With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizingsubtle periodic patterns and uncovering relationships among the collected datasets. Through thiscomprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations infrequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates theefficacy of the AI model in generating productive insights and providing the most accurate predictionsof future criminal trends. These predictive insights are poised to revolutionize the strategies of lawenforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethicalconsiderations, this research ensures the continued feasibility of AI use while safeguarding individuals'constitutional rights, including privacy. The anticipated outcomes of this research are anticipated tofurnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in theidentification of effective crime prevention strategies. By harnessing the potential of AI, this researchcontributes to the promotion of proactive strategies and data-driven models in crime analysis andprediction, offering a promising avenue for enhancing public security in Los Angeles and othermetropolitan areas.
The permeability is the most important parameter that indicates how efficient the reservoir fluids flow through the rock pores to the wellbore. Well-log evaluation and core measurements techniques are typically used to estimate it. In this paper, the permeability has been predicted by using classical and Flow zone indicator methods. A comparison between the two methods shows the superiority of the FZI method correlations, these correlations can be used to estimate permeability in un-cored wells with a good approximation.
The most common cause of upper respiratory tract infection is coronavirus, which has a crown appearance due to the existence of spikes on its envelope. D-dimer levels in the plasma have been considered a prognostic factor for COVID-19 patients.
The aim of the study is to demonstrate the role of COVID-19 on coagulation parameters D-dimer and ferritin with their association with COVID-19 severity and disease progression in a single-center study.
المستودع الرقمي العراقي. مركز المعلومات الرقمية التابع لمكتبة العتبة العباسية المقدسة
This research aims to study the radiation concentration distribution of the old District of Najaf (Iraq), where 15 samples were taken from featured sites in the District, which represents archaeological, religious, and heritage sites. Track detector CR-39 was used to calculate the concentration of three different soil weights for each sample site after being exposed for a month. Geographical information systems (GIS) were used to distribute the radioactive concentration on the sites of the samples, where two interpolation methods, namely the inverse distance weight method (IDW) and the triangle irregular network method (NIT), to study the distribution of the radioactivity concentration. The study showed that the western part of the district
... Show MoreIn general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t
... Show MoreHepatocellular carcinoma (HCC) is the third most common cause of cancer-related death. Therefore, it is critical for researchers to understand molecular biology in greater depth. In several diseases including cancer, abnormal miRNA expression has been linked to apoptosis, proliferation, differentiation, and metastasis. Many miRNAs have been studied in relation to cancer, including miR-122, miR-223, and others. Hepatitis B and C viruses are the most important global risk factors for HCC. This study is intended to test whether serum miRNAs serve as a potential biomarker for both HCC and viral infections HBV and C. The expression of miRNA in 64 serum samples was analyzed by RT-qPCR. Compared to healthy volunteers, HCC patients' sera expre
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Building Information Modeling (BIM) is becoming a great known established collaboration process in Architecture, Engineering, and Construction (AEC) industry. In various cases in many countries, potential benefits and competitive advantages have been reported. However, despite the potentials and benefits of BIM technologies, it is not applied in the construction sector in Iraq just like many other countries of the world. The purpose of this research is to understand the uses and benefits of BIM for construction projects in Iraq. This purpose has been done by establishing a fr |
A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network
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