Engineering Commons^™

Recent Technical Breakthroughs Enable Smart Manufacturing: A Review, Manish Kumar, Shanu Pandey, Kanchan Upadhyay, Pritibala Taunk, Raunak Kumar Tamrakar

Al-Bahir Journal for Engineering and Pure Sciences

In human culture, machine learning has long been used to address complicated issues. Machine learning is successful because of the help provided by computational power and sensing technology. Data-driven strategies and the development of artificial intelligence will soon have a significant impact on the industry. Common examples include search engines, picture recognition, biometrics, speech and handwriting recognition, natural language processing, as well as medical diagnostics and credit scores. It is obvious that when artificial intelligence permeates our globe and, more precisely, our lives, numerous challenges will become public. According to predictions, Industry 4.0 or Smart Manufacturing will be the next …

Detection Of Data Over Wireless Mobile Channels Based On Maximum Likelihood Technique, Mohd Israil

Al-Bahir Journal for Engineering and Pure Sciences

Next generation wireless systems are characterized by very high transmission bit rates which gives rise to severe Intersymbol interference (ISI) and this makes the detection process very challenging. Hence, assessment of performance of near-optimal detectors like Near Maximum Likelihood Detectors (NMLD) over such channels assumes great importance. This paper deals with the detection of data in the presence of Noise and ISI with NMLD. Performance improvement of NMLD, as compared to nonlinear equalization, has been assessed in terms of BER versus SNR curves obtained through computer simulation. A number of different cases of mobile radio channels have been simulated in …

Forecasting The Demand For Asphalt Concrete In Karbala Governorate, Ghayath Ali, Sawsan R Mohammad, Shakir Al-Busaltan

Al-Bahir Journal for Engineering and Pure Sciences

The challenge of forecasting asphalt concrete demand in the Karbala governorate is the subject of this study. Population and number of vehicles in Karbala are considered among the independent (explanatory) variables taken into consideration. These explanatory variables ' historical data have been gathered and examined. The historical data of each explanatory variable has been linked to the historical demand for asphalt concrete in the governorate using regression techniques in the Statistical Package for the Social Sciences (SPSS) program version 26.

A model to forecast the governorate's demand for asphalt concrete has been developed based on these factors. The forecasts have …