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Article ; Online: MRBSChain a novel scalable medical records binance smart chain framework enabling a paradigm shift in medical records management.

Monga, Suhasini / Singh, Dilbag

2022 Volume 12, Issue 1, Page(s) 17660

Abstract: Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is ...

Abstract	Medical records management had always been a challenging in healthcare sector. Traditionally, medical records are handled either manually or electronically that are under the stewardship of hospitals/healthcare institutions. A patient centric approach is the new paradigm where patient is an inherent part of the healthcare ecosystem controlling the access and sharing of his/her personal medical care information. Medical care information requires robust security and privacy. Also there are other issues like confidentiality, interoperability, scalability, cost efficiency and timeliness that need to be addressed. To achieve these objectives, this paper proposes a novel-scalable patient centric yet privacy preserving framework for efficient and secure electronic medical records management. In addition, proposed system generates a unified trusted record and authentication role mapping for enforcing secure access control for medical records using complex encryption algorithms. This paper identifies 13 key performance factors for performance comparison of proposed framework with traditional models. Ethereum and Binance Smart Chain acted as a benchmark platform for performance evaluation of MRBSChain on the basis of three metrics (transaction cost, average block time and deployment cost).At last, a comparative analysis of MRBSChain with other state of art blockchain systems on the basis of execution time is presented in the paper.
MeSH term(s)	Female ; Humans ; Male ; Computer Security ; Ecosystem ; Blockchain ; Electronic Health Records ; Forms and Records Control
Language	English
Publishing date	2022-10-21
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-22569-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Parthenium hysterophorus derived nanostructures as an efficient carbocatalyst for the electrochemical sensing of mercury(II) ions.

Sharma, Ritika / Rana, Dharmender Singh / Gupta, Neeraj / Thakur, Sourbh / Thakur, Kamal Kishor / Singh, Dilbag

Chemosphere

2024 Volume 354, Page(s) 141591

Abstract: The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in ... ...

Abstract	The sustainable utilization of resources motivate us to create eco-friendly processes for synthesizing novel carbon nanomaterials from waste biomass by minimizing chemical usage and reducing energy demands. By keeping sustainability as a prime focus in the present work, we have made the effective management of Parthenium weeds by converting them into carbon-based nanomaterial through hydrothermal treatment followed by heating in a tube furnace under the nitrogen atmosphere. The XPS studies confirm the natural presence of nitrogen and oxygen-containing functional groups in the biomass-derived carbon. The nanostructure has adopted a layered two-dimensional structure, clearly indicated through HRTEM images. Further, the nanomaterials are analyzed for their ability towards the electrochemical detection of mercury, with a detection limit of 6.17 μM, while the limit of quantification and sensitivity was found to be 18.7 μM and 0.4723 μM μA
MeSH term(s)	Mercury ; Parthenium hysterophorus ; Biosensing Techniques/methods ; Nanostructures/chemistry ; Carbon/chemistry ; Ions ; Nitrogen/chemistry ; Oxygen
Chemical Substances	Mercury (FXS1BY2PGL) ; Carbon (7440-44-0) ; Ions ; Nitrogen (N762921K75) ; Oxygen (S88TT14065)
Language	English
Publishing date	2024-03-07
Publishing country	England
Document type	Journal Article
ZDB-ID	120089-6
ISSN	1879-1298 ; 0045-6535 ; 0366-7111
ISSN (online)	1879-1298
ISSN	0045-6535 ; 0366-7111
DOI	10.1016/j.chemosphere.2024.141591
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Article ; Online: Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution.

Singh, Dilbag / Alzubi, Ahmad Ali / Kaur, Manjit / Kumar, Vijay / Lee, Heung-No

IEEE journal of biomedical and health informatics

2024 Volume PP

Abstract: Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, ... ...

Abstract	Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.
Language	English
Publishing date	2024-03-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2024.3377631
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Article ; Online: Pachydermoperiostosis with Hearing Loss.

Singh, Dilbag / Rawat, Ritu / Thakur, Vishal

Skinmed

2022 Volume 20, Issue 4, Page(s) 311–313

Abstract: A 21-year-old unmarried man, born of a non-consanguineous marriage, presented to the dermatology department with progressive thickening of the facial skin and eyelids, plus increased folds over his forehead for the last 5 months. He also complained of ... ...

