Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of various methodologies in the field was created. Unlike previous studies that focused on picture splicing or copy-move detection, this study intends to investigate the universal type-independent strategies required to identify image tampering. The work provided analyses and evaluates several universal techniques based on resampling, compression, and inconsistency-based detection. Journals and datasets are two examples of resources beneficial to the academic community. Finally, a future reinforcement learning model is proposed.
Big data of different types, such as texts and images, are rapidly generated from the internet and other applications. Dealing with this data using traditional methods is not practical since it is available in various sizes, types, and processing speed requirements. Therefore, data analytics has become an important tool because only meaningful information is analyzed and extracted, which makes it essential for big data applications to analyze and extract useful information. This paper presents several innovative methods that use data analytics techniques to improve the analysis process and data management. Furthermore, this paper discusses how the revolution of data analytics based on artificial intelligence algorithms might provide
... Show MoreRegarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct
... Show MoreWhile many educators are highly focused on state test, it is important to consider that
over the course of a year, instructors can build in many opportunities to assess how learners
are learning. Therefore, assessment techniques are considered a good method to get benefit
for both instructors and learners in the process of teaching and learning. The sample consists
of 27 learners who participated in TOEFL training course in the Development and Continuous
Education Centre. Validity and reliability were verified.
To fulfill the aims and verify the hypothesis which reads as follows” It is hypothesized
that the TOEFL learners' scores will not be increased after TOEFL course training.” T-test
for two dependent samp
The technology in continuous and quick development, that reflects in all parts of our life and interred both scientific and practical fields. Marketing is one of them, a customer’s way to deal with choosing and demanding the product deferent from the traditional way. Some of the buying processes are electronic now, therefore the current research is identifying the digital channels that entered the world of marketing and influenced the activities and types that fall under this name and how it affects in positioning strategy, which is how to install the product or brand in the mind of the customer and was dimensions (brand identity, brand personality, brand communication, brand awareness, brand image), The researcher took t
... Show MoreThe research aims to demonstrate the impact of tax techniques on the quality of services provided to income taxpayers by studying the correlational and influencing relationships between the exploited variable (tax techniques) and the dependent variable (the quality of services provided to income taxpayers), and in line with the research objectives, the main hypothesis of the research was formulated (there is a relationship Significance between tax techniques and the quality of services provided to income taxpayers) a number of sub-hypotheses emerged from this hypothesis that were stated in the research methodology, and a number of conclusions were reached, the most important of which were (through the use of the correlation coeff
... Show MoreArabic text categorization for pattern recognitions is challenging. We propose for the first time a novel holistic method based on clustering for classifying Arabic writer. The categorization is accomplished stage-wise. Firstly, these document images are sectioned into lines, words, and characters. Secondly, their structural and statistical features are obtained from sectioned portions. Thirdly, F-Measure is used to evaluate the performance of the extracted features and their combination in different linkage methods for each distance measures and different numbers of groups. Finally, experiments are conducted on the standard KHATT dataset of Arabic handwritten text comprised of varying samples from 1000 writers. The results in the generatio
... Show MoreSpatial and frequency domain techniques have been adopted in this search. mean
value filter, median filter, gaussian filter. And adaptive technique consists of
duplicated two filters (median and gaussian) to enhance the noisy image. Different
block size of the filter as well as the sholding value have been tried to perform the
enhancement process.