Performance Evaluation of Different Universal Steganalysis Techniques in JPG Files

Steganalysis is the art of detecting the presence of hidden data in files. In the last few years, there have been a lot of methods provided for steganalysis. Each method gives a good result depending on the hiding method. This paper aims at the evaluation of five universal steganalysis techniques which are “Wavelet based steganalysis”, “Feature Based Steganalysis”, “Moments of characteristic function using wavelet decomposition based steganalysis”, “Empirical Transition Matrix in DCT Domain based steganalysis”, and “Statistical Moment using jpeg2D array and 2D characteristic function”. A large Dataset of Images -1000 images- are subjected to three types of steganographic techniques which are “Outguess”, “F5” and “Model Based” with the embedding rate of 0.05, 0.1, and 0.2. It was followed by extracting the steganalysis feature used by each steganalysis technique for the stego images as well as the cover image. Then half of the images are devoted to train the classifier. The Support vector machine with a linear kernel is used in this study. The trained classifier is then used to test the other half of images, and the reading is reported The “Empirical Transition Matrix in DCT Domain based steganalysis” achieves the highest values among all the properties measured and it becomes the first choice for the universal steganalysis technique

Potential Influences of Graphic Design, And Critical Thinking on Publishing Scientific Products and Performance of Academic Services

Graphic design is a creative process that includes art and technology to convey thoughts, particularly if it is accompanied with creative skills based on strong academic knowledge. It can be used to reflect ideas, trends, and tendencies and this helps touching their reality. This research is mainly aiming at studying how critical presentation of scientific findings, data and applications with graphic and creative designs using an expressive visual language would help enhancing data dissemination and simplifying difficult scientific data and phenomenon making them more convenient for a wide range of audiences and better understood by various levels of background and professionality

Usability and comfort in Canadian offices: Interview of 170 university employees

Increasing building automation to improve energy efficiency introduces a risk of reducing occupants' perceived control and overall comfort. To this end, this paper presents a field study that used contextual techniques to explore the relationship between occupants' perceived control and comfort, as well as their preferences for building automation. A total of 170 occupants in 23 Canadian university campus buildings were interviewed in their offices using semi-structured interviews. All interviews entailed verbally administering a survey while photographs were systematically used to identify the context of occupants' interactions with building controls. Findings revealed that occupants' perception of comfort was moderately correlated to their perception of control over their indoor environment. Occupants also showed an overwhelming preference for more control opportunities in their offices (e.g. operable windows and dimmable lighting controls). Conducting interviews in offices yielded many interesting anecdotes and enabled the researcher to identify contextual issues related to building controls' accessibility, which may have been unnoticed otherwise. The findings of this research contribute to a broader debate within the research community about the appropriate level of building automation to optimize energy efficiency and occupant comfort

Key Performance Indicators Detection Based Data Mining

One of the most prosperous domains that Data mining accomplished a great progress is Food Security and safety. Some of Data mining techniques studies applied several machine learning algorithms to enhance and traceability of food supply chain safety procedures and some of them applying machine learning methodologies with several feature selection methods for detecting and predicting the most significant key performance indicators affect food safety. In this research we proposed an adaptive data mining model applying nine machine learning algorithms (Naive Bayes, Bayes Net Key -Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), J48, Hoeffding tree, Logistic Model Tree) with feature selection wrapper methods (forward and backward techniques) for detecting food deterioration’s key performance indicators. In conclusion the proposed model applied effectively and successfully detected the most significant indicators for meat safety and quality with the aim of helping farmers and suppliers for being sure of delivering safety meat for consumer and diminishing the cost of monitoring meat safety

Risk Assessment Approaches in Banking Sector –A Survey

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business using historical data, using a model, banks\u27 business, and strategic goals

A robust uniform B-spline collocation method for solving the generalized PHI-four equation

In this paper, we develop a numerical solution based on cubic B-spline collocation method. By applying Von-Neumann stability analysis, the proposed technique is shown to be unconditionally stable. The accuracy of the presented method is demonstrated by a test problem. The numerical results are found to be in good agreement with the exact solution

