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Salam Fraihat
- Professor
- Ext: 5420
Prof. Salam Fraihat received the M.Sc. and Ph.D. degrees in computer science from Aix Marseille University, France, in 2004 and 2010, respectively. He is currently a Professor at the College of Engineering and Information Technology, Ajman University, United Arab Emirates. Prior to that, he was the Head of the Computer Science Department, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, and the Head of the Software Engineering Department, Al-Ahliyya Amman University, Amman, Jordan. His research interests include applied research solving challenging problems in data science and deep learning, advanced theoretical knowledge, applied hands-on experience in machine learning (ML), and text/image/speech processing.
Education
- Ph.D. School of Computer Science, AIX-Marseille University, France, February 2007 until March 2010.
- M.Sc. School of Computer Science, AIX-Marseille University, France, September 2004 until July 2005.
- B.Sc. Computer Engineering, National High School of Informatics (ESI), Algiers, Algeria, September 1998 until July 2004.
Ph.D. School of Computer Science, AIX-Marseille University, France, February 2007 until March 2010.
M.Sc. School of Computer Science, AIX-Marseille University, France, September 2004 until July 2005.
B.Sc. Computer Engineering, National High School of Informatics (ESI), Algiers, Algeria, September 1998 until July 2004.
Experience
- Associate Professor, Information Technolog Department, Ajman University, UAE, 2021-2024
- Head of the Computer Science Department, Princess Sumaya University for Technology PSUT, Amman, Jordan, 2019-2020
- Assistant Professor in Computer Science/Data Science Departments, Princess Sumaya University for Technology PSUT, Amman, Jordan, 2018-2021
- Head of Software Engineering Department, Al-Ahliyyah Amman University, Amman, Jordan, 2018-2019
- Associate Professor in Computer Science, Al-Ahliyyah Amman University, Amman, Jordan, 2017-2019
- Assistant Professor in Computer Science, Al-Ahliyyah Amman University, Amman, Jordan, 2012-2017
- Faculty member in Information Technologies Faculty, UTEC (University of Technology Teaching consular), France, 2010-2012
- Teaching and Research Assistant in Information Technologies Faculty, Aix-Marseille University and Toulon University, 2008-2010
- Research Engineer, CNRS - The National Center for Scientific Research, France,2007-2008
- Professor, Information Technolog Department, Ajman University, UAE, 2024-Now
Associate Professor, Information Technolog Department, Ajman University, UAE, 2021-2024
Head of the Computer Science Department, Princess Sumaya University for Technology PSUT, Amman, Jordan, 2019-2020
Assistant Professor in Computer Science/Data Science Departments, Princess Sumaya University for Technology PSUT, Amman, Jordan, 2018-2021
Head of Software Engineering Department, Al-Ahliyyah Amman University, Amman, Jordan, 2018-2019
Associate Professor in Computer Science, Al-Ahliyyah Amman University, Amman, Jordan, 2017-2019
Assistant Professor in Computer Science, Al-Ahliyyah Amman University, Amman, Jordan, 2012-2017
Faculty member in Information Technologies Faculty, UTEC (University of Technology Teaching consular), France, 2010-2012
Teaching and Research Assistant in Information Technologies Faculty, Aix-Marseille University and Toulon University, 2008-2010
Research Engineer, CNRS - The National Center for Scientific Research, France,2007-2008
Professor, Information Technolog Department, Ajman University, UAE, 2024-Now
Teaching Area
- Data Science
- Deep Learning
- Machine Learning
- Business Intelligence
Data Science
Deep Learning
Machine Learning
Business Intelligence
Research
- Deep Learning
- Text/Image/Speech processing
- Big Data analytics
- Data Science
Deep Learning
Text/Image/Speech processing
Big Data analytics
Data Science
Publications
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Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study
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Telecom Big Data: Social Media Sentiment Analysis
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Meteorological data analytic system: Descriptive and predictive analysis
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Business Intelligence & Data Analytics Framework: Case Study of Humanitarian Organizations Refugees Registration System
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Business intelligence and data analytics system for mobile money
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A framework of business intelligence solution for real estates analysis
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Data analytics and business intelligence framework for stock market trading
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The Implementation of Mobile Technologies in Higher Education: A Mobile Application for University Course Advising.
