A robust facemask forgery detection system in video

An in-depth fake video uses an Artificial Intelligent (AI), AI programming, and a Personal computer (PC) mix to create a deep fake video of the action. Deep-faking can also be used to represent images and sounds. We provide insights into our reviews in this document. We're showing our dataset to start. At this point, we present the subtleties and reproductively of exploratory settings to evaluate the discovered effects finally. It is no surprise to find deep fake videos, which only monitor a tiny section of the video (e.g., the target face appears quickly on the video; hence the time is limited). We remove our system's fixed duration's persistent effects as each video contributes to the preparation, approval, and testing sections to reflect this. The edge groups are isolated from each video successively (without outline skips). The entire pipeline is ready to be finished when the approval stage is ten years old. Convolutional Neural Network (CNN) was the best and most reliable of the classification systems. Fake videos typically use low-quality pictures to mask faults or insist that the general public regard camera defects as unexplainable phenomena. 'This is a common trope with Unidentified Flying Object (UFO) videos: ghostly orbs are lenses; snakes are compression artifacts on one's face. In this study, we have implemented a sophisticated, knowledgeable method to recognize false images. Our test results using various monitored videos have shown that we can reliably predict whether videos are monitored through with simple co-evolutionary Long Short-Term Memory (LSTM) structure

SEARCH ENGINE OPTIMIZATION: A REVIEW

The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business

Game Design Factor Questionerin User Experience Analysis on Selera Nusantara Game

Game products are one of the media to provide pleasure, satisfaction and knowledge for users. Game Selera Nusantara is a game product created by a game studio in Yogyakarta, namely Gambir Studio, created with the aim of providing pleasure, satisfaction and knowledge to its users. In order for the goal of the Selera Nusantara Game to be achieved, a game design by a user-centered game designer is needed. Game Selera Nusantara is a game with the Casual genre that has a target audience of children and teenagers with the main goal of introducing Nusantara cuisine. The good aim of the Selera Nusantara Game which introduces the culture of the archipelago is the basis of this research by assessing the quality of the game so that it can be accepted by the target audience optimally. The User Experience method with Game Design Factor Questionaire and compared with the results of interviews with game designers from Game Selera Nusantara used in this study provides an analysis of the quality of the game quantitatively. The results of the analysis show that Game Selera Nusantara has a high score on the criteria of Game Goals, Game Mechanism, Interaction, Fantasy Game, Narrative, Sensation, Game Value, Mystery, Flow, which is more than 90%. This means that the Selera Nusantara game has been able to fulfill the goal of providing pleasure, satisfaction and knowledge to its users in accordance with the design of the game designer

A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle

Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances

Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors

Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered

Search engine optimization: a review

The Search Engine has a critical role in presenting the correct pages to the user because of the availability of a huge number of websites, Search Engines such as Google use the Page Ranking Algorithm to rate web pages according to the nature of their content and their existence on the world wide web. SEO can be characterized as methodology used to elevate site keeping in mind the end goal to have a high rank i.e., top outcome. In this paper the authors present the most search engine optimization like (Google, Bing, MSN, Yahoo, etc.), and compare by the performance of the search engine optimization. The authors also present the benefits, limitation, challenges, and the search engine optimization application in business

Deep Learning Techniques for Pattern Recognition in EEG Audio Signal-Processing-Based Eye-Closed and Eye-Open Cases

Recently, pattern recognition in audio signal processing using electroencephalography (EEG) has attracted significant attention. Changes in eye cases (open or closed) are reflected in distinct patterns in EEG data, gathered across a range of cases and actions. Therefore, the accuracy of extracting other information from these signals depends significantly on the prediction of the eye case during the acquisition of EEG signals. In this paper, we use deep learning vector quantization (DLVQ), and feedforward artificial neural network (F-FANN) techniques to recognize the case of the eye. The DLVQ is superior to traditional VQ in classification issues due to its ability to learn a code-constrained codebook. On initialization by the k-means VQ approach, the DLVQ shows very promising performance when tested on an EEG-audio information retrieval task, while F-FANN classifies EEG-audio signals of eye state as open or closed. The DLVQ model achieves higher classification accuracy, higher F score, precision, and recall, as well as superior classification abilities as compared to the F-FANN