American Journal of Computer Sciences and Applications



Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Models

Research Article of American Journal of Computer Sciences and Applications Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Models Xuan Hau Nguyena, Yongjian HUb, Khan Gohar Hayatc, Van Thinh Led, Tu D. Truonge a,cResearch Centre of Multimedia Information Security Detection and Intelligent Processing, School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, P.R.China. a,dFaculty of Information Technology Central Industrial and Commercial College, Phu Yen 620000, VietNam, eFaculty of Information Technology Ton Duc Thang University, Ho Chi Minh 700000, VietNam In the era of information explosion today, videos are easily captured and made viral in a short time and tampering videos have become easier due to editing software. So, the authenticity of videos become very essential. Video inter-frame forgeries are the most common type of video forgery methods. Until now some algorithms have been suggested for detecting inter-frame forgeries based on handicraft features but the accuracy and speed in processing of suggested algorithms are still challenging. In this paper, we are going to put forward a video forgeries detection method for detecting video inter-frame forgeries based on convolutional neural network (CNN) models by retraining the available CNN models trained on ImageNet dataset. The proposed methods based on CNN models which have been retrained to exploit spatial-temporal relationships in a video to robustly detect inter-frame forgeries. And in order to eliminate the errors due by the network, we have proposed a confidence score instead of the raw output score from networks. Through the results of experiments, we have proven that the proposed method has significantly higher efficiency and accuracy than recent methods. Keywords: Video forensic, video forgery detection, video inter-frame forgery detection, convolutional neural network, video authenticity, passive forensic ...

Hierarchical Model and Characterize Identified E-Commerce Trust to Generate Customer Satisfaction Data

Research Article of American Journal of Computer Sciences and Applications Hierarchical Model and Characterize Identified E-Commerce Trust to Generate Customer Satisfaction Data Solanke Ilesanmi, Adebayo, A.O., Adekunle, Y.A , Okolie, S.O Department of Computer Science, Babcock University The main reason consumers are reluctant to purchase from the internet vendors is the lack of trust. The lack of clues and face-to-face interaction in the online environment has made it more difficult to establish trust with consumers in e-commerce. The e-commerce trust issue is identified as a Multi-Criteria Decision Making (MCDM) problem thereby resulting to MCDM as an approach used to solve the problem because it emphasizes on the integration between fuzzy logic and Analytical Hierarchical Process (AHP) and is commonly used in the research community. This approach is being used to form the bases for the conceptual framework of this study. The literature review used a narrative method to give a direction and scope of the research, while the exploratory research approach is adopted to narrow it down to specific direction in order to be able to determine the research design, sample size, technique, design and data collection method. Such techniques are both implemented in order to give clear answers to lingering questions about the researcher’s mind, to synthesize all the information obtained and to arrive at a stand on which methodology and methods to be used to solve the problem under investigation. A hierarchical model has been developed which can be used to assess trust parameters in application using MCDM techniques. The methodology used can accurately determine both the subjective and objective trust factors and parameters of e-commerce trust applications simultaneously in complex situations where there is an abundance of ambiguity that is highly inherent in the process of human decision making. To evaluate the overall success of any information ...

Airport Taxi Dispatching Based on VISSIM and Multi-objective Programming Model

Research Article of American Journal of Computer Sciences and Applications Airport Taxi Dispatching Based on VISSIM and Multi-objective Programming Model Boying Lv1*, Yishuai Tian1, Botao Liu1 1College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang, 443002, China. This paper solved the problem of how to manage the distribution of airport taxis and balance the revenue of long- and short-haul passenger taxis. In this research, we established a multi-objective programming model, which was solved using genetic algorithms to obtain a reasonable distribution scheme in airport with the highest riding efficiency: set up a pick-up location in the middle of the pick-up area, requiring all cars to leave uniformly when fully loaded, and release an average of 78 taxis per batch in every single boarding location. In addition, with the queuing theory we set the basic parameters of the road. Taking the income balance difference as the objective function, we used the VISSIM software to simulate the simulation. Then the short-term “priority” arrangement plan was: Calculate the ratio of the travel time of the short-distance taxi to the distance from the airport to the city center. If the ratio is less than 0.0659, the taxis that meet the conditions are allowed to be given priority after return. The results have some guidance and strong practical significance. Keywords: VISSIM; Airport taxi; Genetic algorithm; Short-distance “priority” ...

Application of Artificial Intelligence in Forecasting: A Systematic Review

Review Article of American Journal of Computer Sciences and Applications Application of Artificial Intelligence in Forecasting: A Systematic Review Albert Annor-Antwi and Ayman A. M. Al-Dherasi Supervisor: Dr. Yang Chunting School of Electrical and Electronic Technology and Computer Science, Zhejiang University of Science and Technology Purpose: The aim of this reach is to identify how Artificial Intelligence (AI) could be used in enhancing forecasting to achieve more accurate outcomes. The research also explores the influence that forecasting has on global economy and the reasons why it needs to be accurate. Also, the research explains various pitfalls identified in forecasting. Method: This research implements two research approaches which are review of literature and formulation of hypotheses. Seven hypotheses are created. Findings: AI, when integrated with other technologies such as Machine Learning (ML) and when provided with the right computer power, yields much more accurate results than many other forecasting methods. The technology is costly, however, and it is prone to cyber-attacks. Conclusion: The future of business is highly reliant on forecasting, which directly impacts the global economy. But, not every business will have the power to own the forecasting technology due to the cost, and business will need to increase security to protect the forecasting systems. Keywords: Artificial Intelligence, Forecasting, Business, Finance, Market, Machine Learning ...


Dr. Christo Ananth
Associate Professor, Electronics & Communication Engineering, Francis Xavier, Engineering College.

Dr. Debabrata Samanta
Associate professor, Dept. of Computer Applications,, Dayananda Sagar College of Arts, Science and Commerce, Bangalore, India.

Muhammad Zainuddin Lubis
Department of Informatics Engineering,Politeknik Negeri Batam

Dr. S. Sathish
Assistant Professor and Head, Dept. of Automobile Engineering, Vels University, Pallavaram, Chennai

Dr. Vaibhav Sundriyal
Research Scientist, Old Dominion University research Foundation, USA

Dr. Ali ÇALHAN
Associate Professor, Duzce University Technology Faculty Computer Engineering Department

Dr. M. Ramesh Kumar
Associate Professor/Project Coordinator, Department of Computer Science and Engineering, VSB College of Engineering Technical Campus, Coimbatore, Tamilnadu, India

Dr. S. Krishna Murthy
Associate Professor, Department of Applied Mathematics, Defence Institute of Advanced Technology, Deemed University(DU), Girinagar, Pune

Dr. S. Arivoli
Assistant Professor/EEE, VSB College of Engineering Technical Campus, Coimbatore-642109

Dr. Mustafa SEVINDIK
Institute of Science and Technology, Akdeniz University

Dr. Yashar Hashemi
Coding and Diagram Expert, Tehran Regional Dispatching, Tehran Regional Electric

Dr. Pushpraj Pal
Assistant Professor, SRM IET Ambala

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1.Hussam Elbehiery and Ghada Abdelhady. Unified transportation authenticated reservation system using online QR-code. American Journal of Computer Sciences and Applications, 2018; 2:12. DOI: 10.28933/ajcsa-2018-08-0201. 
2. K. M. Al-soufy, F. S. Al-kamali and F. A. Al-fuhaidy. Performance Evaluation of SC-FDMA Systems Using Wireless Images. American Journal of Computer Sciences and Applications, 2017; 1:11. DOI: 10.28933/ajcsa-2017-11-1801 

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American Journal of Computer Sciences and Applications

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