Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based on the percentage of an accuracy measure of the previous work, are surveyed and introduced, with the aim of producing a concise review of using these algorithms in crime prediction. It is expected that this review study will be helpful for presenting such techniques to crime researchers in addition to supporting future research to develop these techniques for crime analysis by presenting some crime definition, prediction systems challenges and classifications with a comparative study. It was proved though literature, that supervised learning approaches were used in more studies for crime prediction than other approaches, and Logistic Regression is the most powerful method in predicting crime.
There is no doubt that teachers are the leaders of positive changing in community where they directed the students and build their brains. In our current generation that characterized by accelerated technological development that communication changes, economic and politics, needs from the teacher an active leadership skills that match with the soul of our generation and contribute in confrontation the current challenges and the future challenges in the form that lead to create a conscious generation where they will be a basic brick for the future community where the listeners looking forward the education where they support the continuity communication of develop process, economy, scientifically and in all life fields. In our study we take
... Show MoreSoil that has been contaminated by heavy metals is a serious environmental problem. A different approach for forecasting a variety of soil physical parameters is reflected spectroscopy is a low-cost, quick, and repeatable analytical method. The objectives of this paper are to predict heavy metal (Ti, Cr, Sr, Fe, Zn, Cu and Pb) soil contamination in central and southern Iraq using spectroscopy data. An XRF was used to quantify the levels of heavy metals in a total of 53 soil samples from Baghdad and ThiQar, and a spectrogram was used to examine how well spectral data might predict the presence of heavy metals metals. The partial least squares regression PLSR models performed well in pr
All modern critical approaches attempt to cover the meanings and overtones of the text, claiming that they are better than others in the analysis and attainment of the intended meanings of the text. The structural approach claims to be able to do so more than any other modern critical approach, as it claimed that it is possible to separate what is read from the reader, on the presumed belief that it is possible to read the text with a zero-memory. However, the studies in criticism of criticism state that each of these approaches is successful in dealing with the text in one or more aspects while failing in one or more aspects. Consequently, the criticism whether the approach possesses the text, or that the text rejects this possession, r
... Show MoreIn recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of ho
... Show MoreIn recent years, the performance of Spatial Data Infrastructures for governments and companies is a task that has gained ample attention. Different categories of geospatial data such as digital maps, coordinates, web maps, aerial and satellite images, etc., are required to realize the geospatial data components of Spatial Data Infrastructures. In general, there are two distinct types of geospatial data sources exist over the Internet: formal and informal data sources. Despite the growth of informal geospatial data sources, the integration between different free sources is not being achieved effectively. The adoption of this task can be considered the main advantage of this research. This article addresses the research question of how the
... Show MoreThe complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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An automatic text summarization system mimics how humans summarize by picking the most significant sentences in a source text. However, the complexities of the Arabic language have become challenging to obtain information quickly and effectively. The main disadvantage of the traditional approaches is that they are strictly constrained (especially for the Arabic language) by the accuracy of sentence feature functions, weighting schemes, and similarity calculations. On the other hand, the meta-heuristic search approaches have a feature tha
... Show MoreThis paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima
... Show MoreThe study aimed to clarify the meanings learned and inferred from reading books، letters and messages in Surat Al-Qur’an. The inductive method، the analytical method، and the deductive method، One of the most prominent results of the research: that the multiplicity of Qur’anic readings produces a variety and expansion in the meaning that has a clear impact on the interpretation by clarifying the meaning of the verse.