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Most existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. In this paper, we develop a deep learning model for the sentiments expressed in this under-resourced language. We develop an open-source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. The objectives of this work are bi-fold (a) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu; and (b) measurement of up-to-date model performance using a corpus. For their assessment, we performed binary and ternary classification studies utilizing another model, namely long short-term memory (LSTM), recurrent convolutional neural network (RCNN) Rule-Based, N-gram, support vector machine , convolutional neural network, and LSTM. The RCNN model surpasses standard models with 84.98% accuracy for binary classification and 68.56% accuracy for ternary classification. To facilitate other researchers working in the same domain, we have open-sourced the corpus and code developed for this research.
Iqra Safder; Zainab Mehmood; Raheem Sarwar; Saeed‐Ul Hassan; Farooq Zaman; Rao Muhammad Adeel Nawab; Faisal Bukhari; Rabeeh Ayaz Abbasi; Salem Alelyani; Naif Radi Aljohani; Raheel Nawaz. Sentiment analysis for Urdu online reviews using deep learning models. Expert Systems 2021, e12751 .
AMA StyleIqra Safder, Zainab Mehmood, Raheem Sarwar, Saeed‐Ul Hassan, Farooq Zaman, Rao Muhammad Adeel Nawab, Faisal Bukhari, Rabeeh Ayaz Abbasi, Salem Alelyani, Naif Radi Aljohani, Raheel Nawaz. Sentiment analysis for Urdu online reviews using deep learning models. Expert Systems. 2021; ():e12751.
Chicago/Turabian StyleIqra Safder; Zainab Mehmood; Raheem Sarwar; Saeed‐Ul Hassan; Farooq Zaman; Rao Muhammad Adeel Nawab; Faisal Bukhari; Rabeeh Ayaz Abbasi; Salem Alelyani; Naif Radi Aljohani; Raheel Nawaz. 2021. "Sentiment analysis for Urdu online reviews using deep learning models." Expert Systems , no. : e12751.
In-text citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.
Sehrish Iqbal; Saeed-Ul Hassan; Naif Radi Aljohani; Salem Alelyani; Raheel Nawaz; Lutz Bornmann. A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies. Scientometrics 2021, 1 -49.
AMA StyleSehrish Iqbal, Saeed-Ul Hassan, Naif Radi Aljohani, Salem Alelyani, Raheel Nawaz, Lutz Bornmann. A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies. Scientometrics. 2021; ():1-49.
Chicago/Turabian StyleSehrish Iqbal; Saeed-Ul Hassan; Naif Radi Aljohani; Salem Alelyani; Raheel Nawaz; Lutz Bornmann. 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies." Scientometrics , no. : 1-49.
Sensors and sensor networks are the future of fully automated industry solutions. With more capability and complex machinery, the requirements for sensing in larger factories are critical, considering the data amount, latency, and the number of sensors in operation. Given the excellent time-critical operation, bandwidth and the number of devices connected, the 5G indoor femtocells could prove an excellent option for building industrial sensor grids. For more flexibility in control and reliability, operating the 5G indoor femtocell network in license-free frequency bands could be an alternative to commercial 5G services. The 5G networks incorporate a very dense network of indoor femtocells. The Femtocells also enhance data rates, indoor performance, and coverage area both in residential and industrial environments. Therefore, keeping in view the above-stated actualities, this paper addresses different indoor scenarios for radio wave propagation and simulates several path loss models to calculate the likely and most suitable propagation model for indoor signaling. Multiple models for frequencies in the unlicensed band below 6 GHz and above 6 GHz (licensed) 5G femtocells are discussed in the paper considering the constraints of material types, attenuation due to obstacles, various floors, carrier frequency, and distance from the transmitter. The comparative analysis indicates that the ITU model and Keenan-Motley model give the highest path loss in residential and industrial environments, respectively, while the log-distance model has the lowest path loss in both environments for below 6 GHz frequencies in the unlicensed spectrum. For the above 6 GHz licensed bands, the Alpha Beta Gamma (ABG) model and Path Loss Exponent (CIF) model are observed to have the minimum path loss difference.
Noman Shabbir; Lauri Kütt; Muhammad M. Alam; Priit Roosipuu; Muhammad Jawad; Muhammad B. Qureshi; Ali R. Ansari; Raheel Nawaz. Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks. Physical Communication 2021, 47, 101371 .