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A Semantic Multi-Agent Architecture for Multilingual Machine Translation
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ONTOLOGY-CONCEPTS WEIGHTING FOR ENHANCED SEMANTIC CLASSIFICATION OF DOCUMENTS
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An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems
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JournalMalak Fraihat, Salam Fraihat, Mohammed Awad, Mouhammd AlKasassbeh, An efficient enhanced k-means clustering algorithm for best offer prediction in telecom, International Journal of Electrical and Computer Engineering, Jan 2022
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JournalBushra Alhijawi, Arafat Awajan, Salam Fraihat, Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives, ACM Computing Surveys, Dec 2022
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JournalBushra Alhijawi, Salam Fraihat, Arafat Awajan, Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems, International Journal of Information Technology, Mar 2023
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JournalAhmad Ababneh, Yousef Sanjalawe, Salam Fraihat, Salam Al-E'mari, Hamzah Alqudah, Orbit Weighting Scheme in the Context of Vector Space Information Retrieval., Computers, Materials & Continua, Jan 2024
Business Intelligence Framework Design and Implementation: A Real-estate Market Case Study
This article builds on previous work in the area of real-world applications of Business Intelligence (BI) technology. It illustrates the analysis, modeling, and framework design of a BI solution with high data quality to provide reliable analytics and decision support in the Jordanian real estate market. The motivation is to provide analytics dashboards to potential investors about specific segments or units in the market. The article ekxplains the design of a BI solution, including background market and technology investigation, problem domain requirements, solution architecture modeling, design and testing, and the usability of descriptive and predictive features. The resulting framework provides an effective BI solution with user-friendly market insights for investors with little or no market knowledge. The solution features predictive analytics based on established Machine Learning modeling techniques, analyzed and …
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Telecom Big Data: Social Media Sentiment Analysis
Telecom Big Data: Social Media Sentiment Analysis
About the journal
Meteorological data analytic system: Descriptive and predictive analysis
Weather can be described as the status of atmospheric conditions at a specific time. on the other hand, the climate is the weather’s status over a long period. both are very important for people’s life management on multiple levels. Weather prediction is a complicated process that requires input from experts. This paper describes a weather business intelligence solution starting from requirements gathering and analysis all the way to the creation of a dashboard with weather prediction capabilities based on a machine learning technique to fulfill the business needs.
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Business Intelligence & Data Analytics Framework: Case Study of Humanitarian Organizations Refugees Registration System
Business Intelligence and Analytics has gained prominent focus among organizations with information systems that collect and process vast amounts of data. Voluminous, unprocessed data does not lend itself to offering useful insights for businesses, especially with basic statistical methods and traditional reporting techniques. In this work, we design a Business Intelligence and Data Analytics Framework for Refugee Registration System serving over six million refugees to collect, collate and filter demographic data. The proposed reporting mechanism leverages the power of interactive dashboards to offer informative and intuitive reports and visualizations that are accessible and interpretable by stakeholders.
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Business intelligence and data analytics system for mobile money
Developing a Business Intelligence system has a major benefit for business owners as it supports and helps with decision making and strategy development, where a well-designed business intelligence system enables businesses to have a full and holistic view of the historical, current and future insights based on the available data. In this paper we develop a business intelligence system that can be deployed for a mobile money system to serve different user groups based on the needs and business goal of each group.