Abstract	A 21-year-old unmarried man, born of a non-consanguineous marriage, presented to the dermatology department with progressive thickening of the facial skin and eyelids, plus increased folds over his forehead for the last 5 months. He also complained of progressive enlargement of his hands and feet, with intermittent joint pains in his wrists, elbows, and ankles, along with occasional abdominal pain. He had a hearing loss and increased sweating. (
MeSH term(s)	Adult ; Arthralgia ; Face ; Hearing Loss ; Humans ; Male ; Osteoarthropathy, Primary Hypertrophic/complications ; Osteoarthropathy, Primary Hypertrophic/diagnosis ; Skin ; Young Adult
Language	English
Publishing date	2022-08-31
Publishing country	United States
Document type	Journal Article
ZDB-ID	2171125-2
ISSN	1751-7125 ; 1540-9740
ISSN (online)	1751-7125
ISSN	1540-9740
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Article: Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.

B, Ashwini / Kaur, Manjit / Singh, Dilbag / Roy, Satyabrata / Amoon, Mohammed

Diagnostics (Basel, Switzerland)

2023 Volume 13, Issue 20

Abstract: Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor ... ...

Abstract	Brain tumors pose a complex and urgent challenge in medical diagnostics, requiring precise and timely classification due to their diverse characteristics and potentially life-threatening consequences. While existing deep learning (DL)-based brain tumor classification (BTC) models have shown significant progress, they encounter limitations like restricted depth, vanishing gradient issues, and difficulties in capturing intricate features. To address these challenges, this paper proposes an efficient skip connections-based residual network (ESRNet). leveraging the residual network (ResNet) with skip connections. ESRNet ensures smooth gradient flow during training, mitigating the vanishing gradient problem. Additionally, the ESRNet architecture includes multiple stages with increasing numbers of residual blocks for improved feature learning and pattern recognition. ESRNet utilizes residual blocks from the ResNet architecture, featuring skip connections that enable identity mapping. Through direct addition of the input tensor to the convolutional layer output within each block, skip connections preserve the gradient flow. This mechanism prevents vanishing gradients, ensuring effective information propagation across network layers during training. Furthermore, ESRNet integrates efficient downsampling techniques and stabilizing batch normalization layers, which collectively contribute to its robust and reliable performance. Extensive experimental results reveal that ESRNet significantly outperforms other approaches in terms of accuracy, sensitivity, specificity, F-score, and Kappa statistics, with median values of 99.62%, 99.68%, 99.89%, 99.47%, and 99.42%, respectively. Moreover, the achieved minimum performance metrics, including accuracy (99.34%), sensitivity (99.47%), specificity (99.79%), F-score (99.04%), and Kappa statistics (99.21%), underscore the exceptional effectiveness of ESRNet for BTC. Therefore, the proposed ESRNet showcases exceptional performance and efficiency in BTC, holding the potential to revolutionize clinical diagnosis and treatment planning.
Language	English
Publishing date	2023-10-17
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics13203234
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Article ; Online: MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

Kaur, Manjit / Singh, Dilbag / Kumar, Vijay / Lee, Heung-No

IEEE journal of biomedical and health informatics

2023 Volume 27, Issue 10, Page(s) 5004–5014

Abstract: One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated ... ...

Abstract	One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.
MeSH term(s)	Female ; Humans ; Uterine Cervical Neoplasms/diagnosis ; Deep Learning ; Benchmarking ; Exercise ; Neck
Language	English
Publishing date	2023-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2022.3223127
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Article ; Online: Hybrid KNN-SVM machine learning approach for solar power forecasting

Nishant Saxena / Rahul Kumar / Yarrapragada K S S Rao / Dilbag Singh Mondloe / Nishikant Kishor Dhapekar / Abhishek Sharma / Anil Singh Yadav

Environmental Challenges, Vol 14, Iss , Pp 100838- (2024)

2024

Abstract: Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several ... ...

