Mohammad H. Nadimi-Shahraki | Islamic Azad University, Najafabad Branch (original) (raw)

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Papers by Mohammad H. Nadimi-Shahraki

Research paper thumbnail of English-Persian Plagiarism Detection based on a Semantic Approach

Journal of AI and Data Mining, 2017

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works an... more Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-lingual plagiarism. In cross-lingual translation, writers meld a translation with their own words and ideas. Based on monolingual plagiarism detection methods, this paper ultimately intends to find a way to detect cross-lingual plagiarism. A framework called Multi-Lingual Plagiarism Detection (MLPD) has been presented for cross-lingual plagiarism analysis with ultimate objective of detection of plagiarism cases. English is the reference language and Persian materials are back translated using translation tools. The data for assessment of MLPD were obtained from English-Persian Mizan parallel cor...

Research paper thumbnail of DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization

Expert Systems with Applications, 2022

Research paper thumbnail of Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

Electronics, 2022

The optimal power flow (OPF) is a practical problem in a power system with complex characteristic... more The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solu...

Research paper thumbnail of MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

Symmetry, 2021

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on th... more The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search...

Research paper thumbnail of GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

Journal of Computational Science, 2022

Research paper thumbnail of A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Electronics, 2021

Real medical datasets usually consist of missing data with different patterns which decrease the ... more Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the f...

Research paper thumbnail of Full Source codes QANA-Quantum-based avian navigation optimizer algorithm

Research paper thumbnail of Full Source Code 2019 WBSCA A Binary Metaheuristic Algorithm for Wrapper Feature Selection

Research paper thumbnail of Enhancement of Bernstain-Search Differential Evolution Algorithm to Solve Constrained Engineering Problems(EBSD)_FullSourceCode.zip

Research paper thumbnail of Enhancement of Bernstain-Search Differential Evolution Algorithm to Solve Constrained Engineering Problems

Research paper thumbnail of A Binary Metaheuristic Algorithm for Wrapper Feature Selection

The classification accuracy is strongly affected by the quality of the input features used to bui... more The classification accuracy is strongly affected by the quality of the input features used to build a learned-model. Nowadays, datasets grow enormously both in size and number of features. One of the major difficulties confronted by huge datasets analysis is existing redundant, noisy, and irrelevant features, which may reduce the performance of the classifier. Feature selection is an important preprocessing task, which aims to select the most effective subset of features from the original dataset. Therefore, using feature selection method is essential for enhancing the classification accuracy and reducing the complexity of the built model. In this paper, a wrapper-based binary Sine Cosine Algorithm (SCA) for feature selection named WBSCA is proposed. The proposed algorithm was compared with three well-known binary algorithms over seven classification datasets from the UCI machine learning repository. The results show the competitive performance of the proposed algorithm in searching the optimal subset and selecting the salient feature.

Research paper thumbnail of Feature Selection Based on Whale Optimization Algorithm for Diseases Diagnosis

Medical datasets are mainly composed of countless irrelevant and redundant features in a series o... more Medical datasets are mainly composed of countless irrelevant and redundant features in a series of patient records. All these features are not required to obtain a medical decision-making process. On the other hand, the huge size of data is caused to increase the dimensionality and to reduce the performance of classifier. Recently, there have been many methods proposed to solve this problem and their results show that the feature selection can be an effective solution. The feature selection methods are mostly aim to reduce the size of data and enhance the efficiency of learning algorithms by eliminating the unrelated and redundant features. In this paper, a meta-heuristic algorithm is proposed named FSWOA for feature selection. This algorithm is based on the hunting methods of Humpback Whales consisting of three main steps: encircling prey, spiral bubble-net attacking and search for prey. The performance of proposed algorithm is evaluated conducted by four standard medical datasets: Pima Indians Diabetes, Original Wisconsin Breast Cancer, Statlog and Hepatitis. The results show that the proposed algorithm can reduce the dimensionality of medical datasets with acceptable accuracy for diseases diagnosis.

Research paper thumbnail of Multi-trial vector-based differential evolution algorithm_Full Source Code_2020_ASCO.zip

Research paper thumbnail of Binary Whale Optimization Algorithm_Full source code

Research paper thumbnail of Migration-Based Moth-Flame Optimization Algorithm

Processes, 2021

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates suffi... more Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising...

