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Article ; Online: Cluster analysis of urdu tweets

Zarmeen Nasim / Sajjad Haider

Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 5, Pp 2170-

2022 Volume 2179

Abstract: Document clustering allows a user to group semantically similar documents. It has been an interesting research area for the past many years and various methods and techniques have been developed. However, the research has primarily been limited to ... ...

Abstract	Document clustering allows a user to group semantically similar documents. It has been an interesting research area for the past many years and various methods and techniques have been developed. However, the research has primarily been limited to English and other high resource languages. For low-resource languages, such as Urdu, the area of document clustering is open to contributions. This work presents an experimental evaluation of clustering techniques on Urdu tweets. It is a challenging task to semantically cluster tweets due to their very short length. In this paper, various features, including sentence and phrase-level embeddings, TF-IDF features and document embeddings are extracted from tweets and clustering is performed using three different algorithms: K-Means, Bisecting K-Means, and Affinity Propagation algorithms. Furthermore, a comparison is performed with the traditional topic modeling approach. The results indicate that the TF-IDF features combined with the K-means clustering algorithm outperformed the adopted clustering techniques.
Keywords	Document clustering ; Topic modelling ; Unsupervised learning ; Feature extraction methods ; Document embeddings ; Urdu language processing ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	004
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Attention-based deep convolutional neural network for classification of generalized and focal epileptic seizures.

Gill, Taimur Shahzad / Zaidi, Syed Sajjad Haider / Shirazi, Muhammad Ayaz

Epilepsy & behavior : E&B

2024 Volume 155, Page(s) 109732

Abstract: Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure ... ...

Abstract	Epilepsy affects over 50 million people globally. Electroencephalography is critical for epilepsy diagnosis, but manual seizure classification is time-consuming and requires extensive expertise. This paper presents an automated multi-class seizure classification model using EEG signals from the Temple University Hospital Seizure Corpus ver. 1.5.2. 11 features including time-based correlation, time-based eigenvalues, power spectral density, frequency-based correlation, frequency-based eigenvalues, sample entropy, spectral entropy, logarithmic sum, standard deviation, absolute mean, and ratio of Daubechies D4 wavelet transformed coefficients were extracted from 10-second sliding windows across channels. The model combines multi-head self-attention mechanism with a deep convolutional neural network (CNN) to classify seven subtypes of generalized and focal epileptic seizures. The model achieved 0.921 weighted accuracy and 0.902 weighted F1 score in classifying focal onset non-motor, generalized onset non-motor, simple partial, complex partial, absence, tonic, and tonic-clonic seizures. In comparison, a CNN model without multi-head attention achieved 0.767 weighted accuracy. Ablation studies were conducted to validate the importance of transformer encoders and attention. The promising classification results demonstrate the potential of deep learning for handling EEG complexity and improving epilepsy diagnosis. This seizure classification model could enable timely interventions when translated into clinical practice.
Language	English
Publishing date	2024-04-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010587-3
ISSN	1525-5069 ; 1525-5050
ISSN (online)	1525-5069
ISSN	1525-5050
DOI	10.1016/j.yebeh.2024.109732
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Article ; Online: Assessing the utility of hybrid hydrological modeling over complex conditions of the Chitral basin, Pakistan

Zain Syed / Prince Mahmood / Sajjad Haider / Shakil Ahmad

Journal of Water and Climate Change, Vol 14, Iss 12, Pp 4444-

2023 Volume 4464

Abstract: Streamflow forecasting holds pivotal importance for planning and decision-making in the domain of water resources management. The Chitral basin in Pakistan is characterized by high altitude and glaciated terrain. Simulating streamflows in this type of ... ...

