Social networks are regarded as a specific type of social interactions which include activities such as making somebody’s acquaintance, making friends, cooperating, sharing photos, beliefs, and emotions among individuals or groups of people. Cliques are a certain type of groups that include complete communications among all of its members. The issue of identifying the largest clique in the network is regarded as one of the notable challenges in this domain of study. Up to now, several studies have been conducted in this area and some methods have been proposed for solving the problem. Nevertheless, due to the NP-hard nature of the problem, the solutions proposed by the majority of different methods regarding large networks are not sufficiently desirable. In this paper, using a meta-heuristic method based on Artificial Bee Colony (ABC) optimization, a novel method for finding the largest clique in a given social network is proposed and simulated in Matlab on two dataset groups. The former group consists of 17 standard samples adopted from the literature whit know global optimal solutions, and the latter group includes 6 larger instances adopted from the Facebook social network. The simulation results of the first group indicated that the proposed algorithm managed to find optimal solutions in 16 out of 17 standard test cases. Furthermore, comparison of the results of the proposed method with Ant Colony Optimization (ACO) and the hybrid PS-ACO method on the second group revealed that the proposed algorithm was able to outperform these methods as the network size increases.  The evaluation of five DIMACS benchmark instances reveals the high performance in obtaining best-known solutions.

Visual SLAM (Simultaneous Localization and Mapping) is widely used in autonomous robots and vehicles for autonomous navigation. Trajectory estimation is one part of Visual SLAM. Trajectory estimation is needed to estimate camera position in order to align the real image locations. In this paper, we present a new framework for trajectory estimation aided by Monocular Visual Odometry. Our proposed method combines the feature points extracting and matching based on ORB (Oriented FAST and Rotated BRIEF) and PnP (Perspective-n-Point). Thus, it was used a Matlab® dynamic model and an OpenCV/C++   computer graphics platform to perform a very robust monocular Visual Odometry mechanism for trajectory estimation in outdoor environments. Our proposed method displays that meaningful depth estimation can be extracted and frame-to-frame image rotations can be successfully estimated and can be translated in large view even texture-less. The best key-points has been extracted from ORB key point detectors depend on their key-point response value. These extracted key points are used to decrease trajectory estimation errors. Finally, the robustness and high performance of our proposed method were verified on image sequences from public KITTI dataset.

The worldwide acceptability of wireless communication is due to its portability and flexibility. However, its performance is governed by the multipath propagation effects which make wireless communication modelling challenging. The existing technique being used to solve this propagation effects is based on Probability Density Function (PDF) which is inefficient in addressing diversity over combined Rayleigh and Rician (C_(Ray-Ric)) fading due to its complexity. Therefore, this paper aims to develop an approximated Moment Generating Function (MGF) for spatial diversity combining such as Equal Gain Combining (EGC) and Maximal Ratio Combining (MRC) over C_(Ray-Ric) fading channel. A MGF model in form of Taylor’s series is generated from the expected value of the C_(Ray-Ric) fading channels. The MGF is characterized using Amount of Fading (AF) and Bit Error Rate (BER) in term of Line of Sight (LOS) component ‘k’. The MGF is transformed into EGC and MRC, and were measured in terms of propagation paths (L). These are approximated using the Pade ́ Approximation (PA). The approximates obtained are used in the derivation of BER expression of M-ary Quadrature Amplitude Modulation (MQAM) and M-ary Phase Shift Keying (MPSK) in terms of Signal to Noise Ratio (SNR). The models are evaluated using AF and BER at different values of LOS to determine the performance of the diversity techniques. The results obtained show that as LOS component ‘k’ increases from 0, the Af and BER reduce indicating reduction in fading effects. Therefore, the models developed are effective in predicting the performance of diversity techniques and overcome the multipath effects associated with the wireless communication.

Web contents include text, image, and any visual element that represents in web applications. Users conduct web applications throughout visual contents; therefore, the contents should visible clearly and follow a strict contrast ratio to differentiate from the other contents of the application. The color contrast assists to visualize contents combining the contrast ratio between background and foreground. Whether the web contents not visible clearly or overpass to split its color contrast from the background shall be worthless, and in addition, the human brain and psychology have an impact of colors which lead physiologically effects such as feelings and senses. Numerous web applications existing on the web and some applications failed to follow the design principles of Human-Computer Interaction (HCI). In HCI, visualization is the most widespread research area and, in the context of visual interaction, the HCI facilitates and guides application design that to be user-centric. This research reveals the HCI for color effects on the human eye, brain, phycology, and contrast ratio. Also extended the existing standard minimum contrast ratio for the design of web contents in light and dark background and foreground following HCI principles. The extended ratio experimented on a web application contents to differentiate the accuracy between the existing and the extended ratio.

Nowadays, there exists a lot of information that can be handled from business transactions and scientific data and information retrieval is simply no longer enough for decision-making. In this paper will supervised machine learning technique is applied to the mine data warehouse for Enterprise Resource Planning (ERP) of the General Electricity Company of Libya (GECOL). This technique has been applied for the first time on the data of production, transportation and distribution departments. These data are in the form of purchase and work orders of operational material strategic equipment spare parts. This technique would extract prediction rules in order to assist the decision-makers of the company to make appropriate future decisions more easily and in less time. A supervised machine learning technique has been adopted and applied for the mining data warehouse. A well-known software package for data mining which is referred to as WEKA tool was adopted throughout this work. The WEKA tool is applied to the collected data from GECOL. The conducted experiments produce prediction models in the form set of rules in order to help responsible employees make the suitable, right and accurate future decision in a simple way and inappropriate time. The collected data were preprocessed to be prepared in a suitable format to be fed to the WEKA system. A set of experiments has been conducted on those data to obtain prediction models. These models are in the form of decision rules. The produced models were evaluated in terms of accuracy and production time. It can be concluded that the obtained results are very promising and encouraging.

Work-based learning is what equips students with practical skills. All higher learning institutions (HLIs) have a specified period of time for students to carry out field based practices in companies which are relevant to their fields of study. As the number of students in Tanzanian HLIs become larger, coordination and allocation of students to relevant companies is becoming tougher.  This study therefore intended to examine a better method to facilitate coordination and allocation of students to relevant companies through development of an online computer system. The research study to determine systems’ requirements was conducted in Arusha and Kilimanjaro regions by involving 62 HLI students, 3 HLIs and 5 companies. Data were collected using key informant interviews, observation and workshop. Both informative and descriptive information regarding current practices and desired features were collected and analyzed. It was found that, a platform for registering students’ profiles and companies’ information has advantages to all three main stakeholders who are HLIs, students and companies. Prior to actual implementation, collaborative prototype was designed using pencil software and shared to 5 users from each group of stakeholders to evaluate the tasks. Responses from users were used to refine the requirements and design the final prototype. The final prototype design was used to develop a Field Attachment Management System (FAMS). FAMS indicated to have improved access of students to relevant companies, reports generation, students’ assessment and follow-up conducted by HLIs to their students.