Phone : +964 (750) 378-8211

Birth Date: 1983-06-28

Nationality: Iraq

Address: Nawro city

Mohammed Falah Mohammed

Assistant Professor

Department of Computer Science


Speciality
Computational Intelligence
Area Interest
Interests include fuzzy neural networks multiagent systems and pattern classification.
Teaching Materials
VISUAL PROGRAMMING

Mohammed Falah received his BEng degree in computer engineering from the Technical College of Mosul, Mosul, Iraq, in 2006, and the M.Sc. degree in wireless and mobile system and Ph.D. degree in computational intelligence from the School of Electrical and Electronic Engineering, University of Science Malaysia, NibongTebal, Malaysia, in 2010 and 2014, respectively. His current research interests include fuzzy neural networks, multiagent systems, and pattern classification. I employed as Senior Lecturer/Assistant professor with the Faculty of Computing, College of Computing and Applied Sciences, University Malaysia Pahang, Malaysia between 16/10/2014 to 1/9/2019. After that, I employed as an Assistant professor with the Department of Computer Science, University of Zakho, Kurdistan Region, Iraq from 1/9/2019 to 1/7/2020.

Languages

1

Arabic (Native)

2

English (Proficient)

Skills

1

Programming Languages

1. JAVA 2. MATLAB 3. C 4. C++ 5. HTML 6. CSS 7. JavaScript 8. VB.net

Education

2010 – 2014

Ph.D.

Computational Intelligence

Universiti Sains Malaysia (USM)

PhD. in Individual and Ensemble Pattern Classification Models Using Enhanced Fuzzy Min-Max Neural Networks - Computational Intelligence - Electrical and Electronic Engineering - Universiti Sains Malaysia (USM)

2007 – 2010

M.S.

Wireless and Mobile System

Universiti Sains Malaysia

Ms. Sc. in Development of Java-Based RFID API for Heterogeneous RFID System- Wireless and Mobile System - Electrical and Electronic Engineering - Universiti Sains Malaysia (USM) / 2010.

Academic Title

2014-10-16

Assistant Professor

Experience

2019-09-01 – 2020-07-01

Assistant professor

University of Zakho

Kurdistan Region, Iraq

Employed as Assistant professor with the with the Department of Computer Science, University of Zakho, Kurdistan Region, Iraq since 1/9/2019 to 1/7/2020.

2014-10-16 – 2019-09-01

Assistant professor

University Malaysia Pahang

Malaysia

Employed as Assistant professor with the Faculty of Computing, College of Computing and Applied Sciences, University Malaysia Pahang, Kuantan 26300, Malaysia between 16/10/2014 to 1/9/2019.

1970-01-01 – Present

Assistant professor

Cihan University

Duhok, Kurdistan Region, Iraq

Employed as Assistant professor (visitor) with the Department of Computer Science, Cihan University, Duhok, Kurdistan Region, Iraq.

1970-01-01 – Present

ICT Tutor

Organization

Kurdistan Region, Duhok, Iraq

ICT Tutor in an international organization

Membership

2015-01-01 – Present

IEEE senior member

Evaluation

2014-10-16

Positions and Responsibilities

1. Head of the Multimedia Computing and Computer Vision research group (MCVIS) 2. Coordinating and preparing teaching subject materials for the Mobile application subject for Universiti Malaysia Pahang and Muscat College of Oman. 3. Administration tasks: a. Member of the RESEARCH & DEVELOPMENT OF SCIENCE committee. b. Member of the TECHNICAL & DEVELOPMENT committee. c. Member of the Strategic Plan committee. d. Member of the QS Global Academic and Employer Surveys committee. 4. Supervising final year project students and postgraduate students (2 Ph.D.), 5. Evaluating postgraduate and undergraduate students’ dissertations. 6. Research funds as a leader and as a member, 7. Reviewing scientific papers and grants applications.

