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  1. Book ; Online ; E-Book: Plant genomics for sustainable agriculture

    Singh, Ram Lakhan / Mondal, Sukanta / Parihar, Akarsh / Singh, Pradeep Kumar

    2022  

    Author's details Ram Lakhan Singh, Sukanta Mondal, Akarsh Parihar, Pradeep Kumar Singh, editors
    Language English
    Size 1 Online-Ressource (xviii, 399 Seiten), Illustrationen
    Publisher Springer
    Publishing place Singapore
    Publishing country Singapore
    Document type Book ; Online ; E-Book
    Note Description based on publisher supplied metadata and other sources
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT021398093
    ISBN 978-981-16-6974-3 ; 9789811669736 ; 981-16-6974-0 ; 9811669732
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article: MTR-SDL: a soft computing based multi-tier rank model for shoulder X-ray classification.

    Mall, Pawan Kumar / Singh, Pradeep Kumar

    Soft computing

    2023  , Page(s) 1–21

    Abstract: Deep neural networks (DNN) effectiveness are contingent upon access to quality-labelled training datasets since label mistakes (label noise) in training datasets may significantly impair the accuracy of models trained on clean test data. The primary ... ...

    Abstract Deep neural networks (DNN) effectiveness are contingent upon access to quality-labelled training datasets since label mistakes (label noise) in training datasets may significantly impair the accuracy of models trained on clean test data. The primary impediments to developing and using DNN models in the healthcare sector include the lack of sufficient label data. Labeling data by a domain expert are a costly and time-consuming task. To overcome this limitation, the proposed Multi-Tier Rank-based Semi-supervised deep learning (MTR-SDL) for Shoulder X-Ray Classification uses the small labelled dataset to generate a labelled dataset from unable dataset to obtain performance equivalent to approaches trained on the enormous dataset. The motivation behind the suggested model MTR-SDL approach is analogous to how physicians deal with unknown or suspicious patients in everyday life. Practitioners handle these questionable circumstances with the support of professional colleagues. Before initiating treatment, some patients consult with a range of skilled doctors. Patients are treated according to the most suitable professional diagnosis (vote count). In this article, we have proposed a new ensemble learning technique called "Rank based Ensemble Selection with machine learning models" (MTR-SDL) approach. In this technique, multiple machine learning models are trained on a labeled dataset, and their accuracy is ranked. A dynamic ensemble voting approach is then used to tag samples for each base model in the ensemble. The combination of these tags is used to generate a final tag for an unlabeled dataset. Our suggested MTR-SDL model has attained the best accuracy and specificity, sensitivity, precision, Matthew's correlation coefficient, false discovery rate, false positive rate, f1 score, negative predictive value, and false negative rate negative 92.776%, 97.376%, 86.932%, 96.192%, 85.644%, 3.808%, 2.624%, 91.072%, 90.85%, and 13.068% for unseen dataset, respectively. This approach has the potential to improve the performance of ensemble models by leveraging the strengths of multiple base models and selecting the most informative samples for each model. This study results in an improved Semi-supervised deep learning model that is more effective and precise.
    Language English
    Publishing date 2023-06-06
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1476598-6
    ISSN 1433-7479 ; 1432-7643
    ISSN (online) 1433-7479
    ISSN 1432-7643
    DOI 10.1007/s00500-023-08562-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Arbuscular mycorrhizal fungi promote growth and enhance the accumulation of bioactive compounds in Tomato (Solanum lycopersicum L.).

    Singh, Meenakshi / Chauhan, Ambika / Srivastava, Devendra Kumar / Singh, Pradeep Kumar

    Biologia futura

    2024  

    Abstract: Arbuscular mycorrhizal fungi (AMF) have been known to enhance plant growth and nutrient uptake. In this study, we investigated the effects of Funneliformis mosseae, Rhizophagus intraradices, and their co-inoculation on the growth and biochemical ... ...

