Computer and System Engineering

Scheduling with constrained resources, particularly skilled labor, is a major challenge for almost all construction projects. In the literature, various techniques have been developed to reduce consequent project delay of constrained resources. Most of these techniques assume single-skilled resources and use heuristic rules to decide which activity will receive the resource first and which ones to delay. To improve existing solutions, this paper introduces some modifications to heuristic resource-scheduling solutions, considering multiskilled resources. The proposed approach stores and utilizes information about the resource(s) that can be substituted when there is a shortage in another one. Using this information, less utilized resources can be combined to substitute the shortages in constrained resources during the shortage period, taking into consideration the loss in work productivity. To automate the proposed algorithm, a macroprogram has been written on a commercial scheduling software. An example application is presented to show the improved results of the proposed approach over existing heuristics. The use of the proposed approach as a better resource management tool within the construction industry is then discussed.

This paper aims to analyze the heart electrocardiograph (ECG) to diagnose the heart performance and predict any future complications. The authors utilize the MIT BIH database to obtain the original data. The discrete wavelet transform (DWT) is used to decompose the ECG signal and to reconstruct it. The means of different performance measures of the heart performance (QRS,RR,ST,QT,PR) are tested for their significance before comparing them to the limits of normal performance. Any abnormal measure is used to diagnose the corresponding disease(s). Prediction techniques are used to expect any future complications regarding the abnormal measure(s). Three methods of prediction are applied: the Linear Prediction Method (LPM) and the Grid Partitioning and Fuzzy c-mean Clustering based on Neuro-Fuzzy prediction.

Heart performance problems are always detected after analyzing the heart electrocardiogram (ECG) signal. In case of abnormal measures of the heart performance are diagnosed from the analysis, a prediction of any future complications is always needed to help doctors to follow up the case. This paper describes the application of machine learning techniques for the prediction of heart future complications. Four techniques are explained and their results are compared. These are: the Linear Prediction Method (LPM), the Grid Partitioning, Fuzzy c-mean based on Neuro-Fuzzy prediction and also GMDH-PNN.

Brain tumors are intensively studied and many techniques and algorithms have been proposed to extract the features of the brain MRI images and diagnose the tumors. The different techniques are distinguished and favored based on their accuracy and speed. This led to apply optimization methods to improve them. In this paper, we apply three methaheuristic optimization methods which recently gained much interest. These are: the Binary Particle Swarm Optimization (B-PSO), the Ant colony Travel salesman problem (ACO-TSP) and the Artificial Bee colony optimization (ABCO-BFS). These methods are carried out in two main steps. The first step is the segmentation process of the brain MRI images using K-means cluster and the second steps is the features extraction from them. We compare the results of these methods to obtain the best solution for feature extraction. The group method of data handling (GMDH) is applied to classify the extracted features for the tumors diagnosis. Prediction is also carried on by applying the GMDH method for any future complications. The accuracy of classification and prediction is obtained to be range from 88.9% to 98.8%.

The Neural Gas Networks (NGN) is considered one of the most and popular algorithms in most researches. It is considered the fastest neural and it is more practical such as the other neural methods. Recently, it has achieved the fast and best result in a lot of researches. In this paper, we propose NGN to extract the features of brain cancer detection and then we are classifying the features by using Adaptive Neuro Fuzzy Inference system (ANFIS). Actually, this method consists of two main parts, the first part is the segmentation process using K-means cluster and the second part is based on extracting features from MRI cancer brain images based on NGN. After that, we applied Adaptive Neuro Fuzzy Inference System (ANFIS) for classification and prediction. Finally, the ANFIS method achieved accuracy from 85% up to 96%.

This paper aims to design an optimizer followed by a Kawahara filter for optimal classification and prediction of employees' performance. The algorithm starts by processing data by a modified K-means technique as a hierarchical clustering method to quickly obtain the best features of employees to reach their best performance. The work of this paper consists of two parts. The first part is based on collecting data of employees to calculate and illustrate the performance of each employee. The second part is based on the classification and prediction techniques of the employee performance. This model is designed to help companies in their decisions about the employees' performance. The classification and prediction algorithms use the Gradient Boosting Tree classifier to classify and predict the features. Results of the paper give the percentage of employees which are expected to leave the company after predicting their performance for the coming years. Results also show that the Grasshopper Optimization, followed by "KF" with the Gradient Boosting Tree as classifier and predictor, is characterized by a high accuracy. The proposed algorithm is compared with other known techniques where our results are fund to be superior.

With the growth of social media, there is a need to analyse the user-generated content; especially the text reviews. Online text reviews have a lot of potential and opportunities for both users and business owners. Many researches target analysing text reviews extracting useful info especially Named Entity Recognition. In this research, I focus on extracting food and dish names as a named entity. With the lack of labelled data, I try to overcome the cold start and avoid manual labelling by building a lookup table from a dictionary. I work with Yelp dataset, going through each text review, using each noun as a candidate, label the positive samples using the aforementioned lookup table, then using Positive Unlabelled learning techniques to recognise more entities within the unlabelled data, by predicting the probability for each candidate. I considered the surrounding words; preceding and following in building the model, as well as Part of Speech tag for each. To eliminate duplicates ...

DESIRED OUTCOMES students should: Understand the powerful concept of the root locus and its role in control system design.  Know how to obtain a root locus plot by sketching or using computers.  Be familiar with the PID controller as a key element of many feedback systems.  Recognize the role of root locus plots in parameter design and system sensitivity analysis.  Be able to design controllers to meet desired specifications using root locus methods. 8  For investigating the performance of a system, we have to solve the output response of the system. But there are two defects when we do this:  We can utilize the Root-locus technique to make up for the defects above. (1) It is a difficult thing to solve the output response of a systemespecially for a higher order system. (2) We can't easily investigate the changes of a system's performance from the time-domain solution of the system's output response when one or two parameters of the system varies in a given range, or, when one or more devices are added to the system to improve the system's. when some parameter of the system varies in a given range; or when some device is added to the system. The root-locus technique can be conveniently used for designing and analyzing the control systems.