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Kiran A, Nagaraju C, Babu JC, Venkatesh B, Kumar A, Khan SB, Albuali A, Basheer S. Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers. PLoS One 2024; 19:e0298731. [PMID: 38527047 PMCID: PMC10962831 DOI: 10.1371/journal.pone.0298731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/29/2024] [Indexed: 03/27/2024] Open
Abstract
A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance.
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Affiliation(s)
- Ajmeera Kiran
- Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India
| | - Ch Nagaraju
- Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
| | - J. Chinna Babu
- Department of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
| | - B Venkatesh
- Department of Mechanical Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India
| | - Adarsh Kumar
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Salford, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Abdullah Albuali
- Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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Nagarajan B, Chakravarthy S, Venkatesan VK, Ramakrishna MT, Khan SB, Basheer S, Albalawi E. A Deep Learning Framework with an Intermediate Layer Using the Swarm Intelligence Optimizer for Diagnosing Oral Squamous Cell Carcinoma. Diagnostics (Basel) 2023; 13:3461. [PMID: 37998597 PMCID: PMC10670914 DOI: 10.3390/diagnostics13223461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit from automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal oral lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers and classification layers for classifying the histopathological images into two categories, namely, normal and oral squamous cell carcinoma. The intermediate layer is constructed using the proposed swarm intelligence technique called the Modified Gorilla Troops Optimizer. While there are many optimization algorithms used in the literature for feature selection, weight updating, and optimal parameter identification in deep learning models, this work focuses on using optimization algorithms as an intermediate layer to convert extracted features into features that are better suited for classification. Three datasets comprising 2784 normal and 3632 oral squamous cell carcinoma subjects are considered in this work. Three popular CNN architectures, namely, InceptionV2, MobileNetV3, and EfficientNetB3, are investigated as feature extraction layers. Two fully connected Neural Network layers, batch normalization, and dropout are used as classification layers. With the best accuracy of 0.89 among the examined feature extraction models, MobileNetV3 exhibits good performance. This accuracy is increased to 0.95 when the suggested Modified Gorilla Troops Optimizer is used as an intermediary layer.
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Affiliation(s)
- Bharanidharan Nagarajan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Sannasi Chakravarthy
- Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam 638401, India;
| | - Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India; (B.N.); (V.K.V.)
| | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-Be University), Bangalore 562112, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Eid Albalawi
- Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Tiwari RS, Dandabani L, Das TK, Khan SB, Basheer S, Alqahtani MS. Cloud-Based Quad Deep Ensemble Framework for the Detection of COVID-19 Omicron and Delta Variants. Diagnostics (Basel) 2023; 13:3419. [PMID: 37998555 PMCID: PMC10670372 DOI: 10.3390/diagnostics13223419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 10/31/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
The mortality rates of patients contracting the Omicron and Delta variants of COVID-19 are very high, and COVID-19 is the worst variant of COVID. Hence, our objective is to detect COVID-19 Omicron and Delta variants from lung CT-scan images. We designed a unique ensemble model that combines the CNN architecture of a deep neural network-Capsule Network (CapsNet)-and pre-trained architectures, i.e., VGG-16, DenseNet-121, and Inception-v3, to produce a reliable and robust model for diagnosing Omicron and Delta variant data. Despite the solo model's remarkable accuracy, it can often be difficult to accept its results. The ensemble model, on the other hand, operates according to the scientific tenet of combining the majority votes of various models. The adoption of the transfer learning model in our work is to benefit from previously learned parameters and lower data-hunger architecture. Likewise, CapsNet performs consistently regardless of positional changes, size changes, and changes in the orientation of the input image. The proposed ensemble model produced an accuracy of 99.93%, an AUC of 0.999 and a precision of 99.9%. Finally, the framework is deployed in a local cloud web application so that the diagnosis of these particular variants can be accomplished remotely.
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Affiliation(s)
- Ravi Shekhar Tiwari
- Department of Computer Science Engineering, Mahindra University, Hyderabad 500043, India
| | - Lakshmi Dandabani
- School of Computing Science and Engineering, VIT Bhopal University, Bhopal 466114, India;
| | - Tapan Kumar Das
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Mohammed S. Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha 61421, Saudi Arabia;
- BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester LE1 7RH, UK
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Bebortta S, Tripathy SS, Basheer S, Chowdhary CL. FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records. Diagnostics (Basel) 2023; 13:3166. [PMID: 37891987 PMCID: PMC10605926 DOI: 10.3390/diagnostics13203166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/27/2023] [Accepted: 10/04/2023] [Indexed: 10/29/2023] Open
Abstract
In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal-dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant's private medical information.