AMA StyleNoman Shabbir, Lauri Kütt, Muhammad M. Alam, Priit Roosipuu, Muhammad Jawad, Muhammad B. Qureshi, Ali R. Ansari, Raheel Nawaz. Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks. Physical Communication. 2021; 47 ():101371.
Chicago/Turabian StyleNoman Shabbir; Lauri Kütt; Muhammad M. Alam; Priit Roosipuu; Muhammad Jawad; Muhammad B. Qureshi; Ali R. Ansari; Raheel Nawaz. 2021. "Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks." Physical Communication 47, no. : 101371.
The growing use of nonlinear devices is introducing harmonics in power system networks that result in distortion of current and voltage signals causing damage to power distribution systems. Therefore, in power systems, the elimination of harmonics is of great significance. This paper presents an efficient techno-economical approach to suppress harmonics and improve the power factor in power distribution networks using Shunt Hybrid Active Power Filters (SHAPF) based on neural network algorithms like Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). The objective of the proposed algorithms for SHAPF is to enhance system performance by reducing Total Harmonic Distortion (THD). In our filter design approach, we tested and compared conventional pq0 theory and neural networks to detect the harmonics present in the power system. Moreover, for the regulation of DC supply to the inverter of the SHAPF, the conventional PI controller and neural networks-based controllers are used and compared. The applicability of the proposed filter is tested for three different nonlinear load cases. The simulation results show that the neural networks-based filter control techniques satisfy all international standards with minimum current THD, neutral wire current elimination, and small DC voltage fluctuations for voltage regulation current. Furthermore, the three neural network architectures are tested and compared based on accuracy and computational complexity, with RNN outperforming the rest.
Muzammil Iqbal; Muhammad Jawad; Mujtaba Hussain Jaffery; Saleem Akhtar; Muhammad Nadeem Rafiq; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination. IEEE Access 2021, 9, 69913 -69925.
AMA StyleMuzammil Iqbal, Muhammad Jawad, Mujtaba Hussain Jaffery, Saleem Akhtar, Muhammad Nadeem Rafiq, Muhammad Bilal Qureshi, Ali R. Ansari, Raheel Nawaz. Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination. IEEE Access. 2021; 9 ():69913-69925.
Chicago/Turabian StyleMuzammil Iqbal; Muhammad Jawad; Mujtaba Hussain Jaffery; Saleem Akhtar; Muhammad Nadeem Rafiq; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. 2021. "Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination." IEEE Access 9, no. : 69913-69925.
This paper presents a novel incentive-based load shedding management scheme within a microgrid environment equipped with the required IoT infrastructure. The proposed mechanism works on the principles of reverse combinatorial auction. We consider a region of multiple consumers who are willing to curtail their load in the peak hours in order to gain some incentives later. Using the properties of combinatorial auctions, the participants can bid in packages or combinations in order to maximize their and overall social welfare of the system. The winner determination problem of the proposed combinatorial auction, determined using particle swarm optimization algorithm and hybrid genetic algorithm, is also presented in this paper. The performance evaluation and stability test of the proposed scheme are simulated using MATLAB and presented in this paper. The results indicate that combinatorial auctions are an excellent choice for load shedding management where a maximum of 50 users participate.
Bizzat Zaidi; Ihsan Ullah; Musharraf Alam; Bamidele Adebisi; Atif Azad; Ali Ansari; Raheel Nawaz. Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure. Sensors 2021, 21, 1935 .
AMA StyleBizzat Zaidi, Ihsan Ullah, Musharraf Alam, Bamidele Adebisi, Atif Azad, Ali Ansari, Raheel Nawaz. Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure. Sensors. 2021; 21 (6):1935.
Chicago/Turabian StyleBizzat Zaidi; Ihsan Ullah; Musharraf Alam; Bamidele Adebisi; Atif Azad; Ali Ansari; Raheel Nawaz. 2021. "Incentive Based Load Shedding Management in a Microgrid Using Combinatorial Auction with IoT Infrastructure." Sensors 21, no. 6: 1935.
