A lot of current crime cases have been reported to involve pistols, among other firearms. The whole firing pin impression image on a cartridge case is one of the most substantial clues for firearms identification. In ...
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A lot of current crime cases have been reported to involve pistols, among other firearms. The whole firing pin impression image on a cartridge case is one of the most substantial clues for firearms identification. In this study, a total of 16 features of geometric moments up to the sixth order were extracted from the entire firing pin impression images. All five pistols of the Parabellum Vector SPI 9mm model, manufactured in South Africa were used. The pistols were marked Pistol A, Pistol B, Pistol C, Pistol D, and Pistol E. A total of 747 bullets have been launched from the five pistols. Under an initial analysis, Pearson correlation coefficients between all pairs of features have demonstrated that the features were significant and that the features were inter-related. These problematic featureswere solved by dividing the features into subgroups of variables based on the same characteristics under the principle component analysis. The features that are highly correlated were brought together into meaningful components or factors. Discriminant analysis was applied for the identification of the types of pistols used based on the factors obtained. Classification results using cross-validation under the discriminant analysis pointed that 65.7% of the images were rightly classified according to the pistols used.
Many heavy crimes committed such as murders or robberies frequently involve firearms, particularly pistols. In order to solve the crime cases, firearm identification is becoming vital. Unique marks are left on the bul...
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Many heavy crimes committed such as murders or robberies frequently involve firearms, particularly pistols. In order to solve the crime cases, firearm identification is becoming vital. Unique marks are left on the bullet and the cartridge case when a firearm is fired. The firing pin impression is one of the most vital marks on any cartridge case. In this study, a total of 68 features of firing pin impression images – 20 basic statistical features, and 48 geometric moment features up to the sixth order – were extracted from three regions of the firing pin impression image, namely whole, centre and ring images. Five different types of pistol of the Parabellum Vector SPI 9 mm model were tested, where 50 bullets were fired from each pistol. Preliminary analysis using Pearson correlation shows that the features are significantly highly correlated. Therefore principal component analysis (PCA) was used to analyze the interrelationship among the features and combine them into a smaller set of factors while maintaining maximum information of the original patterns. PCA has reduced the dimensionality of the features into nine significant components of features. Discriminant analysis was used to identify the types of pistols used based on the new components. A total of 85.2% of the images were correctly classified according to the pistols used using cross-validation under discriminant analysis. The result demonstrates the potential of using PCA to reduce the dimensions of the numerical features towards an efficient firearm identification system.
There are many crime cases such as murders or robberies which frequently involve firearms, especially pistols. The centre firing pin impression image on a cartridge case is one of the important clues for firearms iden...
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There are many crime cases such as murders or robberies which frequently involve firearms, especially pistols. The centre firing pin impression image on a cartridge case is one of the important clues for firearms identification. In this study, a total of 16 features of geometric moments up to the sixth order were extracted from centre of firing pin impression images. A total of five pistols of the Parabellum Vector SPI 9mm model, made in South Africa were used. The pistols were labelled as Pistol A, Pistol B, Pistol C, Pistol D, and Pistol E. A total of 747 bullets have been fired from the five pistols. Under preliminary analysis, Pearson correlation coefficients between all pairs of features showed the features were significant and highly correlated among the features. This problematic features were solved by dividing the features into subgroups of variables based on similar characteristics under principle component analysis. The features that highly correlated were combined into meaningful components or factors. Discriminant analysis was applied to identify the types of pistols used based on the factors obtained. Classification results using cross-validation under discriminant analysis showed that 75.4% of the images were correctly classified according to the pistols used. The results of the study had shown a significant contribution towards Royal Malaysian Police Force in handling crime cases which involve firearms in more systematic manner.
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...
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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.
Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Bec...
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The objectives of Human Engineering (HE) are generally viewed as increasing human performance, reducing human error, enhancing personnel and equipment safety, and reducing training and related personnel costs. There a...
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The objectives of Human Engineering (HE) are generally viewed as increasing human performance, reducing human error, enhancing personnel and equipment safety, and reducing training and related personnel costs. There are other benefits that are thoroughly consistent with the direction of the Navy of the future, chief among these is reduction of required numbers of personnel to operate and maintain Navy ships. The Naval Research Advisory Committee (NRAC) report on Man-Machine Technology in the Navy estimated that one of the benefits from increased application of man-machine technology to Navy ship design is personnel reduction as well as improving system availability, effectiveness, and safety The objective of this paper is to discuss aspects of the human engineering design of ships and systems that affect manning requirements, and impact human-performance and safety The paper will also discuss how the application of human engineering leads to improved performance, and crew safety, and reduced workload, all of which influence manning levels. Finally, the paper presents a discussion of tools and case studies of good human engineering design practices which reduce manning.
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and s...
In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data—from patient records to imaging—graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human–AI collaboration, paving the way toward clinically meaningful predictions.
The Park City Math Institute 2016 Summer Undergraduate Faculty program met for the purpose of composing guidelines for undergraduate programs in datascience. The group consisted of 25 undergraduate faculty from a var...
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The Park City Math Institute 2016 Summer Undergraduate Faculty program met for the purpose of composing guidelines for undergraduate programs in datascience. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computerscience. These guidelines are meant to provide some structure for institutions planning for or revising a major in datascience.
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