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A framework of business intelligence solution for real estates analysis
Real estate is one of the essential and challenging fields in the market which reflects the economy, and it needs constant improvement. Business intelligence nowadays plays a significant role in enhancing the process of decision making and risk management in many different fields. One of the promising fields is the real estate investment market. This paper proposes a framework for an effective BI solution for analyzing the real estate market and estimating the price of the properties. The building of the BI solution, which passes through multiple phases is demonstrated.
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Data analytics and business intelligence framework for stock market trading
Business intelligence is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. The efficiency of making decisions can increase significantly using business intelligence solutions, by taking advantage of the existing historical or realtime data of the business. Trading in stock markets is imminent with taking risks of losing money, which requires extensive experience in the market, to make efficient decisions. In this paper, we propose a framework that makes use of stock prices historical data, to help investors in making more efficient trading decisions.
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The Implementation of Mobile Technologies in Higher Education: A Mobile Application for University Course Advising.
Mobile technologies have become the most rapidly growing and adopted technology in recent years. Currently, many higher education institutions are using mobile technologies, due to their portability and accessibility, to support a variety of activities in the education process. Course advising is an important part of the education process and it plays an essential role in students’ academic success. However, course advising is a challenging task due to the intensive human effort required from advisors; the unavailability of committed advisors due to other academic commitments; the advisors lack of knowledge; the time-consuming nature of this task; and the unavailability of related information on academic curriculum to the advisors. Nevertheless, such problems make the use of an automated course advising system desirable and helpful. This paper presents the design and implementation of a mobile application for university course advising, called m-advisor, that can be used to reduce the time and effort for both the students and advisors during the course advising process at the beginning of each academic semester. The evaluation results of the m-advisor application revealed that informative advices for students can be given on which appropriate courses, that can fit their needs in accordance with the requirements of the student’s academic program, to register in the upcoming semester.
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A Semantic Multi-Agent Architecture for Multilingual Machine Translation
Machine Translation is the use of computerized methods to automate all or part of the translation process from one natural language into another. Machine Translation systems used to overcome the language barriers, for example, by making digital information understandable to people across the world in minimum amount of time. A Multiagent system is a software system that consists of multiple active, task-oriented and autonomous intelligent agents. Such agents can communicate and coordinate between each other in order to produce high quality solutions to complex problems in different domains. The semantic web is realized by adding semantics to the web in which it gives well-defined semantic meaning of information. It makes it possible to facilitate the representation, interpretation, sharing, searching, and reusing of information. This paper proposes a Semantic Multi-Agent Architecture for Multilingual
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ONTOLOGY-CONCEPTS WEIGHTING FOR ENHANCED SEMANTIC CLASSIFICATION OF DOCUMENTS
Automatic document classification has become increasingly important and difficult due to the large scale of the electronic documents used in the last years. Traditional information retrieval systems are based on the extraction of keywords from documents; these keywords serve as a basis for documents classification. This paper proposes a new semantic approach for documents classification. Specifically, our approach captures, in addition to the keywords frequency, the meaning of these keywords in documents using domain ontology.
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An Item-based Multi-Criteria Collaborative Filtering Algorithm for Personalized Recommender Systems
Recommender Systems are used to mitigate the information overload problem in different domains by providing personalized recommendations for particular users based on their implicit and explicit preferences. However, Item-based Collaborative Filtering (CF) techniques, as the most popular techniques of recommender systems, suffer from sparsity and new item limitations which result in producing inaccurate recommendations. The use of items’ semantic information besides the inclusion of multi-criteria ratings can successfully alleviate such problems and generate more accurate recommendations. This paper proposes an Item-based Multi-Criteria Collaborative Filtering algorithm that integrates the items’ semantic information and multi-criteria ratings of items to lessen known limitations of the item-based CF techniques
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Malak Fraihat, Salam Fraihat, Mohammed Awad, Mouhammd AlKasassbeh, An efficient enhanced k-means clustering algorithm for best offer prediction in telecom, International Journal of Electrical and Computer Engineering, Jan 2022
Telecom companies usually offer several rate plans or bundles to satisfy the customers’ different needs. Finding and recommending the best offer that perfectly matches the customer’s needs is crucial in maintaining customer loyalty and the company’s revenue in the long run. This paper presents an effective method of detecting a group of customers who have the potential to upgrade their telecom package. The used data is an actual dataset extracted from call detail records (CDRs) of a telecom operator. The method utilizes an enhanced k-means clustering model based on customer profiling. The results show that the proposed k-means-based clustering algorithm more effectively identifies potential customers willing to upgrade to a higher tier package compared to the traditional k-means algorithm.