    Abstract Arbuscular mycorrhizal fungi (AMF) have been known to enhance plant growth and nutrient uptake. In this study, we investigated the effects of Funneliformis mosseae, Rhizophagus intraradices, and their co-inoculation on the growth and biochemical composition of tomato (Solanum lycopersicum L.) plants. The findings demonstrated that the inoculation of AMF significantly enhanced shoot and root length, shoot and root dry weight, number of fruits per plant, as well as concentrations of anthocyanin, phenolic compounds, and antioxidants in tomato plants. Both individual and co-inoculation of AMF also significantly increased nitrogen, phosphorus, and potassium concentrations in tomato plants. Our findings suggest that AMF can be used as a potential biofertilizer to enhance the growth and biochemical composition of tomato plants.
    Language English
    Publishing date 2024-04-05
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2987596-1
    ISSN 2676-8607 ; 2676-8615
    ISSN (online) 2676-8607
    ISSN 2676-8615
    DOI 10.1007/s42977-024-00214-6
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Modeling and PID control of quadrotor UAV based on machine learning

    Zhou Lirong / Pljonkin Anton / Singh Pradeep Kumar

    Journal of Intelligent Systems, Vol 31, Iss 1, Pp 1112-

    2022  Volume 1122

    Abstract: The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in ... ...

    Abstract The aim of this article was to discuss the modeling and control method of quadrotor unmanned aerial vehicle (UAV). In the process of modeling, mechanism modeling and experimental testing are combined, especially the motor and propeller are modeled in detail. Through the understanding of the body structure and flight principle of the quadrotor UAV, the Newton–Euler method is used to analyze the dynamics of the quadrotor UAV, and the mathematical model of the UAV is established under the small angle rotation. Process identifier (PID) is used to control it. First, the attitude angle of the model is controlled by PID, and based on this, the speed in each direction is controlled by PID. Then, the PID control of the four rotor aircraft with the center of gravity offset is simulated by MATLAB. The results show that the pitch angle and roll angle can be controlled by 5 degrees together without center of gravity deviation, and the PID can effectively control the control quantity and achieve the desired effect in a short time. Classical BP algorithm, classical GA-BP algorithm, and improved GA-BP algorithm were trained, respectively, with a total of 150 sets of training data, training function uses Levenberg-Marquardt (trainlm), and performance function uses mean squared error (MSE). In the background of the same noise, the improved GA-BP algorithm has the highest detection rate, classical GA-BP algorithm is the second, and classical BP algorithm is the worst. The simulation results show that the PID control law can effectively control the attitude angle and speed of the rotor UAV in the case of center of gravity deviation.
    Keywords quadrotor uav ; pid control ; modeling ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 629
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article: Growth and yield performance of diverse genotypes of tomato (Solanum lycopersicum L.)

    Srinivasulu, Biyyala / Singh, Pradeep Kumar

    Electronic journal of plant breeding. 2021 Mar., v. 12, no. 1

    2021  

    Abstract: A field experiment was carried out in the Vegetable Experimental Field, SKUAST-K, Shalimar, Srinagar during kharif-2018 with twenty seven genotypes of tomato (Solanum lycopersicum L.). They were evaluated to estimate the performance of genotypes on ... ...

    Abstract A field experiment was carried out in the Vegetable Experimental Field, SKUAST-K, Shalimar, Srinagar during kharif-2018 with twenty seven genotypes of tomato (Solanum lycopersicum L.). They were evaluated to estimate the performance of genotypes on various traits like plant height, plant spread, the number of primary branches per plant, days to first flowering, days to 50% flowering, fruit length, fruit diameter, the number of fruits per plant, fruit yield per plot. Analysis of Variance revealed significant differences among the genotypes for all the characters studied. Days to first flowering and days to first fruit set was early in the Jawahar-99, Shalimar Tomato Hybrid-1, Roma, VRT-13, Kashi Hemanth and Kashi Anupam genotypes. Maximum fruit length and diameter was exhibited by Roma, 2016/ TODVAR-3, 2016/TODVAR-11. The highest fruit yield per plot was exhibited by Kashi Sharad and Sel-07.
    Keywords Solanum lycopersicum ; analysis of variance ; field experimentation ; fruit set ; fruit yield ; fruits ; plant height ; tomatoes
    Language English
    Dates of publication 2021-03
    Size p. 183-187.
    Publishing place Indian Society of Plant Breeders
    Document type Article
    ZDB-ID 2534084-0
    ISSN 0975-928X
    ISSN 0975-928X
    DOI 10.37992/2021.1201.027
    Database NAL-Catalogue (AGRICOLA)

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  6. Article: A comprehensive review of critical analysis of biodegradable waste PCM for thermal energy storage systems using machine learning and deep learning to predict dynamic behavior.

