Erssa Arif^1,*, Syed Khuram Shahzad², Muhammad Waseem Iqbal³, Muhammad Arfan Jaffar⁴, Abdullah S. Alshahrani⁵, Ahmed Alghamdi⁶

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4615-4630, 2022, DOI:10.32604/cmc.2022.027571

Abstract The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.… More >

Sherif S. M. Ghoneim^1,*, Amr E. Rashed², Nagy I. Elkalashy¹

Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 467-479, 2021, DOI:10.32604/iasc.2021.016681

Abstract This study uses several artificial intelligence approaches to detect and estimate electrical faults in photovoltaic (PV) farms. The fault detection approaches of random forest, logistic regression, naive Bayes, AdaBoost, and CN2 rule induction were selected from a total of 12 techniques because they produced better decisions for fault detection. The proposed techniques were designed using distributed PV current measurements, plant current, plant voltage, and power. Temperature, radiation, and fault resistance were treated randomly. The proposed classification model was created using the Orange platform. A classification tree was visualized, consisting of seven nodes and four leaves, with a depth of four… More >