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Article ; Online: Assessment of the hydrological and coupled soft computing models, based on different satellite precipitation datasets, to simulate streamflow and sediment load in a mountainous catchment

Muhammad Adnan Khan / Jürgen Stamm

Journal of Water and Climate Change, Vol 14, Iss 2, Pp 610-

2023 Volume 632

Abstract: This study evaluated the performance and hydrologic utility of four different satellite precipitation datasets (SPDs), including GPM (IMERG_F), PERSIANN_CDR, CHIRPS, and CMORPH, to predict daily streamflow and SL using the SWAT hydrological model as well ...

Abstract	This study evaluated the performance and hydrologic utility of four different satellite precipitation datasets (SPDs), including GPM (IMERG_F), PERSIANN_CDR, CHIRPS, and CMORPH, to predict daily streamflow and SL using the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANNs), random forests (SWAT-RFs), and support vector regression (SWAT-SVR), in the mountainous Upper Jhelum River Basin (UJRB), Pakistan. SCMs were developed using the outputs of un-calibrated SWAT models to improve the predictions. Overall, the GPM shows the highest performance for the entire simulation with R2 and PBIAS varying from 0.71 to 0.96 and −13.1 to 0.01%, respectively. For the best GPM-based models, SWAT-RF showed a superior ability to simulate the entire streamflow with R2 of 0.96, compared with the SWAT-ANN (R2 = 0.90), SWAT-SVR (R2 = 0.87), and SWAT-CUP (R2 = 0.71). Similarly, SWAT-ANN presented the best performance capability to simulate the SL with an R2 of 0.71, compared with the SWAT-RF (R2 = 0.66), SWAT-SVR (R2 = 0.52), and SWAT-CUP (R2 = 0.42). Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating hydrological parameters, particularly in complex terrain where gauge network density is low or uneven. HIGHLIGHTS Soft computing models development using the outputs of un-calibrated SWAT models to improve the prediction of daily streamflow and sediment load in Rivers.; Effectiveness of the hydrological coupled soft computing models based on satellite precipitation datasets for simulating hydrological parameters.; Auto-optimization of different sensitive parameters of the soft computing models to improve predictions.;
Keywords	artificial neural networks ; random forest ; satellite precipitation products ; support vector regression ; swat ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350
Subject code	550
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	IWA Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Comparison of prilocaine/lidocaine cream with piroxicam gel for reducing pain during cannulation of arteriovenous fistula for adults undergoing haemodialysis: A randomized crossover clinical trial.

Shaheen, Samia / Arshad, Abdul Rehman / Khattak, Muhammad Adnan Khan

The journal of vascular access

2022 , Page(s) 11297298221142375

Abstract: Background: Pain is a traumatic experience for most patients on hemodialysis. In this trial, we compared prilocaine/lidocaine cream with piroxicam gel for pain reduction during arteriovenous fistula needling.: Methods: This randomized double-blind ... ...

Abstract	Background: Pain is a traumatic experience for most patients on hemodialysis. In this trial, we compared prilocaine/lidocaine cream with piroxicam gel for pain reduction during arteriovenous fistula needling. Methods: This randomized double-blind crossover clinical trial was done at dialysis unit of a tertiary care hospital from June to August 2022. Adult patients, aged 18-75 years, on maintenance hemodialysis through an arteriovenous fistula were selected randomly. Pain severity during needling of fistula was assessed during initial two hemodialysis sessions without application of any drug. Patients were then randomized into two groups receiving 5% prilocaine/lidocaine cream or 0.5% piroxicam gel 1 h before the next two hemodialysis sessions. After a 7-day washout period, patients crossed over to other groups for another two hemodialysis sessions. Pain was assessed on all these occasions. Primary outcome was reduction in pain with each intervention. Results: There were 32 patients aged 46.44 ± 11.58 years. Pain intensity was 6.69 ± 0.58, 3.13 ± 1.28, and 4.55 ± 1.95 without any medication, prilocaine/lidocaine cream and piroxicam gel respectively. There was greater pain reduction with prilocaine/lidocaine cream than piroxicam gel (3.56 ± 1.35 vs 2.14 ± 1.78; Conclusion: Prilocaine/lidocaine cream provides better pain relief than piroxicam gel during arteriovenous fistula needling.
Language	English
Publishing date	2022-12-27
Publishing country	United States
Document type	Journal Article
ZDB-ID	2252820-9
ISSN	1724-6032 ; 1129-7298
ISSN (online)	1724-6032
ISSN	1129-7298
DOI	10.1177/11297298221142375
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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: Smart occupancy detection for road traffic parking using deep extreme learning machine

Shahan Yamin Siddiqui / Muhammad Adnan Khan / Sagheer Abbas / Farrukh Khan

Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 3, Pp 727-

2022 Volume 733

Abstract: Predicting the location of parking is a long-lasting problem that has ultimate importance in our daily life. In this paper, artificial neural networks are used to predict the parking location that will be helpful for drivers to settle on a reasonable ... ...

