National University of Computer and Emerging Sciences
Recent publications
Increasingly, companies are embracing the green-lean-six sigma (GLSS) approach to managing their environmental impacts. Nevertheless, there is a paucity of studies that examined the underlying motivations behind GLSS deployment in manufacturing organizations. Accordingly, this study investigates the GLSS motivators in a developed and developing economy context through multiple case studies in which semistructured interviews are conducted with senior corporate managers in the flexible packaging manufacturing industry. Drawing on the natural resource-based view (NRBV) and institutional theory-based view (ITBV), our analysis revealed various operational, organizational, and environmental factors as internal motivators and factors related to the state, society, and market as external motivators. To the best of our knowledge, it is one of the early comparative studies investigating the GLSS motivators in both developed and developing economies, which can guide future researchers, practitioners, and policymakers in understanding the salient factors influencing the GLSS adoption for environmental sustainability
This paper proposes a novel hybrid fault-tolerant control system (HFTCS) with dedicated non-linear controllers: artificial neural network (ANN) and sliding mode control (SMC) for active and passive parts, respectively. The proposed system can provide both desirable properties of stability to unexpected fast disturbances and post-fault optimal performance. In the active fault tolerant control system (AFTCS) part, the fault detection and isolation (FDI) unit is designed through the use of ANN for the estimation of faulty sensor values in the observer model. In the passive fault-tolerant system (PFTCS) part, the air-fuel ratio (AFR) controller is designed using a robust SMC that allows systems to manage faults in predefined limits without estimation. In the proposed system, SMC will form the passive part to react instantly to faults while ANN will optimize post-fault performance with active compensation. Moreover, Lyapunov stability analysis was also performed to make sure that the system remains stable in both normal and faulty conditions. The simulation results in the Matlab/Simulink environment show that the designed controller is robust to faults in normal and noisy measurements of the sensors. A comparison with the existing works also demonstrates the superior performance of the proposed hybrid algorithm.
Ancient manuscripts are a rich source of history and civilization. Unfortunately, these documents are often affected by different age and storage related degradation which impinge on their readability and information contents. In this paper, we propose a document restoration method that removes the unwanted interfering degradation patterns from color ancient manuscripts. We exploit different color spaces to highlight the spectral differences in various layers of information usually present in these documents. At each image pixel, the spectral representations of all color spaces are stacked to form a feature vector. PCA is applied to the whole data cube to eliminate correlation of the color planes and enhance separation among the patterns. The reduced data cube, along with the pixel spatial information, is used to perform a pixel based segmentation, where each cluster represents a class of pixels that share similar color properties in the decorrelated color spaces. The interfering, unwanted classes can thus be removed by inpainting their pixels with the background texture. Assuming Gaussian distributions for the various classes, a Gaussian Mixture Model (GMM) is estimated through the Expectation Maximization (EM) algorithm from the data, and then used to find appropriate labels for each pixel. In order to preserve the original appearance of the document and reproduce the background texture, the detected degraded pixels are replaced based on Gaussian conditional simulation, according to the surrounding context. Experiments are shown on manuscripts affected by different kinds of degradations, including manuscripts from the DIBCO 2018 and 2019 publicaly available dataset. We observe that the use of a few PCA dominant components accelerates the clustering process and provides a more accurate segmentation.
In this manuscript, a Bazykin–Berezovskaya model with diffusion by strong Allee effects is studied. Neumann boundary conditions are used to see the positive solution of a diffusion system. Local stability analyses are discussed for all the equilibrium points. The analysis of stability for the proposed scheme is also given. Implicit finite difference schemes like: Euler, Crank–Nicolson (CN) and non-standard finite difference (NSFD) are used to verify the simulation by numerically. A comparison reveals that NSFD method is unconditionally stable for any temporal step-size.
With an ever-increasing number of mobile users, the development of mobile applications (apps) has become a potential market during the past decade. Billions of users download mobile apps for divergent use from Google Play Store, fulfill tasks and leave comments about their experience. Such reviews are replete with a variety of feedback that serves as a guide for the improvement of existing apps and intuition for novel mobile apps. However, application reviews are challenging and very broad to approach. Such reviews, when segregated into different classes guide the user in the selection of suitable apps. This study proposes a framework for analyzing the sentiment of reviews for apps of eight different categories like shopping, sports, casual, etc. A large dataset is scrapped comprising 251661 user reviews with the help of ‘Regular Expression’ and ‘Beautiful Soup’. The framework follows the use of different machine learning models along with the term frequency-inverse document frequency (TF-IDF) for feature extraction. Extensive experiments are performed using preprocessing steps, as well as, the stats feature of app reviews to evaluate the performance of the models. Results indicate that combining the stats feature with TF-IDF shows better performance and the support vector machine obtains the highest accuracy. Experimental results can potentially be used by other researchers to select appropriate models for the analysis of app reviews. In addition, the provided dataset is large, diverse, and balanced with eight categories and 59 app reviews and provides the opportunity to analyze reviews using state-of-the-art approaches.
