Lung cancer is the leading cancer for causing death for both men and women. It also has one of the lowest survival rates in five-year of all cancer types. It remains a challenge to lung cancer relapse prediction after surgery, especially for non-small cell lung cancer (NSCLC). This study aimed to enhance prediction and detection using eXtreme Gradient Boosting (XGBoost) model to detect lung cancer diagnoses and predict its relapse after surgery by using gene expression and its transcriptome changes due to cancer. This can aid to enhance early tumour progression handling and reducing the painful treatment. In this study, it used real New Generation RNA_seq (NGS) and microarray gene expression datasets for different types of lung cancer. The results demonstrated the effectiveness of the XGBoost model compared to other machine learning models especially in handling unbalance datasets.
Most lung cancers do not cause symptoms until the disease is in its later stage. That led the lung cancer having a high fatality rate compared to other cancer types. Many scientists try to use artificial intelligence algorithms to produce accurate lung cancer detection. This paper used extreme gradient boosting (XGBoost) models as a base model for its effectiveness. It enhanced lung cancer detection performance by suggesting three stages model; feature stage, XGBooste parallel stage and selection stage. This study used two types of gene expression datasets; RNA-sequence and microarray profiles. The results presented the effectiveness of the proposed model, especially in dealing with imbalanced datasets, by having 100% each of sensitivity, specificity, precision, F1_score, area under curve (AUC), and accuracy metrics when it applied on all of the datasets used in this study.
This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.
Wireless Networked Control System (WNCS), has the advantage of control signal efficiency, reduced costs, robust, and more flexible over the wired Networked Control System (NCS). However, one of the most important challenges of WNCS is the variable and random time delay associated with the network that effect on the whole system stability. In this paper, a fuzzy PID controller is proposed to overcome this issue by controlling the DC motor over the Wi-Fi network. PSO algorithm is used to tune the controller, and TrueTime simulator is used to simulate the Wi-Fi network. Experimental results showed that the tuned Fuzzy PID controller is efficiently reduced the effect of the time delay. The results show that the network can handle up to 7500 and 6000 nodes when the bandwidth is 0.4 and 0.9 respectively without a sensible degradation in the system performance of the tackled system.
The need for vehicle tracking system in real time is growth continues due to increase the cases of theft. This type of system in real time needs to transmit large data with huge number of HTTP request to the server to keep tracking and monitoring in real time, thus causes spend extremely high cost every month for transportation the information on tracking vehicles to server therefor the needs for reducing the number of transportation and data size that transmits in each HTTP request to save expenses. This paper designed and implement an integrated vehicle tracking system in real time to track vehicle anywhere and anytime. This system is divided into two parts: vehicle tracking part and monitoring part. Tracking part is represented by installation the electronic devices in the vehicle using modern Global Positioning System (GPS), microcontroller Arduino UNO R3 and SIM800L GSM/GPRS modem. GPS is determined location of the vehicle via received coordinates from satellites such as latitude and latitude with accuracy ranging approximately 2.5 meters; the coordinates faked to add a type of protection to information on vehicles without effecting on characterizing real time tracking before sending it via a General Packet Radio service (GPRS). The monitoring part is in the cloud and will receive the coordinates and displays it on a map in a web page. The main contribution of this system is it reduced data size that sent from in-vehicle device via selected only necessary data for tracking vehicle from NEMA sentences of GPS and reduced number of HTTP request that sent to remote server via constrain the transmission of information with the movement of vehicles, since when vehicle moved the coordinates each 10s and did not send anything when the vehicle stopped thus will reduce the cost of expenses every month. This system can be utilized to track and monitoring the vehicles of large universities, companies, organization and also can be used in army vehicles and police vehicles.
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