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Affiliation(s)
- Sujit Bebortta
- Department of Computer Science, Ravenshaw University, Cuttack 753003, India;
| | | | - Shakila Basheer
- Department of Information Systems, College of computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Chiranji Lal Chowdhary
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore 632014, India
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Basheer S, Singh KU, Sharma V, Bhatia S, Pande N, Kumar A. A robust NIfTI image authentication framework to ensure reliable and safe diagnosis. PeerJ Comput Sci 2023; 9:e1323. [PMID: 37346677 PMCID: PMC10280420 DOI: 10.7717/peerj-cs.1323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 03/10/2023] [Indexed: 06/23/2023]
Abstract
Advancements in digital medical imaging technologies have significantly impacted the healthcare system. It enables the diagnosis of various diseases through the interpretation of medical images. In addition, telemedicine, including teleradiology, has been a crucial impact on remote medical consultation, especially during the COVID-19 pandemic. However, with the increasing reliance on digital medical images comes the risk of digital media attacks that can compromise the authenticity and ownership of these images. Therefore, it is crucial to develop reliable and secure methods to authenticate these images that are in NIfTI image format. The proposed method in this research involves meticulously integrating a watermark into the slice of the NIfTI image. The Slantlet transform allows modification during insertion, while the Hessenberg matrix decomposition is applied to the LL subband, which retains the most energy of the image. The Affine transform scrambles the watermark before embedding it in the slice. The hybrid combination of these functions has outperformed previous methods, with good trade-offs between security, imperceptibility, and robustness. The performance measures used, such as NC, PSNR, SNR, and SSIM, indicate good results, with PSNR ranging from 60 to 61 dB, image quality index, and NC all close to one. Furthermore, the simulation results have been tested against image processing threats, demonstrating the effectiveness of this method in ensuring the authenticity and ownership of NIfTI images. Thus, the proposed method in this research provides a reliable and secure solution for the authentication of NIfTI images, which can have significant implications in the healthcare industry.
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Affiliation(s)
- Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Kamred Udham Singh
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tai-nan, Taiwan, Taiwan
- School of Computing, Graphic Era Hill University, Dehradun, India
| | | | - Surbhi Bhatia
- King Faisal University, Al Hasa, Saudi Arabia
- Department of Data Science, School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom
| | - Nilesh Pande
- School of Technology Pandit Deendayal Energy University Gandhinagar, Gandhinagar, India
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Kalidas AP, Joshua CJ, Md AQ, Basheer S, Mohan S, Sakri S. Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles. Drones 2023; 7:245. [DOI: 10.3390/drones7040245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Unmanned Aerial Vehicles (UAVs), also known as drones, have advanced greatly in recent years. There are many ways in which drones can be used, including transportation, photography, climate monitoring, and disaster relief. The reason for this is their high level of efficiency and safety in all operations. While the design of drones strives for perfection, it is not yet flawless. When it comes to detecting and preventing collisions, drones still face many challenges. In this context, this paper describes a methodology for developing a drone system that operates autonomously without the need for human intervention. This study applies reinforcement learning algorithms to train a drone to avoid obstacles autonomously in discrete and continuous action spaces based solely on image data. The novelty of this study lies in its comprehensive assessment of the advantages, limitations, and future research directions of obstacle detection and avoidance for drones, using different reinforcement learning techniques. This study compares three different reinforcement learning strategies—namely, Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC)—that can assist in avoiding obstacles, both stationary and moving; however, these strategies have been more successful in drones. The experiment has been carried out in a virtual environment made available by AirSim. Using Unreal Engine 4, the various training and testing scenarios were created for understanding and analyzing the behavior of RL algorithms for drones. According to the training results, SAC outperformed the other two algorithms. PPO was the least successful among the algorithms, indicating that on-policy algorithms are ineffective in extensive 3D environments with dynamic actors. DQN and SAC, two off-policy algorithms, produced encouraging outcomes. However, due to its constrained discrete action space, DQN may not be as advantageous as SAC in narrow pathways and twists. Concerning further findings, when it comes to autonomous drones, off-policy algorithms, such as DQN and SAC, perform more effectively than on-policy algorithms, such as PPO. The findings could have practical implications for the development of safer and more efficient drones in the future.