Strategic analysis of the obstetric and gynaecological internship in Sudan

Background: The high expectations of the increasingly questioning society lays a great burden on the first line treating doctors in Sudan. This is particularly true in the obstetrics and gynaecology departments. The impact of training of the house-officer in surgical departments was not studied before in Sudan.The aim: To evaluate the gains in knowledge and skills of house-officers in the obstetrics and gynaecology departments as reflected by their activities and their opinions.Methodology: A prospective cohort carried in the period from May 2011 through June 2011. The data was collected from 200 house-officers. Their activities and duties as formulated by their seniors and supervisors and gains in knowledge and skills were noted.Results: All house-officers participated actively in the clinical diagnosis (history, physical examination and relevant investigations) and management of cases of antepartum and postpartum haemorrhages. Of them 186(93%) had duties not more than twice a week. However, 121(60.5%) shared training opportunities in units having seven or less peers. Also, 109(54.5%) had regular seminars and tutorials. In practice, 165(82.5%) performed evacuations, 158(79%) participated in normal deliveries, and 110(55%) were assisted in performing caesarean sections.Conclusion: The overall performance of house-officers in the department of obstetrics and gynaecology in Sudan is good. However, standards of training need to revisited to fill gabs in training if these young doctors are to be dispatched to rural hospital immediately after the internshipperiod.Key words: Internship, preregistration medical graduates duties, house-officers, obstetrics and gynaecology, medical education, and medical responsibility

Credit Card Fraud Detection Using Machine Learning Techniques

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today\u27s banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary losses, not just for financial institutions but also for individuals. as technology and usage patterns evolve, making credit card fraud detection a particularly difficult task. Traditional statistical approaches for identifying credit card fraud take much more time, and the result accuracy cannot be guaranteed. Machine learning algorithms have been widely employed in the detection of credit card fraud. The main goal of this review intends to present the previous research studies accomplished on Credit Card Fraud Detection (CCFD), and how they dealt with this problem by using different machine learning techniques

CHOLEDOCHAL CYST DIAGNOSTIC AND OPERATIVE CHALLENGE

The variations in the morphological characteristics of the extra-hepatic biliary system are numerous. It has been stated that the extra-hepatic biliary system has more anomalies in one cubic centimeter of the space around the region of the cystic duct than any other part of the body (1,2). The incidence of congenital anomalies of the extra-hepatic biliary system varies between 0.58% and 47.2% (3).One such rare anomaly is Choledochal cyst ( CDC), also known as congenital common bile duct cyst (BDC), is a rare type of bile duct cyst of uncertain origin. The majority of cases reported are young women and children of Asian descent. In North America, its incidence is estimated to be 1/150 000(2), but it is increasing in Western adults. The most common symptoms of CDC are abdominal pain, jaundice and abdominal mass.(4

Eyes on the Goal! Exploring Interactive Artistic Real-Time Energy Interfaces for Target-Specific Actions in the Built Environment

Current research is focused on sensing and modeling occupant behavior to predict it and automate building controls. Another line of research recommends influencing the behavior of occupants through feedback mechanisms and engagement. Yet, most of the work has focused on pushing occupants to reduce energy consumption over a long time and does not explore the potential to guide users to take specific actions promptly. The study examines a new interface mechanism that aims to solicit immediate and predefined actions from occupants. Building on seminal research in the field, the study uses art visualization to reinterpret social feedback. We test this approach in an immersive interaction space where participants react to artistic visuals to attain predefined settings for three indoor devices. In the 197 interactions recorded, participants’ overall actions conformed with the predefined goals. The participants were able to reach all or some of the targets in more than 80%, within an average of less than 30 seconds. We also see that complementing the visuals with textual hints improved the interaction in terms of engagement and accuracy. We conclude that ambient, abstract, and artistic real-time goal-driven feedback is effective in influencing immediate actions. We recommend that guiding occupants didactically has a strong potential for advancing building controls