descriptions. Due to the advancement in Machine Learning Algorithms, they can help in translating the descriptions to visuals. Generative Adversarial Networks (also known as GANs) can be used to create a set of images from the text which are a form of descriptions. Generative models algorithms come under unsupervised machine learning. Based on the recent advances in gen- erative adversarial networks (GANs), existing T2I models have made great progress. However, they have some limitations. The main target of this project is to address these limitations to enhance the text-to-image generation models to enhance location services. Text-to-image synthesis is proposed in this study using the Attentional Generative Ad- versarial Network (AttnGAN). In order to produce high-quality photos utilizing a multi-step approach, we build an attentional generating network called AttnGAN. The fine-grained image- text matching loss needed to train the AttnGAN’s generator is computed using a multimodal attentional multimodal similarity model that we provide. With an inception score of 4.81 on the PatternNet dataset, our AttnGAN model achieves an impressive R-precision value of 70.61 percent. Because the PatternNet dataset is entirely comprised of photographs, we’ve added verbal descriptions to each one to make it a text-based dataset instead. Many experiments have shown that the AttnGAN’s proposed attention procedures, which are critical for text-to-image production in complex circumstances, are effective.
30/05/2022
Alzheimer’s Disease (AD) is the most common type of dementia. Alzheimer’s Disease (AD) affects an estimated 1 in 10 people over age 65. Symptoms of Alzheimer’s Disease (AD) usually start slowly and worsen over time. It is an irreversible and progressive brain disorder that slowly destroys memory and thinking skills and eventually the ability to perform simple tasks in daily life. Early detection of Alzheimer’s is extremely important because the treatment could be most effective if the disease can be diagnosed in its early stage. With no known cures, leaving specialists in a race for time on behalf of their patients, there is a pressing need to find biomarkers (short for ”biological markers”) that accurately assess and predict disease progres- sion in symptomless patients. Computer-aided systems to support early diagnosis of the disease provide researchers with a powerful and critical tool for indicating medical intervention at the earliest and most effective stage of the progression of the disease. In this project, Magnetic Resonance Imaging (MRI) brain scan images of Alzheimer’s Disease (AD) patients and control (healthy patients) will investigate. A total of 246 biomarkers were identified, including 123 from the left and 123 from the right brain region. The focus of this project is using various machine learning algorithms with feature selection techniques to classify Magnetic Resonance Imaging (MRI) brain scan images for early Alzheimer’s Disease (AD) detection.
20/05/2022
Covid-19 disease has become a global disease that the World Health Organization (WHO) learned about for the first time on December 31, 2019, and this disease has led to many health, social and economic problems internationally and it has changed lot in the concept of living and cleanliness constantly. In order of to maintain safety, there is some simple precautions must be taken, such as avoiding crowding and maintaining distance, which is a very important concern, and it should not be tolerated, which is to maintain distances between people in various social media environments. That is why this project (distance application) aims at facilitating and alerting people to the most crowded places also measuring the distance to be taken, thus would help to reviving our conscience towards laxity in commitment in distances between people and reduce the rates of transmission of viral corona infection among humans.
14/05/2022
With the advancement of Digital Science and the development of technology around the world many people use smart devices, many of them have become faced with different technical problems, so considering the topic of smart devices and their problems and how to solve them is very important in our time and the future. Therefore, the proposed system will be built to serve users of smart devices in solving technical problems facing them in an easy and interactive way between users, where the user poses his problem and others respond to it by solutions and exchange experiences among them, android studio will be used to build this system, firebase will also be used to store data.
14/05/2022
In this technological era, social media has become one of main sources of information, people go to social media to get or even post real-time information about any on-going situation, the same goes for disaster events. This widespread usage of social media platforms is considered to be highly beneficial for emergency teams and decision makers as time is a very critical aspect during disasters and relying on traditional sources of information, like television stations, could take hours. However, information cannot be taken from social media posts directly, loads of posts are posted on social media during disasters, and a large percentage of these posts include spams, replicated posts and other posts that are not informative. Therefore, the aim of our project is to use Deep Learning (DL) and Machine Learning (ML) techniques to identify infor- mative disaster-related posts on social media streams.
13/05/2022