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Implementation of Effective Law for Load Balancing
Sabah Mohammed,Edward A. Pingoy 사단법인 인문사회과학기술융합학회 2011 예술인문사회융합멀티미디어논문지 Vol.1 No.2
In this paper, we discuss about the implementation of an effective law for load balancing Content Delivery Networks (CDN). In this method we will only have the hardware implementation costs that will be giving the output of the program without software implementations costs. So for these requirements, we introduce the hardware driven flow slicing along with software driven load balancer which also reduce implementation costs when compared to the existing system method. So here, we get the effective output along with that we will be having our required method for better performance of the system.
Niki Shakeri,Jinan Fiaidhi,Sabah Mohammed,Tia-hoon Kim 보안공학연구지원센터 2014 International Journal of u- and e- Service, Scienc Vol.7 No.5
Recommender systems are now popular both commercially and in the research community, where many approaches have been suggested for providing recommendations. However, evaluating the affectivity of recommender systems is a challenging problem and most of the approaches used for evaluation are based on using some sort of a dataset. This paper describes a method for measuring the accuracy of a collaborative filtering based recommender systems called “User-Advertisement Simulation” that utilizes a simulation approach that creates artificial users and advertisements of a virtual market, then measures accuracy of the products’ ranking based on the user’s profile.
An overview of nanomaterials for industrial wastewater treatment
Sabah Mohamed Abdelbasir,Ahmed Esmail Shalan 한국화학공학회 2019 Korean Journal of Chemical Engineering Vol.36 No.8
Industrial wastewater is a universal environmental issue. Numerous organic pollutants, heavy metals, and non-disintegrating materials are present at extreme concentrations. Presently, removing these pollutants from industrial wastewater in an effective way has become a momentous issue. Efficient purification procedures are needed to remove those pollutants before disposal. In this direction, wastewater treatment has been one of the nanomaterial applications. Additionally, nanomaterials are innovationally effective for purifying water by using low-budget nanoadsorbents and nanofiltration. This review article highlights the use of nanomaterials for the removal of different polluting materials from industrial wastewater with a special focus on metal and metal oxide nanomaterials (NMs), carbonbased nanomaterials (CNMs) and nanofiber/nanocomposite membranes. The goal is to offer a recent overview and references in the area of emergent nanomaterials used for removing toxic pollutants from real industrial wastewater for researchers and industrializers.
Talib Mohammed Albayati,Ghanim Magbol Alwan,Omar Sabah Mahdy 한국화학공학회 2017 Korean Journal of Chemical Engineering Vol.34 No.1
The Magnetic nanoporous material Fe/MCM-41 was prepared, and its physical characterization studied, to determine the effect of its properties on separation efficiency of methyl orange (MO) from wastewater by adsorption process. The experimental results were analyzed for both adsorbent mesoporous material samples, MCM-41 and magnetic Fe/MCM-41, in order to select the best operating conditions for the different studied parameters, which are: constant temperature (20 oC), pH: (2) adsorbent dosage (0.03 gm), contact time (10minute) and concentrations (30mg/L). The results demonstrate that the adsorption processes can be well fitted by the Langmuir isotherm model for pure MCM-41, with a correlation coefficient of (0.999), and fitted by the Freundlich isotherm model for magnetic Fe/ MCM-41, with a correlation coefficient of (0.994). The adsorption kinetics of MO on to MCM-41 and Fe/MCM-41 are well described by a pseudo-second-order kinetic model.
ExerAdventure: A Mobile 2D Platformer Game to Encourage Fitness
Navdeep Singh,Sabah Mohammed 한국컴퓨터게임학회 2021 한국컴퓨터게임학회논문지 Vol.34 No.2
Physical activity and exercise is an important step in the journey of a healthy lifestyle. It reduces the risk of developing many diseases like diabetes, cardiovascular diseases, and even cancer. In today’s world, we spend most of our time sitting in front of a computer or mobile phones for work and entertainment. This is one of the leading factors in a lack of physical activities and thus a decrease in the fitness level of many individuals. In this paper, we design a mobile 2D platformer game where progress inside the game world is dependent on physical activity in the real world. The game has two modes namely adventure and story mode. This design can motivate people to lead an active and healthy lifestyle. The physical activity monitoring is done solely through the pedometer of a smartphone held by the user while playing the game. The number of steps walked by the user determines the number of revives inside the adventure mode of the game. There are three levels in the story mode which are unlocked by walking more than a certain number of steps in the real world.
Shreya Reddy,Sabah Mohammed 한국컴퓨터게임학회 2021 한국컴퓨터게임학회논문지 Vol.34 No.2
Mental health problems leading to depression have become a critical concern due to the growing engagement of people on social media platforms. Several past approaches have been implemented by analyzing the pattern and behaviour of the posts by users on social networking sites. This research study proposed a system for predicting users who may be depressed, based on the characteristics of users who is already affected. A combination of both the tweet-level and the user-level architecture was used to generate a more robust and reliable system where semantic embeddings trained from advanced neural networks were adopted under the tweet-level. SVM with Word2Vec and TF-IDF has been used and yielded an accuracy of 98.14% and recall of 95.63%.
A Probabilistic Visual Question Answering Model Based VQA
Manva Trivedi,Sabah Mohammed (사)한국컴퓨터게임학회 2022 한국컴퓨터게임학회논문지 Vol.35 No.3
Visual data is present everywhere and natural language is a way of communication understandable to humans. Visual Question Answering (VQA) is a system which takes image as an input and a question about the image and generates a natural language answer using complex reasoning. Thus, a VQA needs detailed understanding of the image and complex reason to predict the answer. Given its multimodal structure and possible real-world implementations, VQA is a challenge of critical importance for artificial intelligence. The architectures and hyperparameters used in deep neural networks for VQA have a big impact on their results. This project introduces a pretrained model (VGGNet) to extract image features and Word2Vec to embed the words and LSTM to get word features from the question and after combining the results will predict the answer having highest probability.