Training Course

2015-01-01 – 2020-07-01

Training and Activities Have Been Attended

Year: 2020 Chair Session in the International Conference on Computer Science and Software Engineering (CSASE), University of Duhok, Duhok, Kurdistan Region, Iraq, 2020. Year: 2019 1. Scientific knowledge sharing titled as Fuzzy Min-Max Neural Networks Variants, Applications and the current Challenges 2. Scientific knowledge sharing titled as Diagnosis of The Parkinson Disease Using Enhanced Fuzzy Min- Max Neural Network and OneR Attribute Evaluation Method Year: 2018 1. Strategic Planning meeting. 2. Scientific sharing session with delegates from TATI University College (Knowledge sharing) 3. Participated in the meeting on elective course reviewing for the IR 4.0. Year: 2017 1. Workshop on Rights Workers Webometric - KALAM2OCW4WEBO, VISTANA Hotel KL. 2. Colloquium event on industrial talk by CEO of Motorola solutions SND BHD by dr. Hari Narayanan, managing director Motorola 3. Presented a topic during March colloquium event with a title “Fuzzy Min Max (FMM) neural network and its variants for pattern classification”. 4. Workshop on introduction to swarm intelligence and its application to optimization. Year: 2016 1. Workshop on android mobile application development. 2. Strategic plan workshop pre FSKKP. 3. Workshop on postgraduate supervision and scope of how to master final projects. 4. Workshop on ICGPA. 5. Information skill class: online database (turn it in). 6. The international workshop on set theories in information systems- STIS. 7. Workshop on SCILAB to improve and enhance the research outcomes. 8. Workshop on computation international and soft computing using MATLAB. 9. Curriculum review meeting for master programs. 10. Workshop on FRGS grant. 11. Many other activities during 2015. Year: 2015 1. Intelligent data analysis using MATLAB. 2. The first international workshop on soft set-based decision making. 3. FSKKP meeting curriculum review for master program. 4. Many other activities during 2015.

Publication Journal

2019-12-17

Face Recognition Using Laplacian Completed Local Ternary Pattern (LapCLTP)

SpringerLink :

Nowadays, the face is one of the typical biometrics that has high-security technology in the biometrics field. In face recognition systems, feature extraction is considered as one of the important steps. In feature extraction, the important and interesting parts of the image are represented as a compact feature vector. Many features had been proposed in the image processing fields such as texture, colour, and shape. Recently, texture descriptors are playing an important and significant role as a local descriptor. Different types of texture descriptors had been proposed and used for face recognition task, such as Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Completed Local Ternary Pattern (CLTP). All these texture features have achieved good performances in terms of recognition accuracy. In this paper, we propose to improve the performance of the CLTP and use it for face recognition. A Laplacian Completed Local Ternary Pattern (LapCLTP) is proposed in this paper. The image is enhanced using a Laplacian filter for pre-processing image process before extracting the CLTP. JAFFR and YALE standard face datasets are used to investigate the performance of the LapCLTP. The experiment results showed that the LapCLTP outperformed the original CLTP in both datasets and achieved higher recognition accuracy. The LapCLTP achieved 99.24%, while CLTP achieved 98.78% with JAFFE dataset. IN YALE, the LapCLTP achieved 85.13%, while CLTP, only 84.46%.

2019-09-22

Analysis on Misclassification in Existing Contraction of Fuzzy Min–Max Models

Springer :

Fuzzy min–max (FMM) neural network is one of the most useful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in FMM models to improve classification accuracy. Hence, this research aims to analyse the existence and execution procedure of addressing the misclassification of the contraction in the current FMM models. In this manner, practitioners and researchers are aided in selecting the convenient model that can address the misclassification of the contraction and improve the performance of models in producing accurate classification results. A total of 15 existing FMM models are identified and analysed in terms of the contraction problem. Results reveal that only five models can address the contraction misclassification problem. However, these models suffer from serious limitations, including the inability to detect all overlap cases, and increasing the network structure complexity. A new model is thus needed to address the specified limitations for increasing the pattern classification accuracy.