Research paper thumbnail of Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

Computer Methods in Applied Mechanics and Engineering, 2022

Research paper thumbnail of Full Source Code -Conscious Neighborhood-based Crow Search (CCSA) Algorithm

Research paper thumbnail of BINARY SINE COSINE ALGORITHMS FOR FEATURE Full Source Code 2019 ACIJ

Research paper thumbnail of Efficient Load Balancing Using Ant Colony Optimization

Workload and resource management are two essential functions provided in the service level of a G... more Workload and resource management are two essential functions provided in the service level of a Grid software infrastructure. Consistently, efficient load balancing algorithms are fundamentally important to improve the global throughput of these environments. Although previous works show that, ant colony algorithm works well for load balancing, the cost is a very important factor in this subject. In this paper, a grid load balancing algorithm is proposed by using an ant colony optimization which is able to consider shortest path, type of resource, and running speed of resource. The experimental results show that the proposed algorithm by using this ant colony optimization can reduce the cost of load balancing in comparison with standard algorithm DASUD.

Research paper thumbnail of An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy, 2021

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward th... more Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to ...

Research paper thumbnail of English-Persian Plagiarism Detection based on a Semantic Approach

Journal of AI and Data Mining, 2017

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works an... more Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-lingual plagiarism. In cross-lingual translation, writers meld a translation with their own words and ideas. Based on monolingual plagiarism detection methods, this paper ultimately intends to find a way to detect cross-lingual plagiarism. A framework called Multi-Lingual Plagiarism Detection (MLPD) has been presented for cross-lingual plagiarism analysis with ultimate objective of detection of plagiarism cases. English is the reference language and Persian materials are back translated using translation tools. The data for assessment of MLPD were obtained from English-Persian Mizan parallel cor...

Research paper thumbnail of DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization

Expert Systems with Applications, 2022

Research paper thumbnail of Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

Electronics, 2022

The optimal power flow (OPF) is a practical problem in a power system with complex characteristic... more The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solu...

Research paper thumbnail of MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

Symmetry, 2021

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on th... more The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search...

Research paper thumbnail of GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

Journal of Computational Science, 2022

Research paper thumbnail of A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Electronics, 2021

Real medical datasets usually consist of missing data with different patterns which decrease the ... more Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the f...

Research paper thumbnail of Full Source codes QANA-Quantum-based avian navigation optimizer algorithm

Research paper thumbnail of Full Source Code 2019 WBSCA A Binary Metaheuristic Algorithm for Wrapper Feature Selection

Research paper thumbnail of Enhancement of Bernstain-Search Differential Evolution Algorithm to Solve Constrained Engineering Problems(EBSD)_FullSourceCode.zip

Research paper thumbnail of Enhancement of Bernstain-Search Differential Evolution Algorithm to Solve Constrained Engineering Problems

Research paper thumbnail of A Binary Metaheuristic Algorithm for Wrapper Feature Selection

The classification accuracy is strongly affected by the quality of the input features used to bui... more The classification accuracy is strongly affected by the quality of the input features used to build a learned-model. Nowadays, datasets grow enormously both in size and number of features. One of the major difficulties confronted by huge datasets analysis is existing redundant, noisy, and irrelevant features, which may reduce the performance of the classifier. Feature selection is an important preprocessing task, which aims to select the most effective subset of features from the original dataset. Therefore, using feature selection method is essential for enhancing the classification accuracy and reducing the complexity of the built model. In this paper, a wrapper-based binary Sine Cosine Algorithm (SCA) for feature selection named WBSCA is proposed. The proposed algorithm was compared with three well-known binary algorithms over seven classification datasets from the UCI machine learning repository. The results show the competitive performance of the proposed algorithm in searching the optimal subset and selecting the salient feature.

Research paper thumbnail of Feature Selection Based on Whale Optimization Algorithm for Diseases Diagnosis

Medical datasets are mainly composed of countless irrelevant and redundant features in a series o... more Medical datasets are mainly composed of countless irrelevant and redundant features in a series of patient records. All these features are not required to obtain a medical decision-making process. On the other hand, the huge size of data is caused to increase the dimensionality and to reduce the performance of classifier. Recently, there have been many methods proposed to solve this problem and their results show that the feature selection can be an effective solution. The feature selection methods are mostly aim to reduce the size of data and enhance the efficiency of learning algorithms by eliminating the unrelated and redundant features. In this paper, a meta-heuristic algorithm is proposed named FSWOA for feature selection. This algorithm is based on the hunting methods of Humpback Whales consisting of three main steps: encircling prey, spiral bubble-net attacking and search for prey. The performance of proposed algorithm is evaluated conducted by four standard medical datasets: Pima Indians Diabetes, Original Wisconsin Breast Cancer, Statlog and Hepatitis. The results show that the proposed algorithm can reduce the dimensionality of medical datasets with acceptable accuracy for diseases diagnosis.

