Diana Abi-Nader¹, Hassan Harb², Ali Jaber¹, Ali Mansour³, Christophe Osswald³, Nour Mostafa^2,*, Chamseddine Zaki²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 279-298, 2025, DOI:10.32604/cmes.2024.056816 - 17 December 2024

Abstract Security and safety remain paramount concerns for both governments and individuals worldwide. In today’s context, the frequency of crimes and terrorist attacks is alarmingly increasing, becoming increasingly intolerable to society. Consequently, there is a pressing need for swift identification of potential threats to preemptively alert law enforcement and security forces, thereby preventing potential attacks or violent incidents. Recent advancements in big data analytics and deep learning have significantly enhanced the capabilities of computer vision in object detection, particularly in identifying firearms. This paper introduces a novel automatic firearm detection surveillance system, utilizing a one-stage detection… More >

S. Sandhya¹, A. Balasundaram^2,*, Ayesha Shaik¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2331-2343, 2023, DOI:10.32604/cmc.2023.032715 - 31 October 2022

Abstract Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network… More >

Arif Iqbal^*, Abdul Basit, Imran Ali, Junaid Babar, Ihsan Ullah

Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 299-309, 2021, DOI:10.32604/iasc.2021.014250 - 18 January 2021

Abstract Automated meter reading has recently been adopted by utility service providers for improving the reading and billing process. Images captured during meter reading are incorporated in consumer bills to prevent reporting false reading and ensure transparency. The availability of images captured during the meter reading process presents the potential of completely automating the meter reading process. This paper proposes a convolutional network-based multi-box model for the automatic meter reading. The proposed research leverages the inception model with a single shot detector to achieve high accuracy and efficiency compared to the existing state-of-the-art machine learning methods. More >