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Muhammad Tahir completed MS degree in Computer Science from National University of Computer \& Emerging Sciences, Islamabad, in 2009, and the PhD degree in Computer Science from Pakistan Institute of Engineering & Applied Sciences, Islamabad, in 2014. He served City University of Science & Information Technology, Peshawar, as Assistant Professor, from 2014 to 2015. From 2015 to the present, he has been serving Department of Computer Science, College of Computing and Informatics at Saudi E-University, as Assistant Professor. His research interests include E-learning paradigms, machine learning, pattern recognition, image processing, deep learning, and bioinformatics.
This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.
Abdullah H. Alenezy; Mohd Tahir Ismail; S. Al Wadi; Muhammad Tahir; Nawaf N. Hamadneh; Jamil J. Jaber; Waqar A. Khan. Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions. Journal of Mathematics 2021, 2021, 1 -10.
AMA StyleAbdullah H. Alenezy, Mohd Tahir Ismail, S. Al Wadi, Muhammad Tahir, Nawaf N. Hamadneh, Jamil J. Jaber, Waqar A. Khan. Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions. Journal of Mathematics. 2021; 2021 ():1-10.
Chicago/Turabian StyleAbdullah H. Alenezy; Mohd Tahir Ismail; S. Al Wadi; Muhammad Tahir; Nawaf N. Hamadneh; Jamil J. Jaber; Waqar A. Khan. 2021. "Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions." Journal of Mathematics 2021, no. : 1-10.
The study aims to examine the effects of artificial intelligence (AI) on the consistency and analysis of financial statements in hotels in ASEZA, Jordan. This research is an exploratory, empirical study, which uses the methodology of data collection and interpretation to draw conclusions. The researchers used the arithmetic mean, standard deviation, T-test and ANOVA test to calculate the degree of significance of the study questions. The findings of a basic linear regression study of the impact of AI implemented in Jordanian hotels on the integration of accounting information systems and the association between AI and the integration of accounting information systems (R = 59.6%) also indicate that the fixed limit value amounted to (2.060) and the value of (Beta) for T-test
Nawaf Hamadneh; Mousa Saleh; Omar Jawabreh; Muhammad Tahir; Rania Al Omari; Nazem Shniekat. The Effect of Artificial Intelligence (AI) on the Quality and Interpretation of Financial Statements in the Hotels Classified in the AQABA Special Economic Zone (ASEZA). 2021, 1 .
AMA StyleNawaf Hamadneh, Mousa Saleh, Omar Jawabreh, Muhammad Tahir, Rania Al Omari, Nazem Shniekat. The Effect of Artificial Intelligence (AI) on the Quality and Interpretation of Financial Statements in the Hotels Classified in the AQABA Special Economic Zone (ASEZA). . 2021; ():1.
Chicago/Turabian StyleNawaf Hamadneh; Mousa Saleh; Omar Jawabreh; Muhammad Tahir; Rania Al Omari; Nazem Shniekat. 2021. "The Effect of Artificial Intelligence (AI) on the Quality and Interpretation of Financial Statements in the Hotels Classified in the AQABA Special Economic Zone (ASEZA)." , no. : 1.
Artificial intelligence (AI) based business process optimization has a significant impact on a country’s economic development. We argue that the use of artificial neural networks in business processes will help optimize these processes ensuring the necessary level in the functioning and compliance with the foundations of sustainable development. In this paper, we proposed a mathematical model using AI to detect outliers in the daily return of Saudi stock market (Tadawul). An outlier is defined as a data point that deviates too much from the rest of the observations in a data sample. Based on the Engle and Granger Causality test, we selected inflation rate, repo rate, and oil prices as input variables. In order to build the mathematical model, we first used the Tukey method to detect outliers in the stock return data from Tadawul that are collected during the period from October 2011 to December 2019. In this way, we categorized the stock return data into two classes, namely, outliers and nonoutliers. These data are further used to train artificial neural network in conjunction with particle swarm optimization algorithm. In order to assess the performance of the proposed model, we employed the mean squared error function. Our proposed model is signified by the mean squared error value of 0.05. The proposed model is capable of detecting outlier values directly from the inflation rate, repo rate, and oil prices. The proposed model can be helpful in developing and applying intelligent optimization techniques to solve problems in business processes.