The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus’s spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.
This article presents flexible online adaptation strategies for the performance-index weights to constitute a variable structure Linear-Quadratic-Integral (LQI) controller for an under-actuated rotary pendulum system. The proposed control procedure undertakes to improve the controller's adaptability, allowing it to flexibly manipulate the control stiffness which aids in efficiently rejecting the bounded exogenous disturbances while preserving the system's closed-loop stability and economizing the overall control energy expenditure. The proposed scheme is realized by augmenting the ubiquitous LQI controller with an innovative online weight adaptation law that adaptively modulates the state-weighting factors of the internal performance index. The weight adaptation law is formulated as a pre-calibrated function of dissipative terms, anti-dissipative terms, and model-reference tracking terms to achieve the desired flexibility in the controller design. The adjusted state weighting factors are used by the Riccati equation to yield the time-varying state-compensator gains.
To efficiently handle the complex region, the intra modes are increased from 9 to 35 in High Efficiency Video Coding (HEVC). As a result, the encoding time is increased and makes HEVC unfit for real time applications. This article applies the firefly algorithm (FFA) in HEVC to perform fast intra mode selection. Firstly, the intra modes of HEVC are mapped to the fireflies in FFA. Secondly, a novel, fast, and efficient objective function is formulated to efficiently handle the current problem of HEVC. Results show that 32.48% of the encoding time of HEVC is reduced. Moreover, it saved 20% more time than the existing work that applied FFA for intra mode decision.
Stock market is a place where shares of public limited companies are traded. The stock exchange allows the end-user to buy and sell the shares and other security/financial instruments. Previously, different mathematical techniques are used to predict the movements in the stock markets or shares based on different market factor i.e. demographic factors, country’s financial position, political factors. The purpose of the research is to find the relationship or impact of the US Dollar fluctuation on the stock price movement. For the analysis purpose use two different dataset, first, movement in the USD as independent data member and fluctuation in stock price as a dependent data member. For this research, deploy different machine learning analytical tools to analyze the prices and predict the near future price or fluctuation of the prices based on different factors of the market.
Nanosheets of ZnO are fabricated on Ni foam (NF) via a single-step hydrothermal reaction and employed as an electrode material for asymmetric supercapacitors. ZnO with nanosheet structure offers remarkable structural features that provide less internal resistance and facilitate easy transportation of ions/electrons, making it eligible for promising electrode material. ZnO nanosheets reveal excellent electrochemical performance as electrode material. Specific capacitance reaches a maximum value of 1209 F g⁻¹ at 1 A g⁻¹ with 83% capacitance retention in an electrolyte of 3 M KOH after 5000 galvanostatic charge/discharge (GCD) cycles. The asymmetric supercapacitor ASC (ZnO@NF//AC@NF) is fabricated using ZnO nanosheets and activated carbon (AC) as positive and negative electrodes, respectively. For ASC, the specific capacitance reaches a maximum value of 87 F g⁻¹ at 1 A g⁻¹ with 75.5% capacitance retention after 4000 GCD cycles. The ASC exhibits an energy density of 28 Wh kg⁻¹ and a power density of 839 W kg⁻¹. The outstanding electrochemical characteristics of ZnO nanosheets electrode direct their potential for electro-energy storage systems as an efficient electrode material.
This paper presents a comprehensive model predictive control (CMPC) method to control a three-phase four-legged inverter (TP4LI) for PV systems. The proposed TP4LI model aims to predictively model and control switching frequency and higher voltage/current switching to reduce losses. The CMPC model can be operated in four modes, namely standard MPC mode (Mode I), switching frequency reduction (SFR) mode (Mode II), high voltage/current switching loss reduction (SLR) mode (Mode III), and SFR plus SLR mode (Mode IV, a combination of Modes II and III). The proposed CMPC approach controls the TP4LI to (1) successfully track balanced and unbalanced reference currents with balanced or unbalanced loads; (2) reduce switching losses; and (3) keep the generated current total harmonic distortion (THD) within the industry’s recommended limits. The TP4LI model with the CMPC approach was verified and validated in the MATLAB/Simulink for a PV system. The simulation results show good tracking and performance of the TP4LI for balanced and unbalanced reference currents with balanced and unbalanced loads in all four modes of operation.
