This paper describes the development of a student attendance system based on Radio Frequency Identification (RFID) technology. The existing conventional attendance system requires students to manually sign the attendance sheet every time they attend a class. As common as it seems, such system lacks of automation, where a number of problems may arise. This include the time unnecessarily consumed by the students to find and sign their name on the attendance sheet, some students may mistakenly or purposely signed another student's name and the attendance sheet may got lost. Having a system that can automatically capture student's attendance by flashing their student card at the RFID reader can really save all the mentioned troubles. This is the main motive of our system and in addition having an online system accessible anywhere and anytime can greatly help the lecturers to keep track of their students' attendance. Looking at a bigger picture, deploying the system throughout the academic faculty will benefit the academic management as students' attendance to classes is one of the key factor in improving the quality of teaching and monitoring their students' performance. Besides, this system provides valuable online facilities for easy record maintenance offered not only to lecturers but also to related academic management staffs especially for the purpose of students' progress monitoring.

Construction cost estimation is one of the essential processes in construction management. Project cost is a complex engineering problem due to various factors affecting the construction industry. Accurate cost estimation is important in construction management and significantly impacts project performance. Artificial intelligence (AI) models have been effectively implemented in construction management studies in recent years owing to their capability to deal with complex problems. In this research, extreme gradient boosting is developed as an advanced input selector algorithm and coupled with three AI models, including random forest (RF), artificial neural network (ANN), and support vector machine (SVM) for cost estimation. Datasets were gathered based on a survey conducted on 90 building projects in Iraq. Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. The results indicated that the best prediction was attained by XGBoost‐RF using six input parameters, with r‐squared and the mean absolute percentage error equal to 0.87 and 0.25, respectively. The comparison results revealed that all AI models showed good prediction performance when applied to datasets affected by more than two parameters. The outcomes of this research revealed an optimistic strategy that can help decision makers select the influencing parameters in the early phases of project management. Also, developing a prediction model with high precision results can assist the project’s estimators in decreasing the errors in the cost estimation process.

When time goes by, human beings are advancing in technology, artificial and natural disasters are drastically increasing. The forest fire is one of the hazards. Forest fire incinerates trees that provide us with oxygen and if it is not detected early, it is very elusive to stop a forest fire from continue burns. The project’s objective is to capture infrared image of forest fire detection using the appropriate camera, detect fire with RGB and YCbCr colour model to isolate fire pixels from the background and separate luminance and chrominance from the original image, and filter image using MATLAB Analyzer to process images. The method is tested on a selected image, which captured by the camera that contains fire. Next method is used for calculating and analysing the fire image, which to differentiate between fire detection or false detection. Other method is used to process the fire image, which the image will compute and shown in terminal nodes and graphs by using Wavelet Analyzer 5.0. The results of this system are achieved fire detection and obtain data for the fire images.

This paper evaluates and compares mobile ad hoc network (MANET) routing protocols for wireless sensor network (WSN). It focuses and compares the performance analysis based on Quality of Service (QoS) factors on the throughput and delay between Ad Hoc on Demand Distance Vector (AODV) and Temporary Ordered Routing Algorithm (TORA) protocols for mesh network with a single source. Ad Hoc on Demand Distance Vector is a reactive protocol and Temporary Ordered Routing Algorithm is on-demand reactive protocol which is the hybrid protocol. Previous research did shown that AODV is better than TORA for a larger nodes that is more than 30 nodes an some says that TORA is better for smaller nodes environment. In wireless ad hoc network, the nodes must be able to interconnect each other to establish the link or connection from source to the destination. The results show that AODV outperformed TORA in the throughput but AODV had higher delay than TORA. After the analysis has been done, this paper shows that AODV outperforms TORA in the wireless sensor network only with a single source and destination which less than 30 nodes in a network.

Applications of artificial intelligence (AI) models have been massively explored for various engineering and sciences domains over the past two decades. Their capacity in modeling complex problems confirmed and motivated researchers to explore their merit in different disciplines. The use of two AI-models (probabilistic neural network and multilayer perceptron neural network) for the estimation of two different water quality indicators (namely dissolved oxygen (DO) and five days biochemical oxygen demand (BOD₅)) were reported in this study. The WQ parameters estimation based on four input modelling scenarios was adopted. Monthly water quality parameters data for the duration from January 2006 to December 2015 were used as the input data for the building of the prediction model. The proposed modelling was established utilizing many physical and chemical variables, such as turbidity, calcium (Ca), pH, temperature (T), total dissolved solids (TDS), Sulfate (SO₄), total suspended solids (TSS), and alkalinity as the input variables. The proposed models were evaluated for performance using different statistical metrics and the evaluation results showed that the performance of the proposed models in terms of the estimation accuracy increases with the addition of more input variables in some cases. The performances of PNN model were superior to MLPNN model with estimation both DO and BOD parameters. The study concluded that the PNN model is a good tool for estimating the WQ parameters. The optimal evaluation indicators for PNN in predicting BOD are (R² = 0.93, RMSE = 0.231 and MAE = 0.197). The best performance indicators for PNN in predicting Do are (R² = 0.94, RMSE = 0.222 and MAE = 0.175).