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Affiliation(s)
- Amudhini P. Kalidas
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Christy Jackson Joshua
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Abdul Quadir Md
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Senthilkumar Mohan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Sapiah Sakri
- Department of Information Systems, College of Computer and Information Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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7
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Bhatia S, Kumar A, Reddy T, Varshney N, Basheer S. Matrix Quantization and LPC Vocoder Based Linear Predictive for Low-Resource Speech Recognition system. ACM T ASIAN LOW-RESO 2023. [DOI: 10.1145/3585313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Over the last ten years, there has been significant progress in the use of low-rate speech coders in voice applications for computers, military communications, and civil communications. This advancement has been made possible by the development of new speech coders that can generate high-quality speech at low data rates. The majority of existing coders include spectral representation of speech, speech waveform matching, and ”optimization” of the coder’s performance for human hearing. The goal of this paper is to provide a thorough evaluation of voice coding methods for educational purposes, with a particular emphasis on the algorithms used in low-rate cellular communication standards. The algorithm we developed using a voice-excited LPC vocoder produces clear, low-distortion results. Ordinary LPCs, on the other hand, fall short of vocoders because they can handle signals other than speech, such as music. To improve quality, additional bandwidth is used to reduce the bit rate. To improve the quality, we tried two approaches. The first was to increase the number of bits required to quantize the DCT coefficients. This coefficient would outperform the inverse DCT in closer error rearrangements. The second possibility is to increase the total number of quantized coefficients. As a result, error array rearrangements would be more accurate. The goal is to identify the point at which a method improvement outperforms the previous, better result. Other coding methods become more complex, but this vocoder suffices.
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Affiliation(s)
- Surbhi Bhatia
- Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia
| | - Ankit Kumar
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Thippa Reddy
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India, and Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - Neeraj Varshney
- Department of Computer Engineering & Applications, GLA University, Mathura, India
| | - Shakila Basheer
- Department of Information Systems College of computer and Information Science. Princess Nourah bint Abdulrahman University P.O. BOX 84428, Riyadh 11671, Saudi Arabia
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Verma D, Agrawal S, Iwendi C, Sharma B, Bhatia S, Basheer S. A Novel Framework for Abnormal Risk Classification over Fetal Nuchal Translucency Using Adaptive Stochastic Gradient Descent Algorithm. Diagnostics (Basel) 2022; 12:2643. [PMID: 36359487 PMCID: PMC9689292 DOI: 10.3390/diagnostics12112643] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/14/2022] [Accepted: 09/22/2022] [Indexed: 11/25/2023] Open
Abstract
In most maternity hospitals, an ultrasound scan in the mid-trimester is now a standard element of antenatal care. More fetal abnormalities are being detected in scans as technology advances and ability improves. Fetal anomalies are developmental abnormalities in a fetus that arise during pregnancy, birth defects and congenital abnormalities are related terms. Fetal abnormalities have been commonly observed in industrialized countries over the previous few decades. Three out of every 1000 pregnant mothers suffer a fetal anomaly. This research work proposes an Adaptive Stochastic Gradient Descent Algorithm to evaluate the risk of fetal abnormality. Findings of this work suggest that proposed innovative method can successfully classify the anomalies linked with nuchal translucency thickening. Parameters such an accuracy, recall, precision, and F1-score are analyzed. The accuracy achieved through the suggested technique is 98.642.%.
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Affiliation(s)
- Deepti Verma
- Department of Computer Application, SAGE University, Indore 452020, India
| | - Shweta Agrawal
- Institute of Advance Computing, SAGE University, Indore 452020, India
| | - Celestine Iwendi
- School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK
| | - Bhisham Sharma
- Department of Computer Science & Engineering, School of Engineering and Technology, Chitkara University, Baddi 174103, India
| | - Surbhi Bhatia
- Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 36362, Saudi Arabia
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. BOX 84428, Riyadh 11671, Saudi Arabia
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Kumar R, Saraswat M, Ather D, Mumtaz Bhutta MN, Basheer S, Thakur RN. Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN. Comput Intell Neurosci 2022; 2022:4406101. [PMID: 35789609 PMCID: PMC9250446 DOI: 10.1155/2022/4406101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/23/2022] [Accepted: 05/16/2022] [Indexed: 01/15/2023]
Abstract
Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.