Khudhayr A. Rashedi; Mohd Tahir Ismail; Nawaf N. Hamadneh; S. AL Wadi; Jamil J. Jaber; Muhammad Tahir. Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns. Journal of Mathematics 2021, 2021, 1 -8.
AMA StyleKhudhayr A. Rashedi, Mohd Tahir Ismail, Nawaf N. Hamadneh, S. AL Wadi, Jamil J. Jaber, Muhammad Tahir. Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns. Journal of Mathematics. 2021; 2021 ():1-8.
Chicago/Turabian StyleKhudhayr A. Rashedi; Mohd Tahir Ismail; Nawaf N. Hamadneh; S. AL Wadi; Jamil J. Jaber; Muhammad Tahir. 2021. "Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns." Journal of Mathematics 2021, no. : 1-8.
E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters (e.g. COVID-19 pandemic etc.) and war circumstances. The reliable, fair, and seamless execution of online exams in E-learning is highly significant. Particularly, online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place. This poses several issues like integrity and security during online exams. To address such issues, researchers frequently proposed different techniques and tools. However, a study summarizing and analyzing latest developments, particularly in the area of online examination, is hard to find in the literature. In this article, a Systematic Literature Review (SLR) of online examination is performed to select and analyze 53 studies published during the last five years (i.e. Jan 2016 to July 2020). Subsequently, five leading online exams features targeted in the selected studies are identified. Moreover, underlying development approaches for the implementation of online exams solutions are explored. Furthermore, 16 important techniques / algorithms and 11 datasets are presented. In addition to this, 21 online exams tools proposed in the selected studies are identified. Additionally, 25 leading existing tools used in the selected studies are also presented. Finally, the participation of countries in online exam research is investigated. Key factors for the global adoption of online exams are identified and compared with major online exams features. This facilitates the selection of right online exam system for a particular country on the basis of existing E-learning infrastructure and overall cost. To conclude, the findings of this article provide a solid platform for the researchers and practitioners of the domain to select appropriate features along with underlying development approaches, tools, and techniques for the implementation of a particular online exams solution as per given requirements.
Abdul Wahab Muzaffar; Muhammad Tahir; Muhammad Waseem Anwar; Qaiser Chaudry; Shamaila Rasheed Mir; Yawar Rasheed. A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption. IEEE Access 2021, 9, 32689 -32712.
AMA StyleAbdul Wahab Muzaffar, Muhammad Tahir, Muhammad Waseem Anwar, Qaiser Chaudry, Shamaila Rasheed Mir, Yawar Rasheed. A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption. IEEE Access. 2021; 9 ():32689-32712.
Chicago/Turabian StyleAbdul Wahab Muzaffar; Muhammad Tahir; Muhammad Waseem Anwar; Qaiser Chaudry; Shamaila Rasheed Mir; Yawar Rasheed. 2021. "A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption." IEEE Access 9, no. : 32689-32712.
The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.
Nawaf N. Hamadneh; Muhammad Tahir; Waqar A. Khan. Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico. Mathematics 2021, 9, 180 .
AMA StyleNawaf N. Hamadneh, Muhammad Tahir, Waqar A. Khan. Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico. Mathematics. 2021; 9 (2):180.
Chicago/Turabian StyleNawaf N. Hamadneh; Muhammad Tahir; Waqar A. Khan. 2021. "Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico." Mathematics 9, no. 2: 180.
Early detection of Isocitrate Dehydrogenase (IDH) mutations can be used in decision making procedures. We demonstrated the role of important features identification using extreme gradient boosting ensemble from MR imagery and their effectiveness in classification of IDH mutations. In this work, the MR images are first pre-processed using a number of image processing techniques. Then features are extracted from the pre-processed images that are further classified using boosting ensemble. After, removing very high negative and postive as well as zero valued attributes from the extracted feature spaces, an increase in the performance accuracy is observed. The proposed technique is simple yet efficient in classifying IDH mutations from MR imagery. This will help practitioners to noninvasively diagnose and predict IDH wildtype and IDH mutants for grades II, III, and IV.