Graph encoding methods have been proven exceptionally useful in many classification tasks — from molecule toxicity prediction to social network recommendations. However, most of the existing methods are designed to work in a centralized environment that requires the whole graph to be kept in memory. Moreover, scaling them on very large networks remains a challenge. In this work, we propose a distributed and permutation invariant graph embedding method denoted as Distributed Graph Statistical Distance (DGSD) that extracts graph representation on independently distributed machines. DGSD finds nodes’ local proximity by considering only nodes’ degree, common neighbors and direct connectivity that allows it to run in the distributed environment. On the other hand, the linear space complexity of DGSD makes it suitable for processing large graphs. We show the scalability of DGSD on sufficiently large random and real-world networks and evaluate its performance on various bioinformatics and social networks with the implementation in a distributed computing environment.
Anwar Said; Saeed-Ul Hassan; Suppawong Tuarob; Raheel Nawaz; Mudassir Shabbir. DGSD: Distributed graph representation via graph statistical properties. Future Generation Computer Systems 2021, 119, 166 -175.
AMA StyleAnwar Said, Saeed-Ul Hassan, Suppawong Tuarob, Raheel Nawaz, Mudassir Shabbir. DGSD: Distributed graph representation via graph statistical properties. Future Generation Computer Systems. 2021; 119 ():166-175.
Chicago/Turabian StyleAnwar Said; Saeed-Ul Hassan; Suppawong Tuarob; Raheel Nawaz; Mudassir Shabbir. 2021. "DGSD: Distributed graph representation via graph statistical properties." Future Generation Computer Systems 119, no. : 166-175.
Urban visual pollution is increasingly affecting the built-up areas of the rapidly urbanizing planet. Outdoor advertisements are the key visual pollution objects affecting the visual pollution index and revenue generation potential of a place. Current practices of uninformed and uncontrolled outdoor advertising (especially billboards) impairs effective control of visual pollution in developing countries. Improving this can result in over 20% reduction of visual pollution. This article presents a spatial decision support system (SDSS) to facilitate all the stakeholders (development control authorities, advertisers, billboard owners, and the public) in balancing the optimal positioning of billboards under the governing regulations. In terms of its technical implementation, SDSS is based on well-known geospatial open source technologies and uses an analytical hierarchy process AHP-inspired approach in spatial decision-making. It can help users through its category-specific user interface to identify potential sites to position new billboards and the selection of boards from existing sites based on a wide variety of characteristics. The observations of all stakeholders have been recorded through panel feedback to assess the system’s initial effectiveness. The proposed system has been found functional in identifying hot spots for the focused management and exploration of the best suitable sites for new billboards. So, it helps the advertising agencies, urban authorities, and city councils in better planning and management of existing billboard locations to optimize revenue and improve urban aesthetics. The system can be replicated in other countries irrespective of spatial boundaries by incorporating jurisdictional rules and regulations.
Khydija Wakil; Ali Tahir; Muhammad Hussnain; Abdul Waheed; Raheel Nawaz. Mitigating Urban Visual Pollution through a Multistakeholder Spatial Decision Support System to Optimize Locational Potential of Billboards. ISPRS International Journal of Geo-Information 2021, 10, 60 .
AMA StyleKhydija Wakil, Ali Tahir, Muhammad Hussnain, Abdul Waheed, Raheel Nawaz. Mitigating Urban Visual Pollution through a Multistakeholder Spatial Decision Support System to Optimize Locational Potential of Billboards. ISPRS International Journal of Geo-Information. 2021; 10 (2):60.
Chicago/Turabian StyleKhydija Wakil; Ali Tahir; Muhammad Hussnain; Abdul Waheed; Raheel Nawaz. 2021. "Mitigating Urban Visual Pollution through a Multistakeholder Spatial Decision Support System to Optimize Locational Potential of Billboards." ISPRS International Journal of Geo-Information 10, no. 2: 60.
Since the coronavirus disease (COVID-19) outbreak in December 2019, studies have been addressing diverse aspects in relation to COVID-19 and Variant of Concern 202012/01 (VOC 202012/01) such as potential symptoms and predictive tools. However, limited work has been performed towards the modelling of complex associations between the combined demographic attributes and varying nature of the COVID-19 infections across the globe. This study presents an intelligent approach to investigate the multi-dimensional associations between demographic attributes and COVID-19 global variations. We gather multiple demographic attributes and COVID-19 infection data (by 8 January 2021) from reliable sources, which are then processed by intelligent algorithms to identify the significant associations and patterns within the data. Statistical results and experts' reports indicate strong associations between COVID-19 severity levels across the globe and certain demographic attributes, e.g. female smokers, when combined together with other attributes. The outcomes will aid the understanding of the dynamics of disease spread and its progression, which in turn may support policy makers, medical specialists and society, in better understanding and effective management of the disease.
