An Expert System for Weapon Identification and Categorization Using Machine Learning Technique to Retrieve Appropriate Response

In response to any terrorist attack on hospitals, airports, shopping malls, schools, universities, colleges, railway stations, passport offices, bus stands, dry ports and the other important private and public places, a proper plan will need to be developed effective response. In normal moments, security guards are deployed to prevent criminals from doing anything wrong. For example, someone is moving around with a weapon, and security guards are watching its movement through closed circuit television (CCTV). Meanwhile, they are trying to identify his weapon in order to plan an appropriate response to the weapon he has. The process of manually identifying weapons is man-made and slow, while the security situation is critical and needs to be accelerated. Therefore, an automated system is needed to detect and classify the weapon so that appropriate response can be planned quickly to ensure minimal damage. Subject to previous concerns, this study is based on the Convoluted Neural Network (CNN) model using datasets that are assembled on the YOLO and you only see once. Focusing on real-time weapons identification, we created a data collection of images of multiple local weapons from surveillance camera systems and YouTube videos. The solution uses parameters that describe the rules for data generation and problem interpretation. Then, using deep convolutional neural network models, an accuracy of 97.01% is achieved

Identification of Finger Vein Images with Deep Neural Networks

To establish identification, individuals often utilize biometrics so that their identity cannot be exploited without their consent. Collecting biometric data is getting easier. Existing smartphones and other intelligent technologies can discreetly acquire biometric information. Authentication through finger vein imaging is a biometric identification technique based on a vein pattern visible under finger's skin. Veins are safeguarded by the epidermis and cannot be duplicated. This research focuses on the consistent characteristics of veins in fingers. We collected invariant characteristics from several cutting-edge deep learning techniques before classifying them using multiclass SVM. We used publicly available image datasets of finger veins for this purpose. Several assessment criteria and a comparison of different deep learning approaches were used to characterize the performance and efficiency of these models on the SDUMLA-HMT dataset.&nbsp

Deep learning to predict Pulmonary Tuberculosis from Lung Posterior Chest Radiographs

Tuberculosis is one of the most dangerous health conditions on the globe. As it affects the human body, tuberculosis is an infectious illness. According to the World Health Organization, roughly 1.7 million individuals get TB throughout the course of their lifetimes. Pakistan ranks fifth among high-burden nations and is responsible for 61% of the TB burden within the WHO Eastern Mediterranean Region. Various methods and procedures exist for the early identification of TB. However, all methods and techniques have their limits. The bulk of currently known approaches for detecting TB rely on model-based segmentation of the lung. The primary purpose of the proposed study is to identify pulmonary TB utilising chest X-ray (Poster Anterior) lung pictures processed using image processing and machine learning methods. The recommended study introduces a unique model segmentation strategy for TB identification. For classification, CNN, Google Net, and other systems based on deep learning are used. On merged datasets, the best accuracy attained by the suggested method utilising Google Net was 89.58 percent. The recommended study will aid in the detection and accurate diagnosis of TB.&nbsp