Crime is considered as an unlawful activity of all kinds and it is punished by law. Crimes have an impact on a society's quality of life and economic development. With a large rise in crime globally, there is a necessity to analyze crime data to bring down the rate of crime. This encourages the police and people to occupy the required measures and more effectively restricting the crimes. The purpose of this research is to develop predictive models that can aid in crime pattern analysis and thus support the Boston department's crime prevention efforts. The geographical location factor has been adopted in our model, and this is due to its being an influential factor in several situations, whether it is traveling to a specific area or living in it to assist people in recognizing between a secured and an unsecured environment. Geo-location, combined with new approaches and techniques, can be extremely useful in crime investigation. The aim is focused on comparative study between three supervised learning algorithms. Where learning used data sets to train and test it to get desired results on them. Various machine learning algorithms on the dataset of Boston city crime are Decision Tree, Naïve Bayes and Logistic Regression classifiers have been used here to predict the type of crime that happens in the area. The outputs of these methods are compared to each other to find the one model best fits this type of data with the best performance. From the results obtained, the Decision Tree demonstrated the highest result compared to Naïve Bayes and Logistic Regression.
Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover. The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit
... Show MoreThe influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreThe research discusses the mechanism for analyzing the salary scale in the public sector through an analysis of grades, their stages, occupants and their financial entitlements, and the extent to which the information obtained for their investment in strategic planning, conducting correction and treatment can be used. The salaries of the employees in them, whose number is (1117) employees, to be a field of research, as the salary structure in it for the year 2019 was analyzed by relying on a number of statistical tools in the analysis process, including the arithmetic circles, upper limits, minimum limits and percentage, and with
... Show MoreNewspaper headlines are described as compressed and ambiguous pieces of discourse that represent the bodies of the articles. Their main function is to provide the readers with an informative message they would have no prior idea about. Ifantidou (2009) claims that the function of a headline is to get the readers’ attention rather than providing information because it does not have to represent the whole of the article it refers to. This paper aims at examining this hypothesis in relation to scientific news headlines reported by a number of news agencies. The paper follows Halliday (1967) information structure theory by applying it on ten selected headlines; each two headlines represent one scientific discovery reported by different new
... Show MoreCleaning is one of the services provided by the state and its ad hoc institutions. Every developed country is clean due to the good planning and attention given to the staff of all municipalities and service institutions. Beside, the attention paid to this sector is considered one of the windows to maintain the states’ aesthetics. If janirtos stop disposing of all the discarded wastes, the result will be having big piles of waste, affecting accordingly the health and safety of its citizens and the aesthetics of the areas. The janitors and those responsible for them are tha staff that the state must take care of because of their importance to the state in general and to the province in particular. The importance of the research
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Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it
... Show MoreThyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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