Wasiq Khan; Abir Hussain; Sohail Ahmed Khan; Mohammed Al-Jumailey; Raheel Nawaz; Panos Liatsis. Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching. Royal Society Open Science 2021, 8, 201823 .
AMA StyleWasiq Khan, Abir Hussain, Sohail Ahmed Khan, Mohammed Al-Jumailey, Raheel Nawaz, Panos Liatsis. Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching. Royal Society Open Science. 2021; 8 (1):201823.
Chicago/Turabian StyleWasiq Khan; Abir Hussain; Sohail Ahmed Khan; Mohammed Al-Jumailey; Raheel Nawaz; Panos Liatsis. 2021. "Analysing the impact of global demographic characteristics over the COVID-19 spread using class rule mining and pattern matching." Royal Society Open Science 8, no. 1: 201823.
This paper aims at an important task of computing the webometrics university ranking and investigating if there exists a correlation between webometrics university ranking and the rankings provided by the world prominent university rankers such as QS world university ranking, for the time period of 2005–2016. However, the webometrics portal provides the required data for the recent years only, starting from 2012, which is insufficient for such an investigation. The rest of the required data can be obtained from the internet archive. However, the existing data extraction tools are incapable of extracting the required data from internet archive, due to unusual link structure that consists of web archive link, year, date, and target links. We developed an internet archive scrapper and extract the required data, for the time period of 2012–2016. After extracting the data, the webometrics indicators were quantified, and the universities were ranked accordingly. We used correlation coefficient to identify the relationship between webometrics university ranking computed by us and the original webometrics university ranking, using the spearman and pearson correlation measures. Our findings indicate a strong correlation between ours and the webometrics university rankings, which proves that the applied methodology can be used to compute the webometrics university ranking of those years for which the ranking is not available, i.e., from 2005 to 2011. We compute the webometrics ranking of the top 30 universities of North America, Europe and Asia for the time period of 2005–2016. Our findings indicate a positive correlation for North American and European universities, but weak correlation for Asian universities. This can be explained by the fact that Asian universities did not pay much attention to their websites as compared to the North American and European universities. The overall results reveal the fact that North American and European universities are higher in rank as compared to Asian universities. To the best of our knowledge, such an investigation has been executed for the very first time by us and no recorded work resembling this has been done before.
Raheem Sarwar; Afifa Zia; Raheel Nawaz; Ayman Fayoumi; Naif Radi Aljohani; Saeed-Ul Hassan. Webometrics: evolution of social media presence of universities. Scientometrics 2021, 126, 951 -967.
AMA StyleRaheem Sarwar, Afifa Zia, Raheel Nawaz, Ayman Fayoumi, Naif Radi Aljohani, Saeed-Ul Hassan. Webometrics: evolution of social media presence of universities. Scientometrics. 2021; 126 (2):951-967.
Chicago/Turabian StyleRaheem Sarwar; Afifa Zia; Raheel Nawaz; Ayman Fayoumi; Naif Radi Aljohani; Saeed-Ul Hassan. 2021. "Webometrics: evolution of social media presence of universities." Scientometrics 126, no. 2: 951-967.
The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety.
Naveed Taimoor; Ikramullah Khosa; Muhammad Jawad; Jahanzeb Akhtar; Imran Ghous; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S. IEEE Access 2020, 8, 223271 -223286.
AMA StyleNaveed Taimoor, Ikramullah Khosa, Muhammad Jawad, Jahanzeb Akhtar, Imran Ghous, Muhammad Bilal Qureshi, Ali R. Ansari, Raheel Nawaz. Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S. IEEE Access. 2020; 8 (99):223271-223286.
Chicago/Turabian StyleNaveed Taimoor; Ikramullah Khosa; Muhammad Jawad; Jahanzeb Akhtar; Imran Ghous; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. 2020. "Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S." IEEE Access 8, no. 99: 223271-223286.