Muhammad Tahir. Brain MRI Classification Using Gradient Boosting. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 294 -301.
AMA StyleMuhammad Tahir. Brain MRI Classification Using Gradient Boosting. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; ():294-301.
Chicago/Turabian StyleMuhammad Tahir. 2020. "Brain MRI Classification Using Gradient Boosting." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 294-301.
The educational system across the world has immensely been affected due to outbreak of COVID-19; it forced the shut down of educational institutions, which adversely affected student fraternity across the globe. Due to its contagious nature, COVID-19 demanded containment and enforced isolation that tremendously affected personal interaction of teachers and students. In the absence of traditional classroom teaching and one-to-one interaction, computer-based learning has emerged as closest substitute for off-line teaching. Against such a backdrop, it is pertinent to examine the students’ perception and readiness about online-learning system adopted at the university level during the ongoing COVID-19 pandemic. For the present study, the quantitative approach has been adopted and responses from 184 university students of National Capital Territory (NCT) of Delhi, India namely Delhi University, Jamia Millia Islamia (Central University) and Guru Gobind Singh Indraprastha University are collected through online questionnaire. This research study was conducted during June–August 2020. The findings of the study reveal students’ positive perception towards e-learning and thus acceptance of this new learning system. It has also empirically demonstrated the significance of e-learning in the time of COVID-19 crisis. In fact, e-learning has emerged as a new way of enhancing the learning process where social media may further improve the learning output. The findings of the study will facilitate educational institutions and policy makers to take this online-learning process to the next level in a better way.
Mohammed Khan; Vivek Vivek; Mohammed Nabi; Maysoon Khojah; Muhammad Tahir. Students’ Perception towards E-Learning during COVID-19 Pandemic in India: An Empirical Study. Sustainability 2020, 13, 57 .
AMA StyleMohammed Khan, Vivek Vivek, Mohammed Nabi, Maysoon Khojah, Muhammad Tahir. Students’ Perception towards E-Learning during COVID-19 Pandemic in India: An Empirical Study. Sustainability. 2020; 13 (1):57.
Chicago/Turabian StyleMohammed Khan; Vivek Vivek; Mohammed Nabi; Maysoon Khojah; Muhammad Tahir. 2020. "Students’ Perception towards E-Learning during COVID-19 Pandemic in India: An Empirical Study." Sustainability 13, no. 1: 57.
Background: The knowledge of subcellular location of proteins is essential to the comprehension of numerous protein functions. Objective: Accurate as well as computationally efficient and reliable automated analysis of protein localization imagery greatly depend on the calculation of features from these images. Methods: In the current work, a novel method termed as MD-LBP is proposed for feature extraction from fluorescence microscopy protein images. For a given neighborhood, the value of central pixel is computed as the difference of global and local means of the input image that is further used as threshold to generate a binary pattern for that neighborhood. Results: The performance of our method is assessed for 2D HeLa dataset using 5-fold crossvalidation protocol. The performance of MD-LBP method with RBF-SVM as base classifier, is superior to that of standard LBP algorithm, Threshold Adjacency Statistics, and Haralick texture features. Conclusion: Development of specialized systems for different kinds of medical imagery will certainly pave the path for effective drug discovery in pharmaceutical industry. Furthermore, biological and bioinformatics based procedures can be simplified to facilitate pharmaceutical industry for drug designing.
Muhammad Tahir. MD-LBP: An Efficient Computational Model for Protein Subcellular Localization from HeLa Cell Lines Using SVM. Current Bioinformatics 2020, 15, 204 -211.
AMA StyleMuhammad Tahir. MD-LBP: An Efficient Computational Model for Protein Subcellular Localization from HeLa Cell Lines Using SVM. Current Bioinformatics. 2020; 15 (3):204-211.
Chicago/Turabian StyleMuhammad Tahir. 2020. "MD-LBP: An Efficient Computational Model for Protein Subcellular Localization from HeLa Cell Lines Using SVM." Current Bioinformatics 15, no. 3: 204-211.