Research paper thumbnail of Multi-trial vector-based differential evolution algorithm_Full Source Code_2020_ASCO.zip

Research paper thumbnail of Binary Whale Optimization Algorithm_Full source code

Research paper thumbnail of Migration-Based Moth-Flame Optimization Algorithm

Processes, 2021

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates suffi... more Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising...

Research paper thumbnail of Starling murmuration optimizer: A novel bio-inspired algorithm for global and engineering optimization

Computer Methods in Applied Mechanics and Engineering, 2022

Research paper thumbnail of Full Source Code -Conscious Neighborhood-based Crow Search (CCSA) Algorithm

Research paper thumbnail of BINARY SINE COSINE ALGORITHMS FOR FEATURE Full Source Code 2019 ACIJ

Research paper thumbnail of Efficient Load Balancing Using Ant Colony Optimization

Workload and resource management are two essential functions provided in the service level of a G... more Workload and resource management are two essential functions provided in the service level of a Grid software infrastructure. Consistently, efficient load balancing algorithms are fundamentally important to improve the global throughput of these environments. Although previous works show that, ant colony algorithm works well for load balancing, the cost is a very important factor in this subject. In this paper, a grid load balancing algorithm is proposed by using an ant colony optimization which is able to consider shortest path, type of resource, and running speed of resource. The experimental results show that the proposed algorithm by using this ant colony optimization can reduce the cost of load balancing in comparison with standard algorithm DASUD.

Research paper thumbnail of An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy, 2021

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward th... more Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to ...

Research paper thumbnail of Advanced Databases 01- پایگاه داده پیشرفته

"اسلاید های درس پایگاه داده پیشرفته برای دانشجویان کارشناسی ارشد مهندسی نرم افزار آدرس http://... more "اسلاید های درس پایگاه داده پیشرفته برای دانشجویان کارشناسی ارشد مهندسی نرم افزار
آدرس
http://research.iaun.ac.ir/pd/nadimi/
دکتر محمد ندیمی"

Research paper thumbnail of Data Mining 02- داده کاوی

این فصل ها شامل مطالب زیر است فصل اول مقدمه ای بر داده کاوی که فایل آن در این سایت هم گذاش... more این فصل ها شامل مطالب زیر است
فصل اول مقدمه ای بر داده کاوی که فایل آن در این سایت هم گذاشته شده.
فصل دوم که در مورد پیش پردازش داده ها در داده کاوی است- جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید :
http://research.iaun.ac.ir/pd/nadimi/
فصل سوم که در مورد انباره داده ها و مکعب داده ها با نگاه داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل چهارم که در مورد قوانین انجمنی به عنوان یکی از پر کاربرد ترین تکنیک های داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل پنجم که در مورد طبقه بندی به عنوان یکی از تکنیک های مهم داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل ششم که در مورد خوشه بندی و روش های آن به عنوان یکی از تکنیک های مهم داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
"

Research paper thumbnail of Data Mining 01- داده کاوی

"این فایل شمل اسلایدهای فصل اول کتاب داده کاوی است. در این فصل مقدمه ای بر داده کاوی نشان داده می... more "این فایل شمل اسلایدهای فصل اول کتاب داده کاوی است. در این فصل مقدمه ای بر داده کاوی نشان داده می شود. شما میتوانید اسلایدهای فصل های دیکر را نیز از صفحه شخصی من دانلود کنید به آدرس
http://research.iaun.ac.ir/pd/nadimi/
این فصل ها شامل مطالب زیر است
فصل اول مقدمه ای بر داده کاوی که فایل آن در این سایت هم گذاشته شده.
فصل دوم که در مورد پیش پردازش داده ها در داده کاوی است- جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید :
http://research.iaun.ac.ir/pd/nadimi/
فصل سوم که در مورد انباره داده ها و مکعب داده ها با نگاه داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل چهارم که در مورد قوانین انجمنی به عنوان یکی از پر کاربرد ترین تکنیک های داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل پنجم که در مورد طبقه بندی به عنوان یکی از تکنیک های مهم داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
فصل ششم که در مورد خوشه بندی و روش های آن به عنوان یکی از تکنیک های مهم داده کاوی است - جهت دانلود فایل آن به صفح شخصی من به آدرس زیر رجوع کنید
http://research.iaun.ac.ir/pd/nadimi/
"