2019-09-06

A Refined Fuzzy Min–Max Neural Network With New Learning Procedures for Pattern Classification

IEEE Transactions on Fuzzy Systems :

The fuzzy min-max (FMM) neural network stands as a useful model for solving pattern classification problems. FMM has many important features, such as online learning and one-pass learning. It, however, has certain limitations, especially in its learning algorithm, which consists of the expansion, overlap test, and contraction procedures. This article proposes a refined fuzzy min-max (RFMM) neural network with new procedures for tackling the key limitations of FMM. RFMM has a number of contributions. First, a new expansion procedure for overcoming the problems of overlap leniency and irregularity of hyperbox expansion is introduced. It avoids the overlap cases between hyperboxes from different classes, reducing the number of overlap cases to one (containment case). Second, a new formula that simplifies the original rules in the overlap test is proposed. It has two important features: (i) identifying the overlap leniency problem during the expansion procedure; (ii) activating the contraction procedure to eliminate the containment case. Third, a new contraction procedure for overcoming the data distortion problem and providing more accurate decision boundaries for the contracted hyperboxes is proposed. Fourth, a new prediction strategy that combines both membership function and distance measure to prevent any possible random decision-making during the test stage is proposed. The performance of RFMM is evaluated with the UCI benchmark datasets. The results demonstrate the effectiveness of the proposed modifications in making RFMM a useful model for solving pattern classification problems, as compared with other existing FMM and non-FMM classifiers.

2019-04-18

A Critical Review on Selected Fuzzy Min-Max Neural Networks and Their Significance and Challenges in Pattern Classification

IEEE Access :

At present, pattern classification is one of the most important aspects of establishing machine intelligence systems for tackling decision-making processes. The fuzzy min-max (FMM) neural network combines the operations of an artificial neural network and fuzzy set theory into a common framework. FMM is considered one of the most useful neural networks for pattern classification. This paper aims to 1) analyze the FMM neural network in terms of its impact in addressing pattern classification problems; 2) examine models that are proposed based on the original FMM model (i.e., existing FMM-based variants); 3) identify the challenges associated with FMM and its variants, and; 4) discuss future trends and make recommendations for improvement. The review is conducted based on a methodical protocol. Through a rigorous searching and filtering process, the relevant studies are extracted and comprehensively analyzed to adequately address the defined research questions. The findings indicate that FMM plays a critical role in providing solutions to pattern classification issues. The FMM model and a number of FMM-based variants are identified and systematically analyzed with respect to their aims, improvements introduced and results achieved. In addition, FMM and its variants are critically analyzed with respect to their benefits and limitations. This paper shows that the existing FMM-based variants still encounter issues in terms of the learning process (expansion, overlap test, and contraction), which influence the classification performance. Based on the review findings, research opportunities are suggested to propose a new model to enhance the number of existing FMM models, particularly in terms of their learning process by minimizing hyperbox overlap pertaining to different classes as well as avoiding membership ambiguity of the overlapped region. In short, this review provides a comprehensive and critical reference for researchers and practitioners to leverage FMM and its variants for undertaking pattern classification tasks.

2018-08-17

Survey of fuzzy min–max neural network for pattern classification variants and applications

IEEE Transactions on Fuzzy Systems :

Over the last few decades, pattern classification has become one of the most important fields of artificial intelligence because it constitutes an essential component in many different real-world applications. Artificial neural networks and fuzzy logic are two most widely used models in pattern classification. To build an efficient and powerful model, researchers have introduced hybrid models that combine both fuzzy logic and artificial neural networks. Among the hybrid models, the fuzzy min-max (FMM) neural network has been proven to be a premier model for undertaking pattern classification problems. While FMM is useful in terms of its capability of online learning, it suffers from several limitations in the learning procedure. Therefore, over the past years, researchers have proposed numerous improvements to overcome the limitations of the original FMM model. This paper carries out a comprehensive survey of the developments conducted on the FMM model for pattern classification. In order to assist recent researchers in selecting the most suitable FMM variant and to provide proper guidance for future developments, this study divides the variants of FMM into two main board categories, namely FMM variants with and without contraction. This division facilitates understanding of the developments conducted by researchers on the original FMM neural network, as well as provides the scope to identify the limitations that still exist in the FMM models. This paper also summarizes the use of FMM and its variants in solving different benchmark and real-world problems. Finally, the possible future trends are highlighted.