Background and Objective: Discriminative and informative feature extraction is the core requirement for accurate and efficient classification of protein subcellular localization images so that drug development could be more effective. The objective of this paper is to propose a novel modification in the Threshold Adjacency Statistics technique and enhance its discriminative power. Mehtods: In this work, we utilized Threshold Adjacency Statistics from a novel perspective to enhance its discrimination power and efficiency. In this connection, we utilized seven threshold ranges to produce seven distinct feature spaces, which are then used to train seven SVMs. The final prediction is obtained through the majority voting scheme. The proposed ETAS-SubLoc system is tested on two benchmark datasets using 5-fold cross-validation technique. Results: We observed that our proposed novel utilization of TAS technique has improved the discriminative power of the classifier. The ETAS-SubLoc system has achieved 99.2% accuracy, 99.3% sensitivity and 99.1% specificity for Endogenous dataset outperforming the classical Threshold Adjacency Statistics technique. Similarly, 91.8% accuracy, 96.3% sensitivity and 91.6% specificity values are achieved for Transfected dataset. Conclusions: Simulation results validated the effectiveness of ETAS-SubLoc that provides superior prediction performance compared to the existing technique. The proposed methodology aims at providing support to pharmaceutical industry as well as research community towards better drug designing and innovation in the fields of bioinformatics and computational biology. The implementation code for replicating the experiments presented in this paper is available at: https://drive.google.com/file/d/0B7IyGPObWbSqRTRMcXI2bG5CZWs/view?usp=sharing
Muhammad Tahir; Bismillah Jan; Maqsood Hayat; Shakir Ullah Shah; Muhammad Amin. Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines. Computer Methods and Programs in Biomedicine 2018, 157, 205 -215.
AMA StyleMuhammad Tahir, Bismillah Jan, Maqsood Hayat, Shakir Ullah Shah, Muhammad Amin. Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines. Computer Methods and Programs in Biomedicine. 2018; 157 ():205-215.
Chicago/Turabian StyleMuhammad Tahir; Bismillah Jan; Maqsood Hayat; Shakir Ullah Shah; Muhammad Amin. 2018. "Efficient computational model for classification of protein localization images using Extended Threshold Adjacency Statistics and Support Vector Machines." Computer Methods and Programs in Biomedicine 157, no. : 205-215.
Extraction of useful and discriminative information from fluorescence microscopy protein images is a challenging task in the field of machine learning and pattern recognition. Gray Level Co-occurrence Matrix (GLCM) was among the first methods developed for textural analysis, which holds information of intensity distribution as well as the respective distance of intensity levels in the original image. In this paper, several GLCMs are constructed with different quantization levels for different values of offset d. Haralick descriptors are extracted from each GLCM, which are then utilized to train support vector machines. The final output is obtained through the majority voting scheme. Hybrid models from different individual feature spaces have also been constructed. Additionally, Correlation-based Feature Selection (CFS) is performed to extract the most useful features from the hybrid models. The empirical analysis reveals that varying the value of parameter d causes the GLCM to extract different information from a particular fluorescence microscopy image. Hence, producing diversified co-occurrence matrices for same images. Similarly, using more quantization levels for constructing a GLCM generates informative and discriminative features for the classification phase. Furthermore, CFS has significantly reduced the feature space dimensionality achieving almost the same accuracy as full feature space. The performance of the proposed system is validated using three benchmark datasets including HeLa (99.6%), CHO (100%), and LOCATE Endogenous (100%) datasets. It is anticipated that GLCM is still an efficient technique for pattern analysis in the field of bioinformatics and computational biology as well as might be helpful in drug discovery related applications
Muhammad Tahir. Pattern analysis of protein images from fluorescence microscopy using Gray Level Co-occurrence Matrix. Journal of King Saud University - Science 2018, 30, 29 -40.
AMA StyleMuhammad Tahir. Pattern analysis of protein images from fluorescence microscopy using Gray Level Co-occurrence Matrix. Journal of King Saud University - Science. 2018; 30 (1):29-40.
Chicago/Turabian StyleMuhammad Tahir. 2018. "Pattern analysis of protein images from fluorescence microscopy using Gray Level Co-occurrence Matrix." Journal of King Saud University - Science 30, no. 1: 29-40.
