A. Araba, Z. Memon, Musab Alhawat, Mumtaz Ali, A. Milad
{"title":"Estimation at Completion in Civil Engineering Projects: Review of Regression and Soft Computing Models","authors":"A. Araba, Z. Memon, Musab Alhawat, Mumtaz Ali, A. Milad","doi":"10.51526/kbes.2021.2.2.1-12","DOIUrl":"https://doi.org/10.51526/kbes.2021.2.2.1-12","url":null,"abstract":"Construction projects are usually associated with several challenges owing to the varying process during the project lifetime. Hence, the final cost of any civil engineering project is influenced by many factors. There are numerous ways of determining the final cost of a project, however, the most essential approach is the Estimate at Completion (EAC) technique. This technique is mostly favored because it considers the probability of risks and project performance. Furthermore, EAC helps project managers in the definition and determination of the critical problems expected during the project period and the likely solutions toward these problems. In this review research, the basic empirical, regression and advanced soft computing methodologies adopted for the EAC computation, were surveyed and reported in detail. The review established on the base to recognize the modern advancement of the soft computing in computing the EAC with accurate, reliable and robust manner. The review was highlighted the main literature limitation, current status and possible future direction.","PeriodicalId":254108,"journal":{"name":"Knowledge-Based Engineering and Sciences","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On The Evaluation of Water Quality Index: Case Study of Euphrates River, Iraq","authors":"O. Khaleefa, A. H. Kamel","doi":"10.51526/kbes.2021.2.2.35-43","DOIUrl":"https://doi.org/10.51526/kbes.2021.2.2.35-43","url":null,"abstract":"This study is used the water quality index (WQI), which is generated by combining several water quality parameters. This index gives a helpful representation of overall water quality for the public and all intended applications, and it demonstrates that pollution is beneficial in water quality management and decision-making. The Euphrates River was assessed in order to determine the quality of drinking water. The Euphrates River was assessed for drinking water quality using the WQI, which includes ten physicochemical water quality criteria. This was achieved by submitting comprehensive physicochemical analysis of water samples collected from 5 stations in the city of Hit-Iraq during 2020-2021. The ten physicochemical parameters included: pH value, Nitrate (NO3), Sulphate (SO4), Turbidity, temperature, Calcium (Ca), Magnesium (Mg), sodium (Na), electric conductivity (EC) and Total Dissolved Solids (TDS). This was accomplished by submitting a full physicochemical analysis of water samples obtained from 5 sites in Hit, Iraq, between 2020 and 2021. The results of the present study show, the total average WQI was 110,156. The high WQI achieved is caused by the high TDS and magnesium concentration due to the different human activities along the river reach. The Euphrates River quality is classified as 'very poor quality' with a minimum WQI of 97.85 in June and 121.75 in November.","PeriodicalId":254108,"journal":{"name":"Knowledge-Based Engineering and Sciences","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130498082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementing Recommender Systems using Machine Learning and Knowledge Discovery Tools","authors":"Mohammad Zahrawi, Ahmad Mohammad","doi":"10.51526/kbes.2021.2.2.44-53","DOIUrl":"https://doi.org/10.51526/kbes.2021.2.2.44-53","url":null,"abstract":"The current research offers a novel use of machine learning strategies to create a recommendation system. At recent era, recommender systems (RSs) have been used widely in e-commerce, entertainment purposes, and search engines. In more general, RSs are set of algorithms designed to recommend relevant items to users (movies to watch, books to read, products to buy, songs to listen, and others). This article discovers the different characteristics and features of many approaches used for recommendation systems in order to filter and prioritize the relevant information and work as a compass for searching. Recommender engines are crucial in some companies as they can create a big amount of income when they are effective or be a way to stand out remarkably from other rivals. As a proof of the importance of recommender engine, it can be stated that Netflix arrange a challenge (the “Netflix prize”) where the mission was to create a recommender engine that achieves better than its own algorithm with a prize of 1 million dollars to win.","PeriodicalId":254108,"journal":{"name":"Knowledge-Based Engineering and Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128120997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