Abstract	Predicting the location of parking is a long-lasting problem that has ultimate importance in our daily life. In this paper, artificial neural networks are used to predict the parking location that will be helpful for drivers to settle on a reasonable area for stopping. This approach eventually adds to the familiarity and wellbeing of traffic on the roads which results in a decrease in turbulence. By using the approach of Deep Extreme Learning Machine (DELM), reliability can be achieved with a marginal error rate thus reducing the skeptical inclination. In this article, the Proposed Car Parking Space Prediction (CPSP) to elaborate on the dilemma of parking space for vehicles, we have used deep learning neural networks in contrast with feedforward and backward propagation. When the results were taken into consideration, it was unveiled that extreme deep machine learning neural network bears the highest accuracy rate with 60% of training (21431 samples), 40% of test and validation (14287 examples). It has been observed that the proposed DELM has the highest precision rate of 91.25%. Simulation results validate the prediction effectiveness of the proposed DELM strategy.
Keywords	Deep extreme machine learning ; ANN ; Feedforward propagation ; Backward propagation ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Frequency of Amblyopia in strabismus patients presenting to tertiary care hospital.

Zahir, Khalil Khan / Israr, Muhammad / Khattak, Muhammad Adnan Khan / Mudassar, Saman / Shaheen, Samia / Ullah, Irfan

Romanian journal of ophthalmology

2023 Volume 67, Issue 1, Page(s) 46–49

Abstract: Objective: ...

Abstract	Objective:
MeSH term(s)	Humans ; Amblyopia/epidemiology ; Amblyopia/diagnosis ; Cross-Sectional Studies ; Tertiary Care Centers ; Strabismus/complications ; Strabismus/epidemiology ; Strabismus/diagnosis ; Ophthalmology
Language	English
Publishing date	2023-04-05
Publishing country	Romania
Document type	Journal Article
ZDB-ID	2842202-8
ISSN	2501-2533 ; 2457-4325
ISSN (online)	2501-2533
ISSN	2457-4325
DOI	10.22336/rjo.2023.8
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Article ; Online: Relationship between Work-Life Balance and Job Performance Moderated by Knowledge Risks

Michele Samuele Borgia / Francesca Di Virgilio / Maura La Torre / Muhammad Adnan Khan

Sustainability, Vol 14, Iss 5416, p

Are Bank Employees Ready?

2022 Volume 5416

Abstract: Despite the focus on knowledge risks in the literature, a limited number of studies have empirically examined technological knowledge risks in terms of digitalization, old technologies, and cybercrime as moderating variables in the relationship between ... ...

Abstract	Despite the focus on knowledge risks in the literature, a limited number of studies have empirically examined technological knowledge risks in terms of digitalization, old technologies, and cybercrime as moderating variables in the relationship between work-life balance and job performance. To address this gap, this paper investigated the moderation effects of technological knowledge risks on the relationship between work-life balance and job performance during the pandemic period in employees of cooperative credit banks. A quantitative approach that involved gathering surveys was adopted. Applying PLS-SEM, the empirical findings revealed that technological knowledge risks have a significant impact on the relationship between work-life balance and job performance. Additionally, this research encourages managers to create and maintain a healthy work environment that promotes valuable employees’ job performance while also evaluating the use of new technological advances and their related risks.
Keywords	knowledge risks ; work-life balance ; job performance ; digitalization ; old technologies ; cybercrime ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
Subject code	650
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A secure and size efficient algorithm to enhance data hiding capacity and security of cover text by using unicode

Allah Ditta / Muhammad Azeem / Shahid Naseem / Khurram Gulzar Rana / Muhammad Adnan Khan / Zafar Iqbal

Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 5, Pp 2180-

2022 Volume 2191

Abstract: With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the ... ...

Abstract	With the advancement of technology, the maximum data hiding capacity and security of cover objects have become a very challenging task for researchers, particularly in text carrier. Text carrier depicts low hiding capacity but more secure for the detection of confidential information. It demands novelty in data hiding algorithms. In this regard, a novel algorithm is proposed by using steganography and cryptography together for the enhancement of capacity and security of confidential data. The recommended algorithm uses a linguistic steganography method to conceal data into the Arabic text carrier. In the described algorithm, the identification of secret information from text files is hard due to less redundant bits in the text as compared to the image, audio, and video steganographic mediums. The current solution uses Unicode characters such as Zero-Width-Character (ZWC) and Zero-Width-Joiner (ZWJ) to hide the secret information. Before hiding confidential information, secret data is encrypted by using bit inversion due to which algorithm achieved high security. It is observed from the simulation results that the proposed algorithm successfully achieved high cover medium capacity, security, and robustness.
Keywords	Text Steganography ; Data communication ; Unicode ; Arabic text ; Cryptography ; Data security ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	005 ; 006
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: Smart cities

Muhammad Saleem / Sagheer Abbas / Taher M. Ghazal / Muhammad Adnan Khan / Nizar Sahawneh / Munir Ahmad

Egyptian Informatics Journal, Vol 23, Iss 3, Pp 417-

Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques

2022 Volume 426

Abstract: Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are ... ...

