In this research the canal silt from the Kabul River Canal was recycled to manufacture artificial aggregate. For making artificial aggregates three batches were prepared each of dry weight of 100kg. The composition of artificial aggregate is Polyvinyl Alcohol (PVA), Ordinary Portland Cement (OPC) and Silt was mixed in different proportions (by weight). Three batches were made having values of PVA varying from 0, 1 and 2 percent, cement content was 20, 19 and 18 percent respectively, and the quantity of silt used was kept constant (80%) in each batch. Samples were prepared from these three different batches and were cured for 28 days. After completion of the curing time, they were crushed to get the desired course aggregate. A number of tests were performed to check the viability of artificial aggregates for being used as a lightweight aggregate. Shape and particles size distribution properties of artificial aggregates were as that of the natural aggregate but not satisfactory at high silt content. The reductions in the specific gravity of the artificial aggregate were also observed compared to natural aggregates.

Based on the statistical evaluation of accidents, the construction industry on an overall basis is full of dangers and risk oriented. The natures of the activities involved become hazardous when compliance with the safety rules and regulations are not properly followed, as a result high rates of accidents occur on the construction sites. In the metropolitan city of Karachi, Pakistan, a poor safety record has been observed in the construction industry. The primary reason existing for this situation is a non-preventive construction safety practice during execution of the projects. The secondary causes are dependent on the lack of resources, skills and interest of the concerned public sector agencies in enforcing the safety protocol regulations for the construction sites. In-addition, this situation also arises due to the lack of training in construction practices, child labor and a minimum awareness about the safety rules and regulations concerned with working on the construction sites. This necessitated a need to embark on a program of collecting data in connection with the accidents occurring on construction sites in Karachi; a six months program involving Construction Site Accidents (CSA) was accomplished. This involved different districts of Karachi region to be included in this study. In particular, CSA contributory factors relating to skilled, semi-skilled, and non-skilled labors were evaluated and marginalized. Through this study, it will be shown how by properly implementing the safety and operational measures, based on statistical conclusions, the rate of accidents can be significantly reduced.

This paper presents an automated multiple color making system. The various stages of work operations included in the adaptation of manually operated color mixing systems towards fully automated multiple colors making system. The proposed system can extract color features of an image and returns the intensities of Red, Green and Blue colors for a specified pixel in the given image. Since RGB Color Model is used for digital communications like televisions and computer monitors, and CMYK Color Model is used for printing and painting. So, for mixing purpose; the intensities of RGB are then converted into the intensities of Cyan, Magenta, Yellow and Black colors which then further converted into the form of electrical signals. Generally, from three primary colors CMY, a wide range of colors will be made with different proportions. The electrical signals will then be transmitted serially to our Logic Controller for mixing process, that sense the level of primary colors to be mixed and then mix colors proportionally in a container accordingly up to a fixed level in order to make different multiple colors of fixed quantity. Simulation results shows that the proposed methodology removes the chances like variation in quantity or inaccuracy which happened with the manually color sensing and mixing. It reduces the labor efforts and makes 7% more accurate with minimum error and achieve good retrieval performance.

Transportation problem is a model which is commonly used in data structure solving a problem (human problem solving due to the computational method) because all the humans are related to transportation in any type of manner. Normally, traditional mathematical procedures used for solving the problem which is quite lengthy, after the computational solving procedures it comes to the bit easier to solve it except traditional lengthy methods. The Genetic Algorithm (GA) is most powerful tool for solving transportation problem. It refines the better optimal solution, for enhancing the optimization of transportation problem, using genetic algorithms lots of the work already has been done. This paper discusses the impact of genetic algorithms on two different types of systems environments i.e., Single-Processor Environment Systems and Multi-Processor Environment Systems, for solving the transportation problem and found the best optimal solution time of both systems.

Tremendous proliferation in data generation in the past few years has paved the way for new research and the development of new and improved techniques and algorithms in different fields of science and education. Initially terms like educational data mining emerged as a branch of data mining borrowing techniques from its ancestor. The challenges brought about by this large and heterogeneous data are diverse and needs a greater serious technical treatment. New and emerging fields like learning analytics have been introduced to manage the complexities of this data deluge. Learning analytics deals with data in the context of learner and the learning environment to improve the overall learning experience. The ultimate aim of the field is to make use of the data about learners and their environments to gain insights into the learning process using some of the well-known techniques and algorithms from the fields of data mining and machine learning. The process involves collecting, analysis of data and reporting the results to understand and optimize the learning experience. The fields of data mining and academic analytics closely related to learning analytics. Systematic Literature Review (SLR) is a robust, organized and rigorous literature review and reporting process aimed at identifying, collecting and synthesizing the relevant literature on a research question according to specified criteria. The process is more unbiased and balanced by systematic sequence of steps. This paper presents a systematic literature review by first developing the systematic literature review protocol and then discussing the main findings of the literature review by especially focusing on the applications and uses of machine learning and data mining techniques in the domain of learning analytics.

Prediction of heart disease is a big concern now a days because everyone is busy and due to heavy load of work people do not give attention to their health. To diagnose a disease is a big challenge. The issue is to extract data that have some meaningful knowledge. For this purpose, data mining techniques are used to extract meaningful data. Decision Tree and ID3 are used to predict heart diseases. Many researchers and practitioners are familiar with prediction of heart diseases and wide range of techniques is available to predict disease. To address this problem, Decision Tree is used to predict the heart disease. In this study the collected data is pre-processed, Decision Tree algorithm and ID3 were then applied to predict the heart disease.

This study involves a discussion of power load factors, which are important terms for power systems. The advanced load factor is suited to the power system from the power consumption site to the power station and has been studied for various feeders as Tube well, New Petaro, LMC, OCF-I, OCF-II, Old Petaro and Allama I.I Qazi of 132/11 kV grid station Jamshoro. What needs to be researched is the relatively low load factor; due to the load factor, Hyderabad Power Supply Company faces many problems such as transformer tripping, conductive heat up equipments, breakdown of insulation & reactive power failure, all of which are bad main cause of distribution system. To analyse the load impact on the distribution losses of the 07 feeders on the 132/11 kV gird station during collection & to investigate the power loss caused by load factor, the calculated peak power & minimum load data loss sources at distinct load times. After analysis, it is proved that reducing peak load to minimize peak demand and minimum load during peak period, increase b/w average load during bed period and stabilize the power distribution system to improve load factor and increase unit load factor savings are done.