Conference

1

Analysis on Misclassification in Existing Contraction of Fuzzy Min–Max Models

the International Conference of Reliable Information and Communication Technology (IRICT 2019)

-
2019-09-23
2

Diagnosis of The Parkinson Disease Using Enhanced Fuzzy Min- Max Neural Network and OneR Attribute Evaluation Method

The International Conference on Advanced Science and Engineering (ICOASE 2019)

-
2019-04-04
3

An ensemble of enhanced fuzzy min-max neural networks for data classification

The International Conference on Electrical, Electronic, Communication and Control Engineering (ICEECC 2017)

-
2017-06-15

Awards


Cendekia Bitara Award in 2018

Cendekia Bitara Award in 2018: is an annual awards ceremony that was created by Universiti Malaysia Pahang (UMP) in 2009 to recognize excellence among staff. The word “Cendekia” refers to a person or a group of intellectuals or academic scholars. “Bitara” on the other hand, means remarkable, extraordinary, or exceptional, which are the qualities aspired by UMP for its staff. Therefore, “Cendekia Bitara Awards” is recognition of outstanding, unrivaled, scholarly achievements attained by UMP staff.


Exhibitions

1. Gold Medal in the British Design, Invention Show, Innovation & Technology Show (BIS 2017), from 18- 21 October 2017 for the invention of “Intelligent IOT Test List Generator.” 2. Gold Medal in ITEX form 11-13 May 2017 for the invention of “Intelligent IOT Test List Generator.” 3. Silver medal in Malaysia Technology Expo from 16-18 Feb. 2017 for the invention of “Modified Greedy Algorithm Strategy for Combinatorial Testing Problem with Constraints Supports.” 4. Bronze medal in Malaysia Technology Expo from 16-18 Feb. 2017 for the invention of “A new Hybrid Variable Interaction Strength Test Data Generation Strategy Based on Harmony Search Algorithm and Cuckoo Search Algorithm 5. Gold Medal in Creation, Innovation, Technology & Research Exposition (CITREX), 2017, Universiti Malaysia Pahang, Campus Gambang for the invention of “A new Hybrid Variable Interaction Strength Test Data Generation Strategy Based on Harmony Search Algorithm and Cuckoo Search Algorithm.” 6. Silver medal in Creation, Innovation, Technology & Research Exposition (CITREX) 2017, Universiti Malaysia Pahang, Campus Gambang for the invention of “New Fuzzy Min-Max Neural Network Techniques to Classifying Medical Data.” 7. Bronze medal in Creation, Innovation, Technology & Research Exposition (CITREX), 2017, Universiti Malaysia Pahang, Campus Gambang for the invention of “Designing a New Discriminative Texture Image Descriptor for Texture, Scene and Medical Image Classification.” 8. Gold Medal in Innovative Research, Invention & Application (I-RIA) 2016, Universiti Utara Malaysia, for the invention of the fuzzy min-max neural network based on new hyperbox selection rule for pattern classification. 9. Bronze medal in Creation, Innovation, Technology & Research Exposition (CITREX) 2016, Universiti Malaysia Pahang, Campus Gambang for the invention of Improving the Fuzzy Min-Max Neural Network with a New Hyperbox Expansion Rule for Pattern Classification. 10. Gold Medal in Malaysia Technology Expo 2010, Kuala Lumpur, for the invention of Gen 2 Passive RFID System with Range Variation for Digital Smart Community Platform. 11. Bronze Medal in IENA 2009, Nuremberg German for the invention of long range detection using 2.45GHz Contactless Active Integrated RFID system (CAIRFID). 12. Gold Medal in Nuclear Innovation Award 2009 for the invention of Radio Frequency Identification (RFID) System for Multiple Range Detection in Nuclear Power Plant Management. 13. Gold Medal in ITEX 2009, Kuala Lumpur Invention for RFIDTM- Reusable RFID Based Module for Networked Based Heterogeneous System. 14. Gold Medal and Best Award in Malaysia Technology Expo (MTE) 2009, Kuala Lumpur for the invention of Contactless Active Integrated RFID (CAIRFID).

Download CV