Abstract	Streamflow forecasting holds pivotal importance for planning and decision-making in the domain of water resources management. The Chitral basin in Pakistan is characterized by high altitude and glaciated terrain. Simulating streamflows in this type of region is challenging due to complex orography and uncertain climate data. This complexity persuaded us to explore three frameworks (soil and water assessment tool (SWAT), artificial neural network (ANN), and hybrid of SWAT–ANN (H2)) for simulating the Chitral river under two different climate datasets (observed climatology (OC) and reconciled gridded climatology (RGC)) to give all six model combinations. Model evaluation was done first by indices (Nash–Sutcliff efficiency, Kling–Gupta efficiency, coefficient of determination, percent bias, and root mean square error) based on which we further assigned scores to models reflecting their performance during calibration and validation epochs. The research revealed that ANN-RGC stood first with 53 points, followed by H2-RGC (50 points) and SWAT-RGC (45 points). Trailing behind in the fourth and fifth positions were SWAT-RGC and SWAT-OC (26 points each), respectively, while ANN-OC finished last (22 points). In addition, this study proposed a bias scaling approach for simulation biases resulting in reduction in recession and baseflow biases and specifically improved low-scoring models. Despite ANN's superiority over conventional models, it could be of limited utility in uncertain or data-scarce conditions. HIGHLIGHTS Reliable climate data hold pivotal importance in hydrological modeling.; Artificial neural networks scored the highest but were also found to be more sensitive to data quantity and quality.; The coupling harnessed the capabilities of the parent frameworks and performed well overall.; In uncertain data conditions, the soil and water assessment tool and hybrid models could be more suitable choices.; Implied linear scaling efficiently removed model biases.;
Keywords	ann ; climate uncertainty ; era5 land ; hybrid model ; streamflow forecasting ; swat ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350
Subject code	550
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	IWA Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The impact of rare earth Nd3+ cations on structural, spectral, magnetic and dielectric parameters of NiFe2O4 nanoparticles

Philips O. Agboola / Sajjad Haider / Imran Shakir

Journal of Taibah University for Science, Vol 16, Iss 1, Pp 392-

2022 Volume 400

Abstract: The co-precipitation route was used for the preparation of Nd-substituted NiFe2O4. The proposed general formula of prepared ferrite was NdxNiFe2-xO4. Here, “x” the moles of Nd3+ were 0.0, 0.0125, 0.025 and 0.05 in various compositions. The prepared ... ...

Abstract	The co-precipitation route was used for the preparation of Nd-substituted NiFe2O4. The proposed general formula of prepared ferrite was NdxNiFe2-xO4. Here, “x” the moles of Nd3+ were 0.0, 0.0125, 0.025 and 0.05 in various compositions. The prepared nanocrystallites were characterized using X-rays diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), vibrating sample magnetometer (VSM) and impedance analyzer. XRD patterns of all prepared samples were analyzed to confirm the phase of the materials and determine the lattice constant, crystallite size and X-rays density. The lattice parameter value varied between 8.3501 and 8.3549 Å. The average crystallite size was ∼15 nm. The FTIR spectral analysis was used to investigate the M-O stretching vibrational bands. FTIR spectra showed the stretching vibrational bands of Fe-O at octahedral and tetrahedral sites. After structural and spectral investigations, the prepared nanocrystallites were subjected to room temperature magnetic measurements by vibrating sample magnetometry (VSM) technique. The magnetic parameters were investigated from the VSM data. The magnetic parameters suggested the possible utilization of these nanocrystalline ferrites for various advanced technological applications like switching devices and high-frequency devices. The dielectric parameters were also investigated using an impedance analyzer.
Keywords	Rare earth ; nanocrystallites ; spinel ferrites ; XRD ; FTIR ; magnetic properties ; Science (General) ; Q1-390
Subject code	530
Language	English
Publishing date	2022-12-01T00:00:00Z
Publisher	Taylor & Francis Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Data for Heuristic Optimization of Electric Vehicles’ Charging Configuration Based on Loading Parameters

Sajjad Haider / Peter Schegner

Data, Vol 5, Iss 102, p

2020 Volume 102

Abstract: This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge the EVs. The data include lognormally sampled hourly sorted scenarios across 11 ... ...

Abstract	This dataset includes multiple files related to optimization of electric vehicles to minimize overloading in low voltage grids by varying the locations available to charge the EVs. The data include lognormally sampled hourly sorted scenarios across 11 charging locations for a stochastics-based Monte Carlo simulation. This simulation runs through 2 million scenarios based on actual probabilities to incorporate most possible situations. It also includes samples from normally distributed household electricity use scenarios based on agent-based modeling. The article includes the test grid parameters for simulation, which were used to create a benchmark grid in DigSilent Powerfactory software, as well as intermediate outputs defining worst case scenarios when electric vehicles were charged and results from three different optimization approaches involving a reduction in voltage drops, cable overloading and total line losses. The outputs from the benchmark grid were used to train a machine learning algorithm, the weights and codes for which are also attached. This trained network acted as the grid for subsequent iterative optimization procedures. Outputs are presented as a comparison between pre-optimization and post-optimization scenarios. The above dataset and procedure were repeated while varying the number of EVs between 0 and 100 in increments of 20, data for which are also attached. The data article supports a related submission titled “Minimization of Overloading Caused by Electric Vehicle (EV) Charging in Low Voltage Networks”.
Keywords	electric vehicles ; optimization ; low voltage ; network ; heuristic ; Bibliography. Library science. Information resources ; Z
Subject code	006
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development of daily bias-corrected ensemble precipitation estimates over the Upper Indus Basin of the Hindukush-Karakoram-Himalaya

Kashif Jamal / Xin Li / Yingying Chen / Sajjad Haider / Muhammad Rizwan / Shakil Ahmad

Journal of Water and Climate Change, Vol 14, Iss 10, Pp 3517-

2023 Volume 3538

Abstract: Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent ... ...