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Affiliation(s)
- Rakesh Kumar
- Department of Computer Engineering & Applications, GLA University Mathura, Mathura-281406, India
| | - Mala Saraswat
- Department of Computer Science and Engineering, ABES Engineering College Ghaziabad, India
| | - Danish Ather
- Department of Computer Science & Engineering, School of Engineering & Technology Sharda University, Grater Noida, India
| | - Muhammad Nasir Mumtaz Bhutta
- Computer Science and Information Technology (CSIT), College of Engineering, Abu Dhabi University, P.O. Box 5991, Abu Dhabi, UAE
| | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
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Alluhaidan AS, Alluhaidan MS, Basheer S. RETRACTED ARTICLE: Internet of Things Based Intelligent Transportation of Food Products During COVID. Wirel Pers Commun 2022; 127:27. [PMID: 34456512 PMCID: PMC8379606 DOI: 10.1007/s11277-021-08777-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/04/2021] [Indexed: 05/11/2023]
Affiliation(s)
- Ala Saleh Alluhaidan
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | | | - Shakila Basheer
- Department of Information Systems, College of Computer and Information Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
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Shahid M, Akram MS, Khan MA, Zubair M, Shah SM, Ismail M, Shabir G, Basheer S, Aslam K, Tariq M. A phytobeneficial strain Planomicrobium sp. MSSA-10 triggered oxidative stress responsive mechanisms and regulated the growth of pea plants under induced saline environment. J Appl Microbiol 2018; 124:1566-1579. [PMID: 29444380 DOI: 10.1111/jam.13732] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/09/2018] [Accepted: 02/06/2018] [Indexed: 01/20/2023]
Abstract
AIMS The study was planned to characterize Planomicrobium sp. MSSA-10 for plant-beneficial traits and to evaluate its inoculation impact on physiology of pea plants under different salinity levels. METHODS AND RESULTS Strain MSSA-10 was isolated from pea rhizosphere and identified by the analysis of 16S rRNA gene sequence. The strain demonstrated phosphate solubilization and auxin production up to 2 mol l-1 NaCl and exhibited 1-aminocyclopropane-1-carboxylic acid deaminase activity up to 1·5 mol l-1 salt. In an inoculation experiment under different salinity regimes, a significant increase in growth was observed associated with decreased levels of reactive oxygen species and enhanced antioxidative enzyme activities. The strain also promoted the translocation of nutrients in plants with subsequent increase in chlorophyll and protein contents as compared to noninoculated plants. It has been observed that rifampicin-resistant derivatives of MSSA-10 were able to survive for 30 days at optimum cell density with pea rhizosphere. CONCLUSION Growth-stimulating effect of MSSA-10 on pea plants may be attributed to its rhizosphere competence, nutrient mobilization and modulation of plant oxidative damage repair mechanisms under saline environment. SIGNIFICANCE AND IMPACT OF THE STUDY Planomicrobium sp. MSSA-10 might be used as potent bioinoculant to relieve pea plants from deleterious effects of salinity.
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Affiliation(s)
- M Shahid
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - M S Akram
- Department of Botany, Government College University, Faisalabad, Pakistan
| | - M A Khan
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - M Zubair
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - S M Shah
- Biotechnology Program, Environmental Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan
| | - M Ismail
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
| | - G Shabir
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - S Basheer
- School of Biological Sciences, University of the Punjab, Lahore, Pakistan
| | - K Aslam
- Institute of Molecular Biology and Biotechnology, Bahauddin Zakariya University, Multan, Pakistan
| | - M Tariq
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, Pakistan
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Fatima N, Aziz F, Ahmad Z, Najeeb MA, Azmeer MI, Karimov KS, Ahmed MM, Basheer S, Shakoor RA, Sulaiman K. Compositional engineering of the pi-conjugated small molecular VOPcPhO : Alq3 complex to boost humidity sensing. RSC Adv 2017. [DOI: 10.1039/c7ra02525d] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
This study exhibits a solution-processed organic semiconductor humidity sensor based on vanadyl 2,9,16,23-tetraphenoxy-29H,31H-phthalocyanine (VOPcPhO), tris-(8-hydroxy-quinoline)aluminum (Alq3), and their composites.