    Sharma, Aman / Singh, Pradeep Kumar / Makki, Emad / Giri, Jayant / Sathish, T

    Heliyon

    2024  Volume 10, Issue 3, Page(s) e25800

    Abstract: This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also ... ...

    Abstract This article explores the use of phase change materials (PCMs) derived from waste, in energy storage systems. It emphasizes the potential of these PCMs in addressing concerns related to fossil fuel usage and environmental impact. This article also highlights the aspects of these PCMs including reduced reliance on renewable resources minimized greenhouse gas emissions and waste reduction. The study also discusses approaches such as integrating nanotechnology to enhance thermal conductivity and utilizing machine learning and deep learning techniques for predicting dynamic behavior. The article provides an overall view of research on biodegradable waste-based PCMs and how they can play a promising role in achieving energy-efficient and sustainable thermal storage systems. However, specific conclusions drawn from the presented results are not explicitly outlined, leaving room, for investigation and exploration in this evolving field. Artificial neural network (ANN) predictive models for thermal energy storage devices perform differently. With a 4% adjusted mean absolute error, the Gaussian radial basis function kernel Support Vector Regression (SVR) model captured heat-related charging and discharging issues. The ANN model predicted finned tube heat and heat flux better than the numerical model. SVM models outperformed ANN and ANFIS in some datasets. Material property predictions favored gradient boosting, but Linear Regression and SVR models performed better, emphasizing application- and dataset-specific model selection. These predictive models provide insights into the complex thermal performance of building structures, aiding in the design and operation of energy-efficient systems. Biodegradable waste-based PCMs' sustainability includes carbon footprint, waste reduction, biodegradability, and circular economy alignment. Nanotechnology, machine learning, and deep learning improve thermal conductivity and prediction. Circular economy principles include waste reduction and carbon footprint reduction. Specific results-based conclusions are not stated. Presenting a comprehensive overview of current research highlights biodegradable waste-based PCMs' potential for energy-efficient and sustainable thermal storage systems.
    Language English
    Publishing date 2024-02-04
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2024.e25800
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Phosphatidylinositol (4,5)-bisphosphate drives the formation of EGFR and EphA2 complexes.

    Singh, Pradeep Kumar / Rybak, Jennifer A / Schuck, Ryan J / Barrera, Francisco N / Smith, Adam W

    bioRxiv : the preprint server for biology

    2024  

    Abstract: Receptor tyrosine kinases (RTKs) regulate many cellular functions and are important targets in pharmaceutical development, particularly in cancer treatment. EGFR and EphA2 are two key RTKs that are associated with oncogenic phenotypes. Several studies ... ...

    Abstract Receptor tyrosine kinases (RTKs) regulate many cellular functions and are important targets in pharmaceutical development, particularly in cancer treatment. EGFR and EphA2 are two key RTKs that are associated with oncogenic phenotypes. Several studies have reported functional interplay between these receptors, but the mechanism of interaction is still unresolved. Here we utilize a time-resolved fluorescence spectroscopy called PIE-FCCS to resolve EGFR and EphA2 interactions in live cells. We tested the role of ligands and found that EGF, but not ephrin A1 (EA1), stimulated hetero-multimerization between the receptors. To determine the effect of anionic lipids, we targeted phospholipase C (PLC) activity to alter the abundance of phosphatidylinositol (4,5)-bisphosphate (PIP
    Language English
    Publishing date 2024-05-04
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.05.03.592400
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Defending the Defender: Adversarial Learning Based Defending Strategy for Learning Based Security Methods in Cyber-Physical Systems (CPS).