Abstract	Predictions about solar power will have a significant impact on large-scale renewable energy plants. Photovoltaic (PV) power generation forecasting is particularly sensitive to measuring the uncertainty in weather conditions. Although several conventional techniques like long short-term memory (LSTM), support vector machine (SVM), etc. are available, but due to some restrictions, their application is limited. To enhance the precision of forecasting solar power from solar farms, a hybrid machine learning model that includes blends of the K-Nearest Neighbor (KNN) machine learning technique with the SVM to increase reliability for power system operators is proposed in this investigation. The conventional LSTM technique is also implemented to compare the performance of the proposed hybrid technique. The suggested hybrid model is improved by the use of structural diversity and data diversity in KNN and SVM, respectively. For the solar power predictions, the suggested method was tested on the Jodhpur real-time series dataset obtained from the data centers of weather stations using Meteonorm. The data set includes metrics such as Hourly Average Temperature (HAT), Hourly Total Sunlight Duration (HTSD), Hourly Total Global Solar Radiation (HTGSR), and Hourly Total Photovoltaic Energy Generation (HTPEG). The collated data has been segmented into training data, validation data, and testing data. Furthermore, the proposed technique performed better when evaluated on the three performance indices, viz., accuracy, sensitivity, and specificity. Compared with the conventional LSTM technique, the hybrid technique improved the prediction with 98 % accuracy.
Keywords	Solar power forecasting ; Hybrid model ; KNN ; Optimization ; Solar energy ; SVM ; Environmental sciences ; GE1-350
Subject code	006
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Multi-modality medical image fusion technique using multi-objective differential evolution based deep neural networks.

Kaur, Manjit / Singh, Dilbag

Journal of ambient intelligence and humanized computing

2020 Volume 12, Issue 2, Page(s) 2483–2493

Abstract: The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more ... ...

Abstract	The advancements in automated diagnostic tools allow researchers to obtain more and more information from medical images. Recently, to obtain more informative medical images, multi-modality images have been used. These images have significantly more information as compared to traditional medical images. However, the construction of multi-modality images is not an easy task. The proposed approach, initially, decomposes the image into sub-bands using a non-subsampled contourlet transform (NSCT) domain. Thereafter, an extreme version of the Inception (Xception) is used for feature extraction of the source images. The multi-objective differential evolution is used to select the optimal features. Thereafter, the coefficient of determination and the energy loss based fusion functions are used to obtain the fused coefficients. Finally, the fused image is computed by applying the inverse NSCT. Extensive experimental results show that the proposed approach outperforms the competitive multi-modality image fusion approaches.
Keywords	covid19
Language	English
Publishing date	2020-08-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2543187-0
ISSN	1868-5145 ; 1868-5137
ISSN (online)	1868-5145
ISSN	1868-5137
DOI	10.1007/s12652-020-02386-0
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Article ; Online: Colonic tuberculosis masquerading as crohn's disease.

Kaur, Harveen / Singh, Dilbag / Kajal, N C

International journal of mycobacteriology

2021 Volume 10, Issue 4, Page(s) 475–477

Abstract: Intestinal tuberculosis (TB) is a diagnostic challenge and can closely mimic Crohn's disease (CD) and colon cancer. These disease entities very closely resemble each other in symptomatology, imaging, appearance, and pathology. We present a case of ... ...

Abstract	Intestinal tuberculosis (TB) is a diagnostic challenge and can closely mimic Crohn's disease (CD) and colon cancer. These disease entities very closely resemble each other in symptomatology, imaging, appearance, and pathology. We present a case of colonic TB where the initial diagnostic workup was suggestive of CD. However, the detection of Mycobacterium tuberculosis in biopsy specimens confirmed the diagnosis.
MeSH term(s)	Colon ; Crohn Disease/diagnosis ; Humans ; Mycobacterium tuberculosis/genetics ; Tuberculosis, Gastrointestinal/diagnosis
Language	English
Publishing date	2021-12-16
Publishing country	Netherlands
Document type	Case Reports
ZDB-ID	2696590-2
ISSN	2212-554X ; 2212-554X
ISSN (online)	2212-554X
ISSN	2212-554X
DOI	10.4103/ijmy.ijmy_175_21
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Article: Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review.

Monga, Anmol / Singh, Dilbag / de Moura, Hector L / Zhang, Xiaoxia / Zibetti, Marcelo V W / Regatte, Ravinder R

Bioengineering (Basel, Switzerland)

2024 Volume 11, Issue 3

Abstract: Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights ...

Abstract	Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.
Language	English
Publishing date	2024-02-28
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering11030236
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