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This academic paper demonstrates the implementation of a Linear Quadratic Regulator (LQR) controller design for optimal controlling a three connected links in an inverted pendulum form that attached to a moving cart to realize the stability of making a pendulum in a straight vertical line via translation of the cart left and right. To maintain a triple link inverted pendulum (TLIP) vertical, genetic algorithm has been employed to adjust and tune the parameters of LQR, which are the weighting matrices Q and R instead of the approach of try and error. In this article, a hybrid control algorithm (GA-LQR) proposed to select the optimal values of weighting matrices to overcome LQR design difficulties, which gives the best transient response requirements such as percentage overshoot and steady state error. The triple link inverted pendulum is model mathematically modelled in MATLAB platform to simulate the actual system where the results from the simulation gives acceptable and adequate performance of LQR controller in making the system stable.

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems.

Routing protocols are extremely important incredibly significant in data communication and computer networks. The high performance, reliability, stability, and security of the networks depend primarily on choosing the best type of dynamic routing protocol. In this paper, we evaluate and investigate the network performance for routing information protocol (RIP), enhanced interior gateway routing protocol (EIGRP), open shortest path first (OSPF), and intermediate system-to-intermediate system (IS-IS) routing protocols with three different scenarios of routes failure using the optimized network engineering tools (OPNET) simulator to determine which of the protocols is the most appropriate and effective in achieving high network performance. The results show that for large area networks, the EIGRP routing protocol gives the best network performance when all network routers are working with no failing, but when some network routers were failing to work and path failure is happening, the IS-IS link-state routing protocol works efficiently and gives the best performance. The obtained results for IS-IS protocol when failing seven routers is as: the hypertext transfer protocol (HTTP) page response time is (247.8 msec), voice delay variation is (4.19 µsec), video delay variation is (8.83 µsec) and ping request and response time is (115 msec).