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Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence
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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.

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Publication Date
Sat Oct 06 2012
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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Publication Date
Tue Jun 01 2021
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Surface Roughness after Turning of Duplex Stainless Steel (DSS)
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Feed Forward Back Propagation artificial neural network (ANN) model utilizing the MATLAB Neural Network Toolbox is designed for the prediction of surface roughness of Duplex Stainless Steel during orthogonal turning with uncoated carbide insert tool. Turning experiments were performed at various process conditions (feed rate, cutting speed, and cutting depth). Utilizing the Taguchi experimental design method, an optimum ANN architecture with the Levenberg-Marquardt training algorithm was obtained. Parametric research was performed with the optimized ANN architecture to report the impact of every turning parameter on the roughness of the surface. The results suggested that machining at a cutting speed of 355 rpm with a feed rate of 0.07 m

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Publication Date
Sat Jul 22 2023
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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This paper deals with prediction the effect of soil remoulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity
according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil remoulding due to actual pile driving. T

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Publication Date
Mon Oct 01 2012
Journal Name
Journal Of Engineering
Prediction of Smear Effect on the Bearing Capacity of Driven Piles
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This paper deals with prediction the effect of soil re-moulding (smear) on the ultimate bearing capacity of driven piles. The proposed method based on detecting the decrease in ultimate bearing capacity of the pile shaft (excluding the share of pile tip) after sliding downward. This was done via conducting an experimental study on three installed R.C piles in a sandy clayey silt soil. The piles were installed so that a gap space is left between its tip and the base of borehole. The piles were tested for ultimate bearing capacity according to ASTM D1143 in three stages. Between each two stages the pile was jacked inside the borehole until a sliding of about 200mm is achieved to simulate the soil re-moulding due to actual pile driving. The re

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Publication Date
Mon Dec 02 2024
Journal Name
Engineering, Technology & Applied Science Research
An Artificial Neural Network Prediction Model of GFRP Residual Tensile Strength
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This study uses an Artificial Neural Network (ANN) to examine the constitutive relationships of the Glass Fiber Reinforced Polymer (GFRP) residual tensile strength at elevated temperatures. The objective is to develop an effective model and establish fire performance criteria for concrete structures in fire scenarios. Multilayer networks that employ reactive error distribution approaches can determine the residual tensile strength of GFRP using six input parameters, in contrast to previous mathematical models that utilized one or two inputs while disregarding the others. Multilayered networks employing reactive error distribution technology assign weights to each variable influencing the residual tensile strength of GFRP. Temperatur

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Applied Engineering Science
Rutting prediction of hot mix asphalt mixtures reinforced by ceramic fibers
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One of the most severe problems with flexible asphalt pavements is permanent deformation in the form of rutting. Accordingly, the practice of adding fiber elements to asphalt mix to improve performance under dynamic loading has grown significantly in order to prevent rutting distress and ensure a safe and long-lasting road surface. This paper explores the effects of a combination of ceramic fiber (CF), a low-cost, easily available mineral fiber, and thermal insulator fiber reinforced to enhance the Marshall properties and increase the rutting resistance of asphalt mixes at high temperatures. Asphalt mixtures with 0%, 0.75%, 1.5%, and 2.25% CF content were prepared, and Marshall stability and wheel tracking tests were employed to stu

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Publication Date
Tue Sep 01 2015
Journal Name
Journal Of Engineering
Application of Box-Behnken Method Based ANN-GA to Prediction of wt.% of Doping Elements for Incoloy 800H Coated by Aluminizing-Chromizing
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In this work , an effective procedure of Box-Behnken based-ANN (Artificial Neural Network) and GA (Genetic Algorithm) has been utilized for finding the optimum conditions of wt.% of doping elements (Ce,Y, and Ge) doped-aluminizing-chromizing of Incoloy 800H . ANN and Box-Behnken design method have been implanted for minimizing hot corrosion rate kp (10-12g2.cm-4.s-1) in Incoloy 800H at 900oC . ANN was used for estimating the predicted values of hot corrosion rate kp (10-12g2.cm-4.s-1) . The optimal wt.% of doping elements combination to obtain minimum hot corrosion rate was calculated using genetic alg

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Publication Date
Sat Sep 30 2017
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Spatial Data Analysis for Geostatistical Modeling of Petrophysical Properties for Mishrif Formaiton, Nasiriya Oil Field
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Spatial data analysis is performed in order to remove the skewness, a measure of the asymmetry of the probablitiy distribution. It also improve the normality, a key concept of statistics from the concept of normal distribution “bell shape”, of the properties like improving the normality porosity, permeability and saturation which can be are visualized by using histograms. Three steps of spatial analysis are involved here; exploratory data analysis, variogram analysis and finally distributing the properties by using geostatistical algorithms for the properties. Mishrif Formation (unit MB1) in Nasiriya Oil Field was chosen to analyze and model the data for the first eight wells. The field is an anticline structure with northwest- south

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Publication Date
Wed Mar 30 2022
Journal Name
Journal Of Educational And Psychological Researches
Skills and Standards of Selection and Use of Assistive Technology among Saudi Teachers of Students with Intellectual and Developmental Disabilities
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The purpose of the current study was to explore the standards that teachers take into consideration when selecting and using assistive technology (AT), in addition to their knowledge and skills in this area. A quantitative, descriptive survey design was used and a convenience sample of 79 teachers of students with intellectual disabilities and autism spectrum disorder (ASD) participated in the current study. Based on the four main areas of the SETT Framework—student, environment, tasks, and tools—, teachers reported a lack consideration for most of the standards in each area. Among other findings, statistically significant differences were found between teachers’ standards of the SETT Framework, with teachers who had previous profe

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Publication Date
Wed Dec 01 2021
Journal Name
Journal Of Economics And Administrative Sciences
THE MEDIATING ROLE OF PRODUCT INNOVATION ON THE RELATIONSHIP BETWEEN MARKETING INFORMATION SYSTEM AND CUSTOMER ORIENTATION
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 Marketing information system (KMIS) is an essential factor of developing business’ performance and getting sustainable success. The main goal of the research is to measure effect of MIS on customer orientation and product innovation. Also, another goal is to analyze the mediation role of product innovation in relationship MIS and customer orientation. This study sought to analyze the marketing information system and measure its effect on the customer orientation and product innovation. The data of the study were collected using questionnaire. The data were analyzed using statistical tools and SPSS programming. The results of the study showed that the KMIS can positively and significantly effect product innovation. Also, t

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