In the contemporary world, security and safety are significant concerns for any country that wants to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based on real-time object detection systems for weapons auto-detection in video surveillance systems. We propose an early weapon detection framework using state-of-the-art, real-time object detection systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely reducing the number of false alarms in order to employ the model in real-life applications. The model is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth. The model can be employed as a precautionary system to prevent robberies by implying the model in outdoor surveillance cameras.
The current research offers a novel use of machine learning strategies to create a recommendation system. At recent era, recommender systems (RSs) have been used widely in e-commerce, entertainment purposes, and search engines. In more general, RSs are set of algorithms designed to recommend relevant items to users (movies to watch, books to read, products to buy, songs to listen, and others). This article discovers the different characteristics and features of many approaches used for recommendation systems in order to filter and prioritize the relevant information and work as a compass for searching. Recommender engines are crucial in some companies as they can create a big amount of income when they are effective or be a way to stand out remarkably from other rivals. As a proof of the importance of recommender engine, it can be stated that Netflix arrange a challenge (the “Netflix prize”) where the mission was to create a recommender engine that achieves better than its own algorithm with a prize of 1 million dollars to win.
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