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Affiliation(s)
- Noshin Fatima
- Department of Electrical Engineering
- Capital University of Science and Technology
- Pakistan
| | - Fakhra Aziz
- Low Dimensional Material Research Center
- Department of Physics
- University of Malaya
- Kuala Lumpur 50603
- Malaysia
| | - Zubair Ahmad
- Center for Advanced Materials (CAM)
- Qatar University
- Doha
- Qatar
| | - M. A. Najeeb
- Center for Advanced Materials (CAM)
- Qatar University
- Doha
- Qatar
| | - M. I. Azmeer
- Low Dimensional Material Research Center
- Department of Physics
- University of Malaya
- Kuala Lumpur 50603
- Malaysia
| | - Kh. S. Karimov
- Ghulam Ishaq Khan Institute of Engineering Sciences and Technology
- Pakistan
- Centre for Innovative Development of Science and Technologies of Academy of Sciences
- Tajikistan
| | - M. M. Ahmed
- Department of Electrical Engineering
- Capital University of Science and Technology
- Pakistan
| | - S. Basheer
- Low Dimensional Material Research Center
- Department of Physics
- University of Malaya
- Kuala Lumpur 50603
- Malaysia
| | - R. A. Shakoor
- Center for Advanced Materials (CAM)
- Qatar University
- Doha
- Qatar
| | - K. Sulaiman
- Low Dimensional Material Research Center
- Department of Physics
- University of Malaya
- Kuala Lumpur 50603
- Malaysia
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16
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Halemani KR, Ahmmed N, Basheer S. Anesthetic management of parturients with cerebral palsy and polymyositis coming for cesarean section: Two case reports. J Obstet Anaesth Crit Care 2016. [DOI: 10.4103/2249-4472.191600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Abstract
A cluster of clinical cases of occupational mental illness has not previously been reported. A prospective cross-sectional study of patients referred for examination and advice about rehabilitation was undertaken to ascertain a variety of employer's rates of occupational mental illness. A background rate of referral for occupational mental illness of 3.1/1,000 employees per year was found apart from in one NHS trust where the rate was 25.6/1,000. Most patients were nurses and diagnoses were anxiety and/or depression with a median length of time off work of four months. There was no evidence that patients from this employer were vulnerable to mental illness. The high rate of occupational mental illness was associated with organisational change and a hostile working climate. This study shows that NHS trusts may be associated with unhealthy working practices. A cluster of occupational mental illness should be statutorily reportable to the Health and Safety Executive for further investigation.
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Affiliation(s)
- C J M Poole
- Occupational Health Department, Dudley NHS Primary Care Trust, Health Centre, Dudley.
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18
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Abstract
This research was conducted to identify the most efficient biomass out of five different types of biomass sources for anaerobic treatment of Olive Mill Wastewater (OMW). This study was first focused on examining the selected biomass in anaerobic batch systems with sodium acetate solutions (control study). Then, the different types of biomass were tested with raw OMW (water-diluted) and with pretreated OMW by coagulation-flocculation using Poly Aluminum Chloride (PACl) combined with hydrated lime (Ca(OH)2). Two types of biomass from wastewater treatment systems of a citrus juice producing company "PriGat" and from a citric acid manufacturing factory "Gadot", were found to be the most efficient sources of microorganisms to anaerobically treat both sodium acetate solution and OMW. Both types of biomass were examined under different concentration ranges (1-40 g l(-1)) of OMW in order to detect the maximal COD tolerance for the microorganisms. The results show that 70-85% of COD removal was reached using Gadot biomass after 8-10 days when the initial concentration of OMW was up to 5 g l(-1), while a similar removal efficiency was achieved using OMW of initial COD concentration of 10 g l(-1) in 2-4 days of contact time with the PriGat biomass. The physico-chemical pretreatment of OMW was found to enhance the anaerobic activity for the treatment of OMW with initial concentration of 20 g l(-1) using PriGat biomass. This finding is attributed to reducing the concentrations of polyphenols and other toxicants originally present in OMW upon the applied pretreatment process.
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Affiliation(s)
- I Sabbah
- The Regional Research & Development Center, The Galilee Society, PO Box 437 Shefa-Amr 20200, Israel
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19
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Basheer S, Snape JB, Mogi K, Nakajima M. Transesterification kinetics of triglycerides for a modified lipase inn-hexane. J AM OIL CHEM SOC 1995. [DOI: 10.1007/bf02638905] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Basheer S, Kut �M, Prenosil JE, Bourne JR. Development of an enzyme membrane reactor for treatment of cyanide-containing wastewaters from the food industry. Biotechnol Bioeng 1993; 41:465-73. [DOI: 10.1002/bit.260410410] [Citation(s) in RCA: 19] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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21
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