In this manuscript, fractional modeling of non-Newtonian Casson fluid squeezed between two parallel plates is performed under the influence of magneto-hydro-dynamic and Darcian effects. The Casson fluid model is fractionally transformed through mixed similarity transformations. As a result, partial differential equations (PDEs) are transformed to a fractional ordinary differential equation (FODE). In the current modeling, the continuity equation is satisfied while the momentum equation of the integral order Casson fluid is recovered when the fractional parameter is taken as α=1. A modified homotopy perturbation algorithm is used for the solution and analysis of highly nonlinear and fully fractional ordinary differential equations. Obtained solutions and errors are compared with existing integral order results from the literature. Graphical analysis is also performed at normal and radial velocity components for different fluid and fractional parameters. Analysis reveals that a few parameters are showing different behavior in a fractional environment as compared to existing integer-order cases from the literature. These findings affirm the importance of fractional calculus in terms of more generalized analysis of physical phenomena.
A significant correlation between financial news with stock market trends has been explored extensively. However, very little research has been conducted for stock prediction models that utilize news categories, weighted according to their relevance with the target stock. In this paper, we show that prediction accuracy can be enhanced by incorporating weighted news categories simultaneously into the prediction model. We suggest utilizing news categories associated with the structural hierarchy of the stock market: that is, news categories for the market, sector, and stock-related news. In this context, Long Short-Term Memory (LSTM) based Weighted and Categorized News Stock prediction model (WCN-LSTM) is proposed. The model incorporates news categories with their learned weights simultaneously. To enhance the effectiveness, sophisticated features are integrated into WCN-LSTM. These include, hybrid input, lexicon-based sentiment analysis, and deep learning to impose sequential learning. Experiments have been performed for the case of the Pakistan Stock Exchange (PSX) using different sentiment dictionaries and time steps. Accuracy and F1-score are used to evaluate the prediction model. We have analyzed the WCN-LSTM results thoroughly and identified that WCN-LSTM performs better than the baseline model. Moreover, the sentiment lexicon HIV4 along with time steps 3 and 7, optimized the prediction accuracy. We have conducted statistical analysis to quantitatively assess our findings. A qualitative comparison of WCN-LSTM with existing prediction models is also presented to highlight its superiority and novelty over its counterparts.
In this manuscript, extension of residual power series method (RPSM) is proposed for ordinary differential equations with boundary conditions. The extended algorithm is tested against different boundary value problems (BVPs), and results are compared with other schemes to show the reliability of proposed methodology. Analysis reveals that extended approach can easily handle BVPs and provide convergent series solution without discretization, perturbation or linearization. Hence, practically RPSM is better choice for scientists and researchers working in different field of engineering and sciences.
Synchronized canoeing is a cyclically repetitive sport, and two canoeists need to work in concert with each other’s power patterns and paddle movements to achieve great results. Most of the analyses of synchronized canoeing focus on the mechanics and work output of oars. This article proposes a motion-capture method based on inertial sensors. The extended Kalman filter method is used to fuse the sensor information and reconstruct human movements. Four sets of joint angular movement data of the shoulders and elbows of two canoeists are extracted, respectively, for kinematics analysis with correlation coefficient and dynamic time warping (DTW) method. The experimental results show that there is a significant correlation ( $r=0.9605$ ) between the shoulder joint angles of the synchronized canoeists. The DTW method can also clearly analyze the posture coordination of two canoeists. According to the analysis results of upper limb kinematics, this research can evaluate the synchronization effect of synchronized canoeing from the perspective of kinematics, help canoeists improve their technical movements, and help coaches reasonably select athletes with matching skills and styles.
Infectious diseases are always alarming for the survival of human life and are a key concern in the public health domain. Therefore, early diagnosis of these infectious diseases is a high demand for modern-era healthcare systems. Novel general infectious diseases such as coronavirus are infectious diseases that cause millions of human deaths across the globe in 2020. Therefore, early, robust recognition of general infectious diseases is the desirable requirement of modern intelligent healthcare systems. This systematic study is designed under Kitchenham guidelines and sets different RQs (research questions) for robust recognition of general infectious diseases. From 2018 to 2021, four electronic databases, IEEE, ACM, Springer, and ScienceDirect, are used for the extraction of research work. These extracted studies delivered different schemes for the accurate recognition of general infectious diseases through different machine learning techniques with the inclusion of deep learning and federated learning models. A framework is also introduced to share the process of detection of infectious diseases by using machine learning models. After the filtration process, 21 studies are extracted and mapped to defined RQs. In the future, early diagnosis of infectious diseases will be possible through wearable health monitoring cages. Moreover, these gages will help to reduce the time and death rate by detection of severe diseases at starting stage.
Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal assistants, to understand and explain the decision-making process of the artificial intelligence (AI) model. Smart homes embedded with smart devices and sensors enabled many context-aware applications to recognize physical activities. This study presents XAI-HAR, a novel XAI-empowered human activity recognition (HAR) approach based on key features identified from the data collected from sensors located at different places in a smart home. XAI-HAR identifies a set of new features (i.e., the total number of sensors used in a specific activity), as physical key features selection (PKFS) based on weighting criteria. Next, it presents statistical key features selection (SKFS) (i.e., mean, standard deviation) to handle the outliers and higher class variance. The proposed XAI-HAR is evaluated using machine learning models, namely, random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB) and deep learning models such as deep neural network (DNN), convolution neural network (CNN), and CNN-based long short-term memory (CNN-LSTM). Experiments demonstrate the superior performance of XAI-HAR using RF classifier over all other machine learning and deep learning models. For explainability, XAI-HAR uses Local Interpretable Model Agnostic (LIME) with an RF classifier. XAI-HAR achieves 0.96% of F-score for health and dementia classification and 0.95 and 0.97% for activity recognition of dementia and healthy individuals, respectively.
In the past few years, due to the increased usage of internet, smartphones, sensors and digital cameras, more than a million images are generated and uploaded daily on social media platforms. The massive generation of such multimedia contents has resulted in an exponential growth in the stored and shared data. Certain ever-growing image repositories, consisting of medical images, satellites images, surveillance footages, military reconnaissance, fingerprints and scientific data etc., has increased the motivation for developing robust and efficient search methods for image retrieval as per user requirements. Hence, it is need of the hour to search and retrieve relevant images efficiently and with good accuracy. The current research focuses on Content-based Image Retrieval (CBIR) and explores well-known transfer learning-based classifiers such as VGG16, VGG19, EfficientNetB0, ResNet50 and their variants. These deep transfer leaners are trained on three benchmark image datasets i.e., CIFAR-10, CIFAR-100 and CINIC-10 containing 10, 100, and 10 classes respectively. In total 16 customized models are evaluated on these benchmark datasets and 96% accuracy is achieved for CIFAR-10 while 83% accuracy is achieved for CIFAR-100.
In this manuscript, modification of homotopy perturbation method (HPM) is proposed for integro-differential equations by coupling the least square method (LSM) with HPM. Improved accuracy in a very few iterations is the general advantage of this technique. The proposed method is applied to different higher order integro-differential equations of linear and nonlinear nature, and results are compared with exact as well as available solutions from the literature. Numerical and graphical analysis reveal that the proposed algorithm is reliable for integro-differential equations and hence can be utilized for more complex problems.
Social media has become a driving force for social change in the global society. Events that take place in one part of the world can quickly reverberate across the globe due to the vast amount of data generated on these platforms. However, developers of these platforms face numerous challenges in keeping cyberspace as inclusive and healthy as possible. In recent years, there has been an increase in offensive and hate speech on social media. Manual efforts to address this issue have been inadequate due to the vast scope of the problem. Therefore, there is a need for an automated technique that can detect and remove offensive and hateful comments before they can cause harm. In this research, we use transfer learning to utilize pre-trained FastText Urdu word embeddings and multi-lingual BERT embeddings (RoBERTa) for our task. We also develop an Urdu language hate lexicon and use it to create an annotated dataset of 7800 Urdu tweets. Our results show that RoBERTa is able to achieve a macro F1-score of 0.82 on our multi-class classification task, outperforming deep learning and machine learning baseline models.
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5,953 members
Muhammad Tariq
  • Department of Electrical engineering
Muhammad Rafi
  • Department of Computer Science
Muhammad Imran Babar
  • Department of Computer Science
M. Farasat Shamir
  • Department of Sciences and Humanities
Muhammad Aamir Khan
  • Department of Computer Science
AK Brohi Road, H11/4, 44000, Lahore, Punjab, Pakistan
Head of institution
Dr. M. Ayub Alvi
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