It is indeed a challenge for the existing machine learning approaches to segregate the hateful content from the one that is merely offensive. One prevalent reason for low accuracy of hate detection with the current methodologies is that these techniques treat hate classification as a multi-class problem. In this work, we present the hate identification on the social media as a multi-label problem. To this end, we propose a CNN-based service framework called "HateClassify" for labeling the social media contents as the hate speech, offensive, or non-offensive. Results demonstrate that the multi-class classification accuracy for the CNN based approaches particularly Sequential CNN (SCNN) is competitive and even higher than certain state-of-the-art classifiers. Moreover, in the multi-label classification problem, sufficiently high performance is exhibited by the SCNN among other CNN-based techniques. The results have shown that using multi-label classification instead of multi-class classification, hate speech detection is increased up to 20%.
Muhammad Usman Shahid Khan; Assad Abbas; Attiqa Rehman; Raheel Nawaz. HateClassify: A Service Framework for Hate Speech Identification on Social Media. IEEE Internet Computing 2020, 25, 40 -49.
AMA StyleMuhammad Usman Shahid Khan, Assad Abbas, Attiqa Rehman, Raheel Nawaz. HateClassify: A Service Framework for Hate Speech Identification on Social Media. IEEE Internet Computing. 2020; 25 (1):40-49.
Chicago/Turabian StyleMuhammad Usman Shahid Khan; Assad Abbas; Attiqa Rehman; Raheel Nawaz. 2020. "HateClassify: A Service Framework for Hate Speech Identification on Social Media." IEEE Internet Computing 25, no. 1: 40-49.
Spring Loaded Pantographs (SLPs) are frequently used in designing lightweight limbs for multi-legged robots. Quadruped robots that incorporate cable-pulled SLP legs have proven to be agile, robust and capable of conserving energy during their gait cycle. In such designs, the extension of the distal segments via the knee joint is dependent upon the length of the cable. In this article we propose the use of an Elastically Loaded Scissors Mechanism (ELS Mechanism or ELSM), which is a variant of the SLP. Driven by ’pulling’ onto the proximal joint of the scissors as opposed to the distal joint, this proposed leg utilizes the increased mechanical advantage of the scissors mechanism to ’amplify’ input angles to larger output displacement by the knee joint. Analysis and Simulations reveal that the proposed mechanism achieves increased motion speed as compared to the SLP mechanism. This, however, comes at the cost of higher load on the actuator which serves as an engineering trade-off. This is validated by experimentation using motion capture and load motor techniques of the SLP and ELS configurations in a physical quadruped robot.
Muhammad Hamza Asif Nizami; Zaid Ahsan Shah; Yasar Ayaz; Muhammad Jawad Khan; Sara Ali; Muhammad Naveed; Khalid Akhtar; Darren Dancey; Raheel Nawaz. Proximal Actuation of an Elastically Loaded Scissors Mechanism for the Leg Design of a Quadruped Robot. IEEE Access 2020, 8, 208240 -208252.
AMA StyleMuhammad Hamza Asif Nizami, Zaid Ahsan Shah, Yasar Ayaz, Muhammad Jawad Khan, Sara Ali, Muhammad Naveed, Khalid Akhtar, Darren Dancey, Raheel Nawaz. Proximal Actuation of an Elastically Loaded Scissors Mechanism for the Leg Design of a Quadruped Robot. IEEE Access. 2020; 8 ():208240-208252.
Chicago/Turabian StyleMuhammad Hamza Asif Nizami; Zaid Ahsan Shah; Yasar Ayaz; Muhammad Jawad Khan; Sara Ali; Muhammad Naveed; Khalid Akhtar; Darren Dancey; Raheel Nawaz. 2020. "Proximal Actuation of an Elastically Loaded Scissors Mechanism for the Leg Design of a Quadruped Robot." IEEE Access 8, no. : 208240-208252.