Abstract	Smart cities have been developed over the past decade, and reducing traffic congestion has been the top concern in smart city development. Short delays in communication between vehicles and Roadside Units (RSUs), smooth traffic flow, and road safety are the key challenges of Intelligent Transportation Systems (ITSs). The rapid upsurge in the number of road vehicles has increased traffic congestion and the number of road accidents. To fix this issue, Vehicular Networks (VNs) have developed many new ideas, including vehicular communications, navigation, and traffic control. Machine Learning (ML) is an efficient approach to finding hidden insights into ITS without being programmed explicitly by learning from data. This research proposed a fusion-based intelligent traffic congestion control system for VNs (FITCCS-VN) using ML techniques that collect traffic data and route traffic on available routes to alleviate traffic congestion in smart cities. The proposed system provides innovative services to the drivers that enable a view of traffic flow and the volume of vehicles available on the road remotely, intending to avoid traffic jams. The proposed model improves traffic flow and decreases congestion. The proposed system provides an accuracy of 95% and a miss rate of 5%, which is better than previous approaches.
Keywords	Vehicular networks ; Smart city ; Machine learning ; Fusion ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	380
Language	English
Publishing date	2022-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Bias correction and projection of temperature over the altitudes of the Upper Indus Basin under CMIP6 climate scenarios from 1985 to 2100

Kashif Jamal / Xin Li / Yingying Chen / Muhammad Rizwan / Muhammad Adnan Khan / Zain Syed / Prince Mahmood

Journal of Water and Climate Change, Vol 14, Iss 7, Pp 2490-

2023 Volume 2514

Abstract: The identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias- ... ...

Abstract	The identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies. HIGHLIGHTS Bias correction of temperature maximum and minimum (Tmax and Tmin) of the multimodel ensemble mean of seven CMIP6 GCMs is carried out.; A reduction in the diurnal temperature range (DTR) is anticipated in the future due to high warming in Tmin as compared to Tmax.; Elevation-dependent warming (EDW) is only pronounced in Tmin.; The duration of snow and glacier melt can expand by 1–2 months due to rise in temperature.;
Keywords	climate change ; cmip6 ; elevation-dependent warming ; temperature ; upper indus basin ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350
Subject code	550
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	IWA Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: AI-Driven livestock identification and insurance management system

Munir Ahmad / Sagheer Abbas / Areej Fatima / Taher M. Ghazal / Meshal Alharbi / Muhammad Adnan Khan / Nouh Sabri Elmitwally

Egyptian Informatics Journal, Vol 24, Iss 3, Pp 100390- (2023)

2023

Abstract: Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been ... ...

Abstract	Cattle identification is pivotal for many reasons. Animal health management, traceability, bread classification, and verification of insurance claims are largely depended on the accurate identification of the animals. Conventionally, animals have been identified by various means such as ear tags, tattoos, rumen implants, and hot brands. Being non-scientific approaches, these controls can be easily circumvented. The emerging technologies of biometric identification are extensively applied for Human recognition via thumb impression, face features, or eye retina patterns. The application of biometric recognition technology has now moved towards animals. Cattle identification with the help of muzzle patterns has shown tremendous results. For precise identification, nature has awarded a unique Muzzle pattern that can be utilized as a primary biometric feature. Muzzle pattern image scanning for biometric identification has now been extensively applied for identification. Animal recognition via Muzzle pattern image for different applications has been proliferating gradually. One of those applications includes the identification of fake insurance claims under livestock insurance. Fraudulent animal owners tend to lodge fake claims against livestock insurance with proxy animals. In this paper, we proposed the solution to avoid and/or discard fraudulent claims of livestock insurance by intelligently identifying the proxy animals. Data collection of animal muzzle patterns remained challenging. Key aspects of the proposed system include: (1) the Animal face will be detected through visual using YOLO v7 object detector. (2) After face detection, the same procedures will apply to detect muzzle point (3) the muzzle pattern is extracted and then stored in the database. The System has a mean average precision of 100% for the face and 99.43% for the nose/muzzle point of the animal. Once the animal is registered in the database, the identification process is initiated by extracting unique nose pattern features with ORB and/or SIFT. ...
Keywords	Machine Learning ; Transfer Learning ; Deep Learning ; Artificial Intelligence ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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