Abstract	Accurate precipitation estimates over space and time are critically important, particularly in data-scarce areas, for effective hydrological modeling and efficient regional water resources management. Gridded precipitation datasets are the preeminent alternative in such areas. However, gridded precipitation datasets contain different kinds of uncertainties owing to the retrieval algorithms used in their development. In this study, five precipitation datasets (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), APHRODITE, Climate Hazards Group Infra-Red Precipitation with Station data (CHIRPS), and PERSIANN) were evaluated, and an ensemble of daily precipitation datasets from 2001 to 2017 at a resolution of 0.05 degree was created based on three ensemble approaches (Bayesian model ensemble, relative bias-based ensemble, and correlation-based ensemble) over the Upper Indus basin. To improve the accuracy of the ensemble dataset, a linear bias correction technique is applied with respect to gauging precipitation. The accuracy of the bias-corrected ensemble dataset was evaluated using statistical and novelty categorical measures. A reasonable agreement was found between the ensemble and gauge precipitation (Pearson correlation 0.83–0.89 and relative bias 1–8.7 mm/month), while large biases were noted in five precipitation datasets (1.7–53.9 mm/month). The study suggests that utilizing ensemble approaches to gridded precipitation can significantly enhance the accuracy of the estimates compared to relying on a single precipitation dataset. HIGHLIGHTS The study developed bias-corrected precipitation estimates using three ensemble approaches.; The new relative bias-based ensemble approach estimates are slightly better than the existing ensemble approaches used in this study.; A nonlinear precipitation increase/decrease trend is found with altitude.; The direct use of gridded precipitation is not recommended due to the large biases present in each precipitation dataset.;
Keywords	bias correction ; ensemble approaches ; precipitation datasets ; upper indus basin ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350
Subject code	333 ; 910
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	IWA Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessment of Soft Computing Techniques for the Prediction of Suspended Sediment Loads in Rivers

Muhammad Adnan Khan / Jürgen Stamm / Sajjad Haider

Applied Sciences, Vol 11, Iss 8290, p

2021 Volume 8290

Abstract: A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft ... ...

Abstract	A key goal of sediment management is the quantification of suspended sediment load (SSL) in rivers. This research focused on a comparison of different means of suspended sediment estimation in rivers. This includes sediment rating curves (SRC) and soft computing techniques, i.e., local linear regression (LLR), artificial neural networks (ANN) and the wavelet-cum-ANN (WANN) method. Then, different techniques were applied to predict daily SSL at the Pirna and Magdeburg Stations of the Elbe River in Germany. By comparing the results of all the best models, it can be concluded that the soft computing techniques (LLR, ANN and WANN) better predicted the SSL than the SRC method. This is due to the fact that the former employed non-linear techniques for the data series reconstruction. The WANN models were the overall best performer. The WANN models in the testing phase showed a mean R 2 of 0.92 and a PBIAS of −0.59%. Additionally, they were able to capture the suspended sediment peaks with greater accuracy. They were more successful as they captured the dynamic features of the non-linear and time-variant suspended sediment load, while other methods used simple raw data. Thus, WANN models could be an efficient technique to simulate the SSL time series because they extract key features embedded in the SSL signal.
Keywords	sediment rating curves ; local linear regression ; artificial neural networks ; wavelet transform ; Gamma test ; M-test ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Language	English
Publishing date	2021-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Sensitivity analysis and optimization of land use/cover and aquifer parameters for improved calibration of hydrological model

Ammara Nusrat / Hamza Farooq Gabriel / Sajjad Haider / Mohsin Siddique

Mehran University Research Journal of Engineering and Technology, Vol 41, Iss 2, Pp 21-

2022 Volume 34

Abstract: Integrated Flood Analysis System (IFAS) model, based on Tank model philosophy, is a widely used flood forecasting model that has the capability to simulate the catchment processes of any river system provided the surface and aquifer parameters of each ... ...