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Bushra Alhijawi, Arafat Awajan, Salam Fraihat, Survey on the objectives of recommender systems: Measures, solutions, evaluation methodology, and new perspectives, ACM Computing Surveys, Dec 2022
Recently, recommender systems have played an increasingly important role in a wide variety of commercial applications to help users find favourite products. Research in the recommender system field has traditionally focused on the accuracy of predictions and the relevance of recommendations. However, other recommendation quality measures may have a significant impact on the overall performance of a recommender system and the satisfaction of users. Hence, researchers’ attention in this field has recently shifted to include other recommender system objectives. This article aims to provide a comprehensive review of recent research efforts on recommender systems based on the objectives achieved: relevance, diversity, novelty, coverage, and serendipity. In addition, the definitions and measures associated with these objectives are reviewed. Furthermore, the article surveys the evaluation methodology used to measure the impact of the main challenges on performance and the new applications of the recommender system. Finally, new perspectives, open issues, and future directions are provided to develop the field.
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Bushra Alhijawi, Salam Fraihat, Arafat Awajan, Multi-factor ranking method for trading-off accuracy, diversity, novelty, and coverage of recommender systems, International Journal of Information Technology, Mar 2023
Collaborative filtering (CF) is one of the most popular and commonly used recommendation methods. Currently, most rating prediction CF methods select top-N recommendations based on their predicted rating. Thus, CF achieved a remarkable prediction accuracy, but it has shown modest performance in terms of novelty, diversity, and coverage. This research study presents a new efficient ranking method for CF, namely, multi-factor ranking (MF-R). The proposed method adopts two factors to rank items: the predicted rating and popularity of items. MF-R aims to select recommendations achieving accuracy, novelty, diversity, and coverage objectives. A set of experiments are conducted to compare MF-R with the traditional ranking method. Three benchmark datasets, MovieLens-Latest, MovieLens-100 K, and HotelExpedia, are utilized. Both ranking methods are integrated with different single-criterion and multi-criteria CF techniques. On average, MF-R achieved 26%, 496%, 39%, and 0.9% improvements in terms of precision, novelty, coverage, and diversity, respectively. The results demonstrate the MF-R capability to achieve the four objectives of RS irrespective of the recommendation size. Besides, the results show that MF-R degrades the effect of the long-tail challenge.
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Ahmad Ababneh, Yousef Sanjalawe, Salam Fraihat, Salam Al-E'mari, Hamzah Alqudah, Orbit Weighting Scheme in the Context of Vector Space Information Retrieval., Computers, Materials & Continua, Jan 2024
This study introduces the Orbit Weighting Scheme (OWS), a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval (IR) models, which have traditionally relied on weighting schemes like tf-idf and BM25. These conventional methods often struggle with accurately capturing document relevance, leading to inefficiencies in both retrieval performance and index size management. OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space, emphasizing term relationships and distribution patterns overlooked by existing models. Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall, Precision, Interpolated Average Precision (IAP), and Mean Average Precision (MAP). Additionally, we assess OWS’s effectiveness in reducing the inverted index size, crucial for model efficiency. We compare OWS-based retrieval models against others using different schemes, including tf-idf variations and BM25Delta. Results reveal OWS’s superiority, achieving a 54% Recall and 81% MAP, and a notable 38% reduction in the inverted index size. This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.
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Memberships, Awards and Honors
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.