Effect of Human Related Factors on Requirements Change Management in Offshore Software Development Outsourcing: A theoretical framework

Mehmood, Faisal,Zulfqar, Sukana Institute of Information Science and Technology 2021 Soft computing and machine intelligence Vol.1 No.1

Software development organizations are globalizing their development activities increasingly due to strategic and economic gains. Global software development (GSD) is an intricate concept, and various challenges are associated with it, specifically related to the software requirement change management Process (RCM). This research aims to identify humans' related success factors (HSFs) and human-related challenges (HCHs) that could influence the RCM process in GSD organizations and propose a theoretical framework of the identified factors concerning RCM process implementation. The Systematic Literature Review (SLR) method was adopted to investigate the HSFs and HCHs. Using the SLR approach, a total of 10 SFs and 10 CHs were identified. The study also reported the critical success factors (HCSFs) and critical challenges (HCCHs) for RCM process implementation following the factors having a frequency 50% as critical. Our results reveal that five out of ten HSFs and 4 out of ten HCHs are critical for RCM process implementation in GSD. Finally, we have developed a theoretical framework based on the identified factors that indicated a relationship among the identified factors and the implementation of the RCM process in the context of GSD. We believe that the results of this research can help tackle the complications associated with the RCM in GSD environment, which is vigorous to the success and progression of GSD organizations.

Tailoring cathode structure of catalyst coated membranes for performance enhancement in direct methanol fuel cells

Mehmood, A.,An, M.,Ha, H.Y. Pergamon Press 2016 International journal of hydrogen energy Vol.41 No.46

<P>In this study, we demonstrate how the formulation of colloidal catalyst ink and fabrication conditions affect the cathode microstructure of catalyst coated membranes (CCMs) prepared via decal technique. The CCMs based on conventional and high concentration cathode inks are compared in a direct methanol fuel cell (DMFC). It is found that the cathode catalyst layer made with a high concentration ink possesses superior porosity, leading to an improved DMFC performance. The temperature of roll-press used for preparing CCM is varied ranging from 170 to 210 degrees C in order to determine the optimal fabrication conditions for high concentration ink-based cathode. The CCM hot-pressed at 200 degrees C (advanced CCM) retains a significantly higher pore volume and outperforms' the conventional CCM by delivering an excellent DMFC performance with a maximum power density of 155 mW cm(-2), which is 20% higher than that of the conventional CCM. (C) 2016 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.</P>

Unified Planning of Wind Generators and Switched Capacitor Banks: A Multiagent Clustering-Based Distributed Approach

Mehmood, Khawaja Khalid,Kim, Chul-Hwan,Khan, Saad Ullah,Haider, Zunaib Maqsood IEEE 2018 IEEE transactions on power systems Vol.33 No.6

<P>In this paper, a multiagent clustering-based distributed approach for the optimal planning of wind-distributed generators (DGs) and switched capacitor banks (SCBs) is proposed. First, electrical distance matrices for the power systems are constructed. Additionally, a constrained optimization problem, which includes several indices and a few constraints, for the optimal clustering of distribution networks is formulated and solved. After obtaining optimal clusters, agents are assigned to the clusters, and a second multiobjective optimization problem (MOOP) for the distributed planning of wind DGs and SCBs is formulated and assigned to a head agent. The number of objective functions in the MOOP is equal to the number of agents. The objective function of an agent consists of three indices: annual energy losses, investment costs, and voltage enhancement. Moreover, a deep neural network architecture is designed, and four independent networks are trained with six years of wind speed data for the seasonal wind speed forecasting. Two IEEE unbalanced test feeders, one with 37 nodes and the other with 123 nodes, and eight test cases are considered for simulations. The results show that losses and costs are optimized, and the voltage unbalance of the system is reduced.</P>

Variable selection of spectroscopic data through monitoring both location and dispersion of PLS loading weights

Mehmood Tahir,Turk Arslan Munir 한국통계학회 2021 Journal of the Korean Statistical Society Vol.50 No.4

High dimensional data sets against the small sample size is essential for most of the sciences. The variable selection contributes to a better prediction of real-life phenomena. A multivariate approach called partial least squares (PLS) has the potential to model the high dimensional data, where the sample size is usually smaller than the number of variables. Truncation for variables selection in PLS T−PLS is considered a reference method. T−PLS and many others only monitors the location of PLS loading weights for variable selection. In the current article, we propose to monitor both location and dispersion of PLS loading weights for variable selection over the high dimensional spectral data. The proposed PLS variants are based on location, dispersion, both location and dispersion and at least location or dispersion monitoring of PLS loading weights, and are denoted by X−PLS, S−PLS, X&S−PLS and X|S−PLS respectively. Proposed PLS variants are compared with standard PLS and T−PLS through the Monte Carlo simulation of 100 runs on simulated and real data sets which includes corn, milk, and oil contents prediction based on spectroscopic data. X&S−PLS shows the best capability in selecting the real variables over the simulated data. The validated RMSE comparison indicates X|S−PLS and X&S−PLS outperforms compared to other methods in predicting corn, milk, and oil contents. X&S−PLS selects the smallest number of variables. Interestingly, selected variables by X&S−PLS are more consistent compared to all other methods. Hence X&S−PLS appears a potential candidate for variable selection in high dimensional data.

