<span lang="EN-US">Artificial Intelligence is great when it comes to routine activities and vast amounts of data are analyzed. This can be done more quickly and efficiently than men. In the world of digital marketing, Artificial Intelligence is quickly coming into play. With Artificial Intelligence joining the digital marketing environment, predicting user behavior, search cycles, and much more will be easier. This can support websites that are highly user-friendly for organizations. Moreover, with the aid of Artificial Intelligence, content creation has become a faster and easier task for brands. Practically, a company's degree of enterprise marketing can have an effect on its overall business efficiency. Entrepreneurial marketing is driven by entrepreneurial opportunities which involves the proactive identification and exploitation of opportunities for acquiring and retaining profitable customers through Digital approaches to risk management, resource leveraging and value creation. This research was done by collecting data using semi structure questionnaire distributed to 169 start up owners in Klang Valley area. Using two-layer 6-3-1 with hyperbolic tangent-purelin configurations neural network model, it was found that proactiveness, risk taking, resource leveraging, opportunity focus, intensity and value add are the significant factors towards digital marketing respectively. It is expected that the findings would give some inputs to the Malaysian entrepreneurs on innovative digital marketing in their businesses, regardless the sizes.</span>
Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. In this study, a set of data which focused on selected 6 features of firing pin impression images before an entirety of five different pistols of South African made; the Parabellum Vector SPI 9mm model, were used. The numerical features are geometric moments of whole image computed from a total of 747 cartridge case images. Under pattern recognition theory, the supervised features of firing pin impression images were then trained and validated using a two-layer backpropagation neural network (BPNN) design with computed hidden layers. A two-layer 6-7-5 connections BPNN of sigmoid/linear transfer functions with 'trainlm' algorithm was found to yield the best classification result using cross-validation, where 96% of the images were correctly classified according to the pistols used. Moreover, the network was trained under very small mean-square error (MSE=0.01). This means that neural network method is capable to learn and validate well the numerical features of whole firing pin impression with high precision and fast classification results.
Neural framework has for quite a while been known for its ability to handle a complex nonlinear system without a logical model and can learn refined nonlinear associations gives. Theoretically, the most surely understood computation to set up the framework is the backpropagation (BP) count which relies on upon the minimization of the mean square error (MSE). However, this algorithm is not totally efficient in the presence of outliers which usually exist in dynamic data. This paper exhibits the modelling of quadriceps muscle model by utilizing counterfeit smart procedures named consolidated backpropagation neural network nonlinear autoregressive (BPNN-NAR) and backpropagation neural network nonlinear autoregressive moving average (BPNN-NARMA) models in view of utilitarian electrical incitement (FES). We adapted particle swarm optimization (PSO) approach to enhance the performance of backpropagation algorithm. In this research, a progression of tests utilizing FES was led. The information that is gotten is utilized to build up the quadriceps muscle model. 934 preparing information, 200 testing and 200 approval information set are utilized as a part of the improvement of muscle model. It was found that both BPNN-NAR and BPNN-NARMA performed well in modelling this type of data. As a conclusion, the neural network time series models performed reasonably efficient for non-linear modelling such as active properties of the quadriceps muscle with one input, namely output namely muscle force.
This paper implements two layer neural networks with different feedforward backpropagation algorithms for better performance of firearm classification us-ing numerical features from the ring image. A total of 747 ring images which are extracted from centre of the firing pin impression have been captured from five different pistols of the Parabellum Vector SPI 9mm model. Then, based on finding from the previous studies, the six best geometric moments numerical fea-tures were extracted from those ring images. The elements of the dataset were further randomly divided into the training set (523 elements), testing set (112 el-ements) and validation set (112 elements) in accordance with the requirement of the supervised learning nature of the backpropagation neural network (BPNN). Empirical results show that a two layer BPNN with a 6-7-5 configura-tion and tansig/tansig transfer functions with ‘trainscg’ training algorithm has produced the best classification result of 98%. The classification result is an improvement compared to the previous studies as well as confirming that the ring image region contains useful information for firearm classification.
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