Abstract	Integrated Flood Analysis System (IFAS) model, based on Tank model philosophy, is a widely used flood forecasting model that has the capability to simulate the catchment processes of any river system provided the surface and aquifer parameters of each sub-model are accurately calibrated. In this study, sensitivity analysis and optimization of hydrogeological parameters of Tank model have been performed to identify the key hydrogeological parameters and their significance in simulating the stream flows in the basins of two important rivers of Pakistan – Jhelum River and Chenab Rivers – respectively. IFAS includes a set of four sub-models namely: surface tank model, sub-surface tank model, aquifer tank model and river course model. Each of the sub-models simulates its own flow processes using surface/aquifer parameters. In this study, sensitivity analysis is performed to identify the parameters that significantly affect the model performance to simulate the flows in the river. Linear stochastic metamodels of Jhelum River and Chenab River Basins developed in this study played the role of metamodels or surrogate functions to determine the ranges of parameter values in different flow periods. The outcome demonstrates when the aquifer tank parameters values obtained from metamodels are applied, the simulation results in a nearly accurate calibration, which clearly indicates the efficiency of present methodology and the important role of hydrogeological parameters. Further, the analysis of the variability in the effectiveness of these parameters in different flow periods as well as for different catchments areas depicts spatial-temporal heterogeneous characteristics. This confirms that the analysis should be directed independently for each study basin because the results of sensitivity analysis are not transferable among catchments.
Keywords	Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Science ; Q
Subject code	550
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Mehran University of Engineering and Technology
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Optimal designing of grid-connected microgrid systems for residential and commercial applications in Pakistan

Syeda Sakina Zaidi / Syed Sajjad Haider Zaidi / Bilal Muhammad Khan / Lubna Moin

Heliyon, Vol 9, Iss 7, Pp e17990- (2023)

2023

Abstract: Conventional energy sources (CESs) are currently serving most of the global energy demands, but they will be substantially depleted as moving towards the end of this decade. The generation of electricity from such sources is causing the emission of ... ...

Abstract	Conventional energy sources (CESs) are currently serving most of the global energy demands, but they will be substantially depleted as moving towards the end of this decade. The generation of electricity from such sources is causing the emission of greenhouse gases that is resulting in deleterious effect on the environment along with changing climatic and energy patterns of the planet. Therefore, the world is heading toward decentralization, and microgrids are playing a key role in this process. The advantages of renewables, which are acknowledged globally as benign, eco-friendly, economical, and inexhaustible resources available worldwide, are to credit for such a massive surge in the utilization of renewable resources in microgrid technology. Despite having an enormous renewable energy potential, Pakistan spends a sizable portion of its budget on energy imports of coal, oil, and liquefied natural gas, however, with good planning, current energy crises might be eliminated or at least mitigated to a greater extent, assuring energy security, economic prosperity, and lower carbon emissions inside the country. This study considers the optimal component planning in a grid-connected microgrid with five objectives to achieve that are to reduce the cost of energy, increase the renewable share, cut greenhouse gas emissions, enhance the reliability of power supply and to make electricity generation sustainable in the long run for the country. Different solar PV capacities are tested against the energy cost, renewable share and emission of greenhouse gases in order to attain the trade-off. The cost of energy is minimized by 92.47%, renewable share rises to 85%, and CO2 emissions are decreased by 48% for residential application. In the case of commercial application, however, the cost of energy is lowered by 48.52%, the renewable energy share rises to 71.1%, and CO2 emissions are reduced by 61% through incorporating solar PV into the current power system.
Keywords	Grid-connected microgrid ; Cost of energy ; Renewable share ; Greenhouse gases ; Reliability ; Sustainability ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	690
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Hydrogels

Adnan Haider / Sajjad Haider

2018

Abstract: This new important book is a collection of research and review articles from different parts of the world discussing the dynamic and vibrant field of hydrogels. The articles are linking new findings and critically reviewing the fundamental concepts and ... ...

Abstract	This new important book is a collection of research and review articles from different parts of the world discussing the dynamic and vibrant field of hydrogels. The articles are linking new findings and critically reviewing the fundamental concepts and principles that are making the base for innovation. Each chapter discusses the potential of hydrogels in diverse areas. These areas include tissue engineering, implants, controlled drug release, and oil reserve treatment. The book is offering an up-to-date knowledge of hydrogels to experienced as well as new researchers.
Keywords	Physical Sciences ; Engineering and Technology ; Materials Science ; Biomaterials ; Polymers
Language	English
Publisher	IntechOpen
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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