Foreign Exchange or Forex is the sale purchase market point of foreign currency pairs. Due to the high volatility in the forex market, it is difficult to predict the future price of any currency pair. This study shows that a significant enhancement in the prediction of forex price can be achieved by incorporating domain knowledge in the process of training machine learning models. The proposed system integrates the Forex Loss Function (FLF) into a Long Short-Term Memory model called FLF-LSTM — that minimizes the difference between the actual and predictive average of Forex candles. Using the data of 10,078 four-hour candles of EURUSD pair, it is found that compared to the classic LSTM model, the proposed FLF-LSTM system shows a decrease in overall mean absolute error rate by 10.96%. It is also reported that the error in forecasting the high and low prices is reduced by 10% and 9%, respectively. The proposed model, in comparison to the Recurrent Neural Network-based prediction system, shows an overall reduction of 73.57% in mean absolute error, by exhibiting up to 68.71% and 72.31% error reduction in high and low prices, respectively. In comparison to Auto-Regressive Integrated Moving Average, our proposed model shows a 13% reduced error. Specifically, in the open, high, and low prices, the error is reduced by 28.5%, 14.2%, 9.3%, respectively. Finally, we compare our model with another well-known time series forecasting model, i.e., FB Prophet — where FLF-LSTM demonstrates 31.8%, 47.7%, 23.6%, 47.7% error reduction in open, high, low, and close prices, respectively. The data and the code used in this study can be accessed at the following URL: https://github.com/slab-itu/forex_flf_lstm.
Salman Ahmed; Saeed-Ul Hassan; Naif Radi Aljohani; Raheel Nawaz. FLF-LSTM: A novel prediction system using Forex Loss Function. Applied Soft Computing 2020, 97, 106780 .
AMA StyleSalman Ahmed, Saeed-Ul Hassan, Naif Radi Aljohani, Raheel Nawaz. FLF-LSTM: A novel prediction system using Forex Loss Function. Applied Soft Computing. 2020; 97 ():106780.
Chicago/Turabian StyleSalman Ahmed; Saeed-Ul Hassan; Naif Radi Aljohani; Raheel Nawaz. 2020. "FLF-LSTM: A novel prediction system using Forex Loss Function." Applied Soft Computing 97, no. : 106780.
The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.
Waqar Ahmed; Hammad Ansari; Bilal Khan; Zahid Ullah; Sahibzada Muhammad Ali; Chaudhry Arshad Arshad Mehmood; Muhammad B. Qureshi; Iqrar Hussain; Muhammad Jawad; Muhammad Usman Shahid Khan; Amjad Ullah; Raheel Nawaz. Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts. IEEE Access 2020, 8, 185059 -185078.
AMA StyleWaqar Ahmed, Hammad Ansari, Bilal Khan, Zahid Ullah, Sahibzada Muhammad Ali, Chaudhry Arshad Arshad Mehmood, Muhammad B. Qureshi, Iqrar Hussain, Muhammad Jawad, Muhammad Usman Shahid Khan, Amjad Ullah, Raheel Nawaz. Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts. IEEE Access. 2020; 8 (99):185059-185078.
Chicago/Turabian StyleWaqar Ahmed; Hammad Ansari; Bilal Khan; Zahid Ullah; Sahibzada Muhammad Ali; Chaudhry Arshad Arshad Mehmood; Muhammad B. Qureshi; Iqrar Hussain; Muhammad Jawad; Muhammad Usman Shahid Khan; Amjad Ullah; Raheel Nawaz. 2020. "Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts." IEEE Access 8, no. 99: 185059-185078.
The unprompted patient’s and inimitable physician’s experience shared on online health communities (OHCs) contain a wealth of unexploited knowledge. Med Help and eHealth are some of the online health communities offering new insights and solutions to all health issues. Diabetes mellitus (DM), thyroid disorders and tuberculosis (TB) are chronic diseases increasing rapidly every year. As part of the project described in this article comments related to the diseases from Med Help were collected. The comments contain the patient and doctor discussions in an unstructured format. The sematic vision of the internet of things (IoT) plays a vital role in organizing the collected data. We pre-processed the data using standard natural language processing techniques and extracted the essential features of the words using the chi-squared test. After preprocessing the documents, we clustered them using the K-means++ algorithm, which is a popular centroid-based unsupervised iterative machine learning algorithm. A generative probabilistic model (LDA) was used to identify the essential topic in each cluster. This type of framework will empower the patients and doctors to identify the similarity and dissimilarity about the various diseases and important keywords among the diseases in the form of symptoms, medical tests and habits.
Pradeepa Sampath; Gayathiri Packiriswamy; Nishmitha Pradeep Kumar; Vimal Shanmuganathan; Oh-Young Song; Usman Tariq; Raheel Nawaz. IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique. Electronics 2020, 9, 1469 .