혼합형 태양조명 시스템의 효율적 적용을 통한 에너지절약 및 온실 가스 배출 감소 연구

Mehmood Uzair Muhammad,임상훈(Lim Sang Hoon),한현주(Han Hyun Joo),문성호(Moon Sung Ho),천원기(Chun Wongee) 한국에너지기후변화학회 2019 한국에너지기후변화학회 학술대회 Vol.2019 No.05

A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns

Mehmood, R.M.,Lee, H.J. Pergamon Press ; Elsevier Science Ltd 2016 Computers & electrical engineering Vol.53 No.-

<P>Several methods for collecting psychophysiological data from humans have been developed, including galvanic skin response (GSR), electromyography (EMG), electroencephalography (EEG), and the electrocardiogram (ECG). This paper proposes a feature extraction method for emotion recognition in EEG-based human brain signals. In this research, emotions were elicited from subjects using emotion-related stimuli from the International Affective Picture System (IAPS) database. We selected four kinds of emotional stimuli in the arousal-valence domain. Raw brain signals were preprocessed using independent component analysis (ICA) to remove artifacts. We introduced a feature extraction method using LPP, and implemented a benchmark based on statistical and frequency domain features. The LPP-based results show the highest accuracy when using SVM in the all-selected feature set. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system using brain signals. (C) 2016 Elsevier Ltd. All rights reserved.</P>

The Reaction of the Malaysian Stock Market to the COVID-19 Pandemic

Mehmood, Waqas,Mohd-Rashid, Rasidah,Aman-Ullah, Attia,Shafique, Owais,Tajuddin, Ahmad Hakimi World Association for Triple Helix and Future Stra 2021 Journal of Contemporary Eastern Asia Vol.20 No.2

The present study was conducted to understand the turmoil effects of COVID-19 pandemic on the Malaysian stock market during the different periods of the Movement Control Order (MCO). The present study was based on the secondary data extracted from the DataStream and Bloomberg from 2nd January 2020 to 29th May 2020 to evaluate the effects of COVID-19 pandemic on the Malaysian stock market. The findings suggested that during the different periods of the Movement Control Order (MCO) from the 1st January to 29th May 2020, the COVID-19 pandemic adversely affected the performance of KLCI index and all sectoral indices. The weakest performance indices were energy, property, and finance while the least affected indices were healthcare, technology, telecommunications, and media. This paper provides a review of the impacts of COVID-19 pandemic on the Malaysian stock market throughout the different periods of MCO.

Comparative Analysis of Consumer's Impulse Buying Behavior with Different Household Incomes : Empirical Evidence from Faisalabad

Mehmood, Sana,Hamid, Kashif East Asia Business Economics Association 2017 East asian journal of business management Vol.5 No.2

In today's highly unpredictable marketing environment, where the consumer demands and behaviors are continuously and rapidly changing therefore various factors of consumer impulse buying behavior are proving to be challenging for the existing and new business organizations. Shopping has become a relaxing and rejoicing activity for the consumers making impulsive buying as a socially acceptable and common practice. So by taking into account all these aspects, the objective of this study was to understand the factors affecting impulse buying behavior of the consumer. Store atmosphere and fashion involvement were selected as independent variables while consumer impulse buying behavior was taken as dependent variable for this study. Likewise, impulse buying behavior of consumers with different monthly household income was also analyzed in this study. Primary data was collected through a questionnaire from 250 respondents of district Faisalabad, and then it was analyzed by using various statistical techniques. The results indicated a significant positive impact of store atmosphere and fashion involvement on consumer impulse buying behavior. The study also revealed that among consumer groups with different household incomes; at least one group differed from others in impulse buying behavior. These results were consistent with previous literature. These results could provide information to the marketers and retailers for planning and execution of various marketing techniques. Moreover, educators could expand on the findings by developing new studies examining consumer impulse buying behavior.

Multivariate Procedure for Variable Selection and Classification of High Dimensional Heterogeneous Data

Mehmood, Tahir,Rasheed, Zahid The Korean Statistical Society 2015 Communications for statistical applications and me Vol.22 No.6

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

Corroborating social media echelon in cancer research

Mehmood, A.,On, B. W.,Lee, I.,Park, H. W.,Choi, G. S. Springer Science + Business Media 2018 Quality & quantity Vol.52 No.2

<P>Worldwide medical facilities differ, and for this reason, the causes of death can vary. Cancer is considered the second leading cause of death after heart disease worldwide, and the same causes of death are observed in the United States (US). Therefore, the purposes of this study are to explore worldwide research levels in the field of cancer and the social collaboration of researchers and institutions in this field. This article examines the structural patterns of international co-authors and co-institutions in science citation index papers in cancer research. The study uses measures from the social network analysis method, including degree centrality, betweenness centrality, eigenvector centrality, and effectiveness, to investigate the effects of social networks in the area of cancer research. Empirical analysis results identify the US is the most central country, followed by Germany, Italy, France, and China, in terms of co-authored networks in this research field. Institutional analysis results indicate that the University of Milan is at the top in terms of degree centrality. The Gustave Roussy Cancer Campus in France and German University of Dusseldorf occupy the second and fourth positions, respectively. The University of California in Los Angeles and Harvard University, both in the US, are at third and fifth positions, respectively.</P>