Muhammad Tahir; Asifullah Khan. Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Information Sciences 2016, 345, 65 -80.
AMA StyleMuhammad Tahir, Asifullah Khan. Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification. Information Sciences. 2016; 345 ():65-80.
Chicago/Turabian StyleMuhammad Tahir; Asifullah Khan. 2016. "Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification." Information Sciences 345, no. : 65-80.
Proteins are the executants of biological functions in living organisms. Comprehension of protein structure is a challenging problem in the era of proteomics, computational biology, and bioinformatics because of its pivotal role in protein folding patterns. Owing to the large exploration of protein sequences in protein databanks and intricacy of protein structures, experimental and theoretical methods are insufficient for prediction of protein structure classes. Therefore, it is highly desirable to develop an accurate, reliable, and high throughput computational model to predict protein structure classes correctly from polygenetic sequences. In this regard, we propose a promising model employing hybrid descriptor space in conjunction with optimized evidence-theoretic K-nearest neighbor algorithm. Hybrid space is the composition of two descriptor spaces including Multi-profile Bayes and bi-gram probability. In order to enhance the generalization power of the classifier, we have selected high discriminative descriptors from the hybrid space using particle swarm optimization, a well-known evolutionary feature selection technique. Performance evaluation of the proposed model is performed using the jackknife test on three low similarity benchmark datasets including 25PDB, 1189, and 640. The success rates of the proposed model are 87.0%, 86.6%, and 88.4%, respectively on the three benchmark datasets. The comparative analysis exhibits that our proposed model has yielded promising results compared to the existing methods in the literature. In addition, our proposed prediction system might be helpful in future research particularly in cases where the major focus of research is on low similarity datasets.
Maqsood Hayat; Muhammad Tahir; Sher Afzal Khan. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. Journal of Theoretical Biology 2014, 346, 8 -15.
AMA StyleMaqsood Hayat, Muhammad Tahir, Sher Afzal Khan. Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces. Journal of Theoretical Biology. 2014; 346 ():8-15.
Chicago/Turabian StyleMaqsood Hayat; Muhammad Tahir; Sher Afzal Khan. 2014. "Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces." Journal of Theoretical Biology 346, no. : 8-15.
Discriminative feature extraction technique is always required for the development of accurate and efficient prediction systems for protein subcellular localization so that effective drugs can be developed. In this work, we showed that Local Ternary Patterns (LTPs) effectively exploit small variations in pixel intensities; present in fluorescence microscopy based protein images of human and hamster cell lines. Further, Synthetic Minority Oversampling Technique is applied to balance the feature space for the classification stage. We observed that LTPs coupled with data balancing technique could enable a classifier, in this case support vector machine, to yield good performance. The proposed ensemble based prediction system, using 10-fold cross-validation, has yielded better performance compared to existing techniques in predicting various subcellular compartments for both 2D HeLa and CHO datasets. The proposed predictor is available online at: http://111.68.99.218/Protein_SubLoc/, which is freely accessible to the public.
Muhammad Tahir; Asifullah Khan; Hüseyin Kaya. Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. Journal of Theoretical Biology 2014, 340, 85 -95.
AMA StyleMuhammad Tahir, Asifullah Khan, Hüseyin Kaya. Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images. Journal of Theoretical Biology. 2014; 340 ():85-95.
Chicago/Turabian StyleMuhammad Tahir; Asifullah Khan; Hüseyin Kaya. 2014. "Protein subcellular localization in human and hamster cell lines: Employing local ternary patterns of fluorescence microscopy images." Journal of Theoretical Biology 340, no. : 85-95.
Protein subcellular localization plays a vital role in understanding proteins’ behavior under different circumstances. The effectiveness of various drugs can be assessed by the successful prediction of protein locations. Therefore, it is important to develop a prediction system that is sufficiently reliable and accurate in making decisions regarding the protein localization. However, main problem in developing a reliable and high throughput prediction system is the presence of imbalanced data, which greatly affects the performance of a prediction system. In order to remedy this problem, we utilized the notion of oversampling through Synthetic Minority Oversampling TEchnique (SMOTE). Further, different feature extraction strategies and ensemble classification techniques are assessed for their contribution toward the solution of the challenging problem of subcellular localization. After applying SMOTE data balancing technique, a remarkable improvement is observed in the performance of random forest and rotation forest ensemble classifiers for CHOM, CHOA and VeroA datasets. It is anticipated that our proposed model might be helpful for the research community in the field of functional and structural proteomics as well as in drug discovery.