AMA StylePradeepa Sampath, Gayathiri Packiriswamy, Nishmitha Pradeep Kumar, Vimal Shanmuganathan, Oh-Young Song, Usman Tariq, Raheel Nawaz. IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique. Electronics. 2020; 9 (9):1469.
Chicago/Turabian StylePradeepa Sampath; Gayathiri Packiriswamy; Nishmitha Pradeep Kumar; Vimal Shanmuganathan; Oh-Young Song; Usman Tariq; Raheel Nawaz. 2020. "IoT Based Health—Related Topic Recognition from Emerging Online Health Community (Med Help) Using Machine Learning Technique." Electronics 9, no. 9: 1469.
Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.
Muhammad Jawad; Muhammad Bilal Qureshi; Sahibzada Muhammad Ali; Noman Shabbir; Muhammad Usman Shahid Khan; Afnan Aloraini; Raheel Nawaz. A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique. Sensors 2020, 20, 4842 .
AMA StyleMuhammad Jawad, Muhammad Bilal Qureshi, Sahibzada Muhammad Ali, Noman Shabbir, Muhammad Usman Shahid Khan, Afnan Aloraini, Raheel Nawaz. A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique. Sensors. 2020; 20 (17):4842.
Chicago/Turabian StyleMuhammad Jawad; Muhammad Bilal Qureshi; Sahibzada Muhammad Ali; Noman Shabbir; Muhammad Usman Shahid Khan; Afnan Aloraini; Raheel Nawaz. 2020. "A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique." Sensors 20, no. 17: 4842.
In this paper, we address the problem of identifying the quality of citation as important or unimportant to the developments presented in the research papers. We gather features represented by four state-of-the-art machine learning techniques and combined them with newly engineered, natural language-based features. Using a known dataset of 465 citations, manually labeled by experts, our approach out-performed state-of-the-art by using fine-tuned Random Forest Classifier with 90.7% F1 score and 97.7% precision. We also employ Convolutional Neural Networks with AdamW optimizer with focal loss function - that converges quickly on small data to achieve considerably significant results.
Syyab Rahi; Iqra Safder; Sehrish Iqbal; Saeed-Ul Hassan; Iain Reid; Raheel Nawaz. Citation Classification Using Natural Language Processing and Machine Learning Models. Lecture Notes in Electrical Engineering 2020, 357 -365.
AMA StyleSyyab Rahi, Iqra Safder, Sehrish Iqbal, Saeed-Ul Hassan, Iain Reid, Raheel Nawaz. Citation Classification Using Natural Language Processing and Machine Learning Models. Lecture Notes in Electrical Engineering. 2020; ():357-365.
Chicago/Turabian StyleSyyab Rahi; Iqra Safder; Sehrish Iqbal; Saeed-Ul Hassan; Iain Reid; Raheel Nawaz. 2020. "Citation Classification Using Natural Language Processing and Machine Learning Models." Lecture Notes in Electrical Engineering , no. : 357-365.
Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive summarisation called HTSS. We extend the well-known pointer generator model for the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the science news website EurekaAlert (www.eurekalert.org). Our results show that our proposed HTSS model outperforms neural text simplification (NTS) on SARI score and abstractive text summarisation (ATS) on the ROUGE score. We further introduce a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and summarisation by 38.94% and 53.40%, respectively. We provide all code, models and corpora to the scientific community for future research at the following URL: https://github.com/slab-itu/HTSS/.
Farooq Zaman; Matthew Shardlow; Saeed-Ul Hassan; Naif Radi Aljohani; Raheel Nawaz. HTSS: A novel hybrid text summarisation and simplification architecture. Information Processing & Management 2020, 57, 102351 .
AMA StyleFarooq Zaman, Matthew Shardlow, Saeed-Ul Hassan, Naif Radi Aljohani, Raheel Nawaz. HTSS: A novel hybrid text summarisation and simplification architecture. Information Processing & Management. 2020; 57 (6):102351.
Chicago/Turabian StyleFarooq Zaman; Matthew Shardlow; Saeed-Ul Hassan; Naif Radi Aljohani; Raheel Nawaz. 2020. "HTSS: A novel hybrid text summarisation and simplification architecture." Information Processing & Management 57, no. 6: 102351.