    Sheikh, Zakir Ahmad / Singh, Yashwant / Singh, Pradeep Kumar / Gonçalves, Paulo J Sequeira

    Sensors (Basel, Switzerland)

    2023  Volume 23, Issue 12

    Abstract: Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other ... ...

    Abstract Cyber-Physical Systems (CPS) are prone to many security exploitations due to a greater attack surface being introduced by their cyber component by the nature of their remote accessibility or non-isolated capability. Security exploitations, on the other hand, rise in complexities, aiming for more powerful attacks and evasion from detections. The real-world applicability of CPS thus poses a question mark due to security infringements. Researchers have been developing new and robust techniques to enhance the security of these systems. Many techniques and security aspects are being considered to build robust security systems; these include attack prevention, attack detection, and attack mitigation as security development techniques with consideration of confidentiality, integrity, and availability as some of the important security aspects. In this paper, we have proposed machine learning-based intelligent attack detection strategies which have evolved as a result of failures in traditional signature-based techniques to detect zero-day attacks and attacks of a complex nature. Many researchers have evaluated the feasibility of learning models in the security domain and pointed out their capability to detect known as well as unknown attacks (zero-day attacks). However, these learning models are also vulnerable to adversarial attacks like poisoning attacks, evasion attacks, and exploration attacks. To make use of a robust-cum-intelligent security mechanism, we have proposed an adversarial learning-based defense strategy for the security of CPS to ensure CPS security and invoke resilience against adversarial attacks. We have evaluated the proposed strategy through the implementation of Random Forest (RF), Artificial Neural Network (ANN), and Long Short-Term Memory (LSTM) on the ToN_IoT Network dataset and an adversarial dataset generated through the Generative Adversarial Network (GAN) model.
    MeSH term(s) Artificial Intelligence ; Computer Security ; Intelligence ; Memory, Long-Term ; Neural Networks, Computer
    Language English
    Publishing date 2023-06-09
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s23125459
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Accent labeling algorithm based on morphological rules and machine learning in English conversion system

    Liu Xiaofeng / Singh Pradeep Kumar / Pavlovich Pljonkin Anton

    Journal of Intelligent Systems, Vol 30, Iss 1, Pp 881-

    2021  Volume 892

    Abstract: The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution ... ...

    Abstract The dependency of a speech recognition system on the accent of a user leads to the variation in its performance, as the people from different backgrounds have different accents. Accent labeling and conversion have been reported as a prospective solution for the challenges faced in language learning and various other voice-based advents. In the English TTS system, the accent labeling of unregistered words is another very important link besides the phonetic conversion. Since the importance of the primary stress is much greater than that of the secondary stress, and the primary stress is easier to call than the secondary stress, the labeling of the primary stress is separated from the secondary stress. In this work, the labeling of primary accents uses a labeling algorithm that combines morphological rules and machine learning; the labeling of secondary accents is done entirely through machine learning algorithms. After 10 rounds of cross-validation, the average tagging accuracy rate of primary stress was 94%, the average tagging accuracy rate of secondary stress was 94%, and the total tagging accuracy rate was 83.6%. This perceptual study separates the labeling of primary and secondary accents providing the promising outcomes.
    Keywords text-to-speech conversion ; unregistered words ; stress annotation ; accent labeling ; machine learning ; Science ; Q ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 410
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article: Necrotizing fasciitis and gas gangrene due to

    Mohanty, Srujana / Ali, S Manwar / Singh, Pradeep Kumar

    IDCases

    2022  Volume 28, Page(s) e01508

    Abstract: Aeromonas ... ...

    Abstract Aeromonas hydrophila
    Language English
    Publishing date 2022-05-20
    Publishing country Netherlands
    Document type Case Reports
    ZDB-ID 2745454-X
    ISSN 2214-2509
    ISSN 2214-2509
    DOI 10.1016/j.idcr.2022.e01508
    Database MEDical Literature Analysis and Retrieval System OnLINE

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