Muhammad Tahir; Asifullah Khan; Abdul Majid; Alessandra Lumini. Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing. Applied Soft Computing 2013, 13, 4231 -4243.
AMA StyleMuhammad Tahir, Asifullah Khan, Abdul Majid, Alessandra Lumini. Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing. Applied Soft Computing. 2013; 13 (11):4231-4243.
Chicago/Turabian StyleMuhammad Tahir; Asifullah Khan; Abdul Majid; Alessandra Lumini. 2013. "Subcellular localization using fluorescence imagery: Utilizing ensemble classification with diverse feature extraction strategies and data balancing." Applied Soft Computing 13, no. 11: 4231-4243.
Mitochondrial protein of Plasmodium falciparum is an important target for anti-malarial drugs. Experimental approaches for detecting mitochondrial proteins are costly and time consuming. Therefore, MitProt-Pred is developed that utilizes Bi-profile Bayes, Pseudo Average Chemical Shift, Split Amino Acid Composition, and Pseudo Amino Acid Composition based features of the protein sequences. Hybrid feature space is also developed by combining different individual feature spaces. These feature spaces are learned and exploited through SVM based ensemble. MitProt-Pred achieved significantly improved prediction performance for two standard datasets. We also developed the score level ensemble, which outperforms the feature level ensemble.
Muhammad Tayyeb Mirza; Asifullah Khan; Muhammad Tahir; Yeon Soo Lee. MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification. Computers in Biology and Medicine 2013, 43, 1502 -1511.
AMA StyleMuhammad Tayyeb Mirza, Asifullah Khan, Muhammad Tahir, Yeon Soo Lee. MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification. Computers in Biology and Medicine. 2013; 43 (10):1502-1511.
Chicago/Turabian StyleMuhammad Tayyeb Mirza; Asifullah Khan; Muhammad Tahir; Yeon Soo Lee. 2013. "MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification." Computers in Biology and Medicine 43, no. 10: 1502-1511.
Motivation: Subcellular localization of proteins is one of the most significant characteristics of living cells. Prediction of protein subcellular locations is crucial to the understanding of various protein functions. Therefore, an accurate, computationally efficient and reliable prediction system is required. Results: In this article, the predictions of various Support Vector Machine (SVM) models have been combined through majority voting. The proposed ensemble SVM-SubLoc has achieved the highest success rates of 99.7% using hybrid features of Haralick textures and local binary patterns (HarLBP), 99.4% using hybrid features of Haralick textures and Local Ternary Patterns (HarLTP). In addition, SVM-SubLoc has yielded 99.0% accuracy using only local ternary patterns (LTPs) based features. The dimensionality of HarLBP feature vector is 581 compared with 78 and 52 for HarLTP and LTPs, respectively. Hence, SVM-SubLoc in conjunction with LTPs is fast, sufficiently accurate and simple predictive system. The proposed SVM-SubLoc approach thus provides superior prediction performance using the reduced feature space compared with existing approaches. Availability: A web server accompanying the proposed prediction scheme is available at http://111.68.99.218/SVM-SubLoc Contact:[email protected]; [email protected] Supplementary information:Supplementary data are available at Bioinformatics online.
Muhammad Tahir; Asifullah Khan; Abdul Majid. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics 2011, 28, 91 -97.
AMA StyleMuhammad Tahir, Asifullah Khan, Abdul Majid. Protein subcellular localization of fluorescence imagery using spatial and transform domain features. Bioinformatics. 2011; 28 (1):91-97.
Chicago/Turabian StyleMuhammad Tahir; Asifullah Khan; Abdul Majid. 2011. "Protein subcellular localization of fluorescence imagery using spatial and transform domain features." Bioinformatics 28, no. 1: 91-97.