Target detection and tracking is important in military as well as in civilian applications. In order to detect and track high-speed incoming threats, modern surveillance systems are equipped with multiple sensors to overcome the limitations of single-sensor based tracking systems. This research proposes the use of information from RADAR and Infrared sensors (IR) for tracking and estimating target state dynamics. A new technique is developed for information fusion of the two sensors in a way that enhances performance of the data association algorithm. The measurement acquisition and processing time of these sensors is not the same; consequently the fusion center measurements arrive out of sequence. To ensure the practicality of system, proposed algorithm compensates the Out of Sequence Measurements (OOSMs) in cluttered environment. This is achieved by a novel algorithm which incorporates a retrodiction based approach to compensate the effects of OOSMs in a modified Bayesian technique. The proposed modification includes a new gating strategy to fuse and select measurements from two sensors which originate from the same target. The state estimation performance is evaluated in terms of Root Mean Squared Error (RMSE) for both position and velocity, whereas, track retention statistics are evaluated to gauge the performance of the proposed tracking algorithm. The results clearly show that the proposed technique improves track retention and and false track discrimination (FTD).
Yifang Shi; Sundas Qayyum; Sufyan Ali Memon; Uzair Khan; Junaid Imtiaz; Ihsan Ullah; Darren Dancey; Raheel Nawaz. A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements. Sensors 2020, 20, 3821 .
AMA StyleYifang Shi, Sundas Qayyum, Sufyan Ali Memon, Uzair Khan, Junaid Imtiaz, Ihsan Ullah, Darren Dancey, Raheel Nawaz. A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements. Sensors. 2020; 20 (14):3821.
Chicago/Turabian StyleYifang Shi; Sundas Qayyum; Sufyan Ali Memon; Uzair Khan; Junaid Imtiaz; Ihsan Ullah; Darren Dancey; Raheel Nawaz. 2020. "A Modified Bayesian Framework for Multi-Sensor Target Tracking with Out-of-Sequence-Measurements." Sensors 20, no. 14: 3821.
The purpose of the study is to (a) contribute to annotating an Altmetrics dataset across five disciplines, (b) undertake sentiment analysis using various machine learning and natural language processing–based algorithms, (c) identify the best-performing model and (d) provide a Python library for sentiment analysis of an Altmetrics dataset. First, the researchers gave a set of guidelines to two human annotators familiar with the task of related tweet annotation of scientific literature. They duly labelled the sentiments, achieving an inter-annotator agreement (IAA) of 0.80 (Cohen’s Kappa). Then, the same experiments were run on two versions of the dataset: one with tweets in English and the other with tweets in 23 languages, including English. Using 6388 tweets about 300 papers indexed in Web of Science, the effectiveness of employed machine learning and natural language processing models was measured by comparing with well-known sentiment analysis models, that is, SentiStrength and Sentiment140, as the baseline. It was proved that Support Vector Machine with uni-gram outperformed all the other classifiers and baseline methods employed, with an accuracy of over 85%, followed by Logistic Regression at 83% accuracy and Naïve Bayes at 80%. The precision, recall and F1 scores for Support Vector Machine, Logistic Regression and Naïve Bayes were (0.89, 0.86, 0.86), (0.86, 0.83, 0.80) and (0.85, 0.81, 0.76), respectively.
Saeed-Ul Hassan; Aneela Saleem; Saira Hanif Soroya; Iqra Safder; Sehrish Iqbal; Saqib Jamil; Faisal Bukhari; Naif Radi Aljohani; Raheel Nawaz. Sentiment analysis of tweets through Altmetrics: A machine learning approach. Journal of Information Science 2020, 1 .
AMA StyleSaeed-Ul Hassan, Aneela Saleem, Saira Hanif Soroya, Iqra Safder, Sehrish Iqbal, Saqib Jamil, Faisal Bukhari, Naif Radi Aljohani, Raheel Nawaz. Sentiment analysis of tweets through Altmetrics: A machine learning approach. Journal of Information Science. 2020; ():1.
Chicago/Turabian StyleSaeed-Ul Hassan; Aneela Saleem; Saira Hanif Soroya; Iqra Safder; Sehrish Iqbal; Saqib Jamil; Faisal Bukhari; Naif Radi Aljohani; Raheel Nawaz. 2020. "Sentiment analysis of tweets through Altmetrics: A machine learning approach." Journal of Information Science , no. : 1.