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.
Hierarchical Model and Characterize Identified E-Commerce Trust to Generate Customer Satisfaction Data
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 system, the usability evaluation was described as an essential construct based on the findings from this study.
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.
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.
Purpose: The aim of this research is to thoroughly analyze blockchain with respect to the role it plays in cybersecurity, and how this role may affect the future of blockchain and cybersecurity. Also, gaps are identified along with the shortcomings that cause these gaps. This research also identifies possible solutions to the gaps or issues. Method: the research approach used here is a review of the literature using the systematic-analysis technique. Other works that address various aspects of blockchain are analyzed in-depth to show its effectiveness. Results: there is a great possibility that blockchain is one of the future’s greatest cybersecurity solutions. Among the major issues include quantum computing, user habits, and conflicting interests. All these issues have various ways through which they can be addressed effectively in order to brighten the future of blockchain’s applicability in cybersecurity. Conclusion: blockchain, as it is, promotes fraud in cryptocurrency and therefore needs modification. Blockchain only needs reinforcement from technologies such as Artificial Intelligence and Machine learning to make it the future’s most dependable cybersecurity provider.
Rate monotonic scheduling algorithm (abbr. RM) is one of the main algorithms in real-time systems, but its operation efficiency is low relatively. In this paper, two-level scheduling method is used to improve the operational efficiency of RM algorithm, and the basic principle of computer processor in real-time system is analyzed, and the RM scheduling algorithm is implemented concretely. Considering the shortcoming of RM algorithm, a modified RM algorithm based two-level scheduling strategy is proposed. As a result, the performance and reliability of real-time system is increased, and the applicability of the method is widened.
Ifá scholars have primarily focused on its sociological and linguistic aspects while the scientific and computer aspects have been variously neglected. This paper explored the mathematical and computer model of Ifá corpus, which will assist Ifá priests to use the oracular process to simulate Ikin (the sixteen sacred palm nuts) and Ọ̀pẹ̀lẹ̀ (the divining bead chain) on the way to produce Odù (Ifá poetries) signatures. Each signature links the 256 Odùs in the database which invariably retrieved the corresponding verses with conforming sacrifices or advices. Microsoft Visual Studio.Net Express 2018 Community Edition on Window 10 Professional, 64-bit Operating System with Intel core duo CPU at 2.60 GHz, 12 GB memory was used to implement Ifá Application Tool (IAT). IAT interface supported Ikin and Ọ̀pẹ̀lẹ̀ simulation, the manual inscription of Odù signature, display of verses, stories, advises and recommended sacrifices. Usability testers scored the tool high in the ease of finding information within the user interface while it was above average in the skill to capture essential features for Odù divination accomplishments. This model supported Ifá professionals to make informed decisions and assessment by eliminating the level of ambiguity to interpret Odù corpus with a clear demarcation of its meanings.
Airborne missile servo system (AMSS) is a complex time-varying nonlinear system and the design of which is a multi-objective optimization problem. Fuzzy PID controller (FPC) is demonstrated appropriate for complex time-varying nonlinear systems but the design of which needs a tedious trial and error process. Non-dominated Sorting Genetic Algorithm III (NSGA-III) is a multi-objective evolutionary algorithm with good generality and robustness which can do a big favor for parameter tuning of complex system. This paper develops NSGA-III for parameter tuning in design process of FPC. Resulting FPCs are tested with model of AMSS on simulink. For further comparison, performance of conventional PID controller and sectional PID controller which is widely used in the engineering are also shown. Comparison shows that NSGA-III tuned FPCs have the better performance in AMSS.
Ascertaining the impact of Temperature, Rainfall, Water level, and Water discharge on Sediment Yield
The state of our natural environment is continuously changing. Various texts, global environmental monitoring bodies and environmental focused research groups agree that the deterioration, if not checked, will in the nearest future make it impossible for all living things to continue to exist in the ways we are accustomed to. As Earth’s temperature steadily increases, so has its sea level. This has brought about a lot of changes in the landscape of different catchment areas which is as a result of flooding. Flooding, in turn, has also resulted in death and proliferation of water borne diseases This work is motivated by the need to understand what factors mostly affect sediment yield in order to safe guard against its effects. The work takes a look into temperature, rainfall, water level, and water discharge factors from Oyan gauging station of the Ogun-Osun River basin, in the south western part of Nigeria. Our results show that all factors considered have a Sconsiderable effect on sediment yield.
Neural networks represent a brain metaphor for information processing. These models are biologically inspired rather than an exact replica of how the brain actually functions. Neural networks have been shown to be very promising systems in many forecasting applications and business classification applications due to their ability to learn from the data. This article aims to provide a brief overview of artificial neural network.The artificial neural network learns by updating the network architecture and connection weights so that the network can efficiently perform a task. It can learn either from available training patterns or automatically learn from examples or input-output relations.