Kumawat  Piyush Kumar  Chaturvedi  Nitin Dutt 《Clean Technologies and Environmental Policy》2022,24(1):273-288

Clean Technologies and Environmental Policy - Water is considered a significant resource in process industries. It is essential for planners to target and optimize the use of water as an external...  相似文献   

    

Meenakshi Aggarwal;Vikas Khullar;Nitin Goyal;Rashi Rastogi;Aman Singh;Vanessa Yelamos Torres;Marwan Ali Albahar; 《International journal of imaging systems and technology》2024,34(2):e22994

Correct identification of tumor in brain images is critical for treatment. In the medical domain, class distributions of recorded data could differ with locations and require high levels of privacy while collaboratively training the deep learning (DL) models for classifications. The main aim of this paper is to propose a privacy-preserving collaborative model for the classification of brain tumor in heterogeneously distributed magnetic resonance imaging (MRI) images. In this paper, initially, an open-source dataset has been acquired and analyzed as per the required competencies. The acquired dataset has four types of MRI images: pituitary tumor, meningioma tumor, glioma tumor, and no tumor. First, the acquired dataset was analyzed using DL and transfer learning algorithms. By applying implementations of basic algorithms, better algorithms were identified for further implementations in a federated learning ecosystem. DenseNet201-based transfer learning was identified as a better neural network and further utilized for collaborative transfer learning implementations. Here, the paper also focused on developing a suitable system for a heterogeneous distributed tumor database. Heterogeneous data were converted from the available data by applying nonidentical data distribution. The study discovered that the federated DL models, involving multiple clients, exhibited superior performance compared to conventional pretrained models. The proposed framework possesses distinctive characteristics that distinguish it from existing classification methods for brain tumor identification, particularly in terms of ensuring data privacy for edge devices with limited resources. Due to these additional features, the framework stands as the optimal alternative solution for early diagnosis of brain tumor.  相似文献   

    

Prabhjot Kaur;Anand Muni Mishra;Nitin Goyal;Sachin Kumar Gupta;Achyut Shankar;Wattana Viriyasitavat; 《Expert Systems》2024,41(7):e13543

Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter-class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid-Convolutional Support Machine (H-CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.  相似文献   

    

Nitin Shivappa  Cindy K. Blair  Anna E. Prizment  David R. Jacobs  James R. Hébert 《Molecular nutrition & food research》2017,61(5)

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Pavan Kumar  M. K. Chatli  Akhilesh K. Verma  Nitin Mehta  O. P. Malav  Devendra Kumar 《Critical reviews in food science and nutrition》2017,57(13):2844-2856

Fermentation of meat is a traditional preservation method used widely for improving quality and shelf life of fermented meat products. Fermentation of meat causes a number of physical, biochemical, and microbial changes, which eventually impart functional properties, sensory characteristics, and nutritional aspects to these products and inhibit the growth of various pathogenic and spoilage microorganisms. These changes include acidification (carbohydrate catabolism), solubilization and gelation of myofibrillar and sarcoplasmic proteins of muscle, degradation of proteins and lipids, reduction of nitrate into nitrite, formation of nitrosomyoglobin, and dehydration. Dry-fermented sausages are increasingly being used as carrier of probiotics. The production of biogenic amines during fermentation can be controlled by selecting proper starter cultures and other preventive measures such as quality of raw materials, hygienic measures, temperature, etc.  相似文献   

    

Nitin Bhatia  Alfred Stein  Ils Reusen  Valentyn A. Tolpekin 《International journal of remote sensing》2018,39(8):2480-2505

The article describes a novel approach to estimate and calibrate column water vapour (CWV), a key parameter for atmospheric correction of remote-sensing data. CWV is spatially and temporally variable, and image-based methods are used for its inference. This inference, however, is affected by methodological and numeric limitations, which likely propagate to reflectance estimates. In this article, a method is proposed to estimate CWV iteratively from target surface reflectances. The method is free from assumptions for at sensor radiance-based CWV estimation methods. We consider two cases: (a) CWV is incorrectly estimated in a processing chain and (b) CWV is not estimated in a processing chain. To solve (a) we use the incorrect estimations as initial values to the proposed method during calibration. In (b), CWV is estimated without initial information. Next, we combined the two scenarios, resulting in a generic method to calibrate and estimate CWV. We utilized the hyperspectral mapper (HyMap) and airborne prism experiment (APEX) instruments for the synthetic and real data experiments, respectively. Noise levels were added to the synthetic data to simulate real imaging conditions. The real data used in this research are cloud-free scenes acquired from the airborne campaigns. For performance assessment, we compared the proposed method with two state-of-the-art methods. Our method performed better as it minimizes the absolute error close to zero, only within 8–10 iterations. It thus suits existing operational chains where the number of iterations is considerable. Finally, the method is simple to implement and can be extended to address other atmospheric trace gases.  相似文献   

    

Nitin Goyal  Vijaya Kumar Bulasara  Sanghamitra Barman 《分离科学与技术》2019,54(2):224-232

This study represents the application of nanozeolite NaX (NZX) synthesized by hydrothermal treatment without addition of any expensive structure directing agent for the highly effectual adsorption of biochanin A from aqueous solution. The characteristics and surface morphology of NZX were determined using FT-IR, SEM, TEM, BET, and XRD. The adsorption performance of NZX was analyzed at different conditions such as temperature, pH, contact time, adsorbent dose, and initial adsorbate concentration. The adsorption mechanism was well verified by pseudo-second-order kinetic and Redlich–Peterson isotherm models. The thermodynamic parameters revealed that the removal process was exothermic and spontaneous.  相似文献   

    

Nitin Muchhal  Shweta Srivastava 《国际射频与微波计算机辅助工程杂志》2019,29(9)

In this article, a wideband bandpass filter (BPF) is designed using the comb slotted substrate integrated waveguide (SIW) cavities. The comb‐shaped slots engraved on the SIW cavity are used to constitute a novel multiple‐mode resonator (MMR) that accomplishes a wide passband of operation. Further, a Jerusalem cross defected ground structure (DGS) is introduced to miniaturize it and enhance filter performance in the pass band and stop band. The filter is fabricated on RT/Duroid 5880 having dielectric constant 2.2 and tested to prove the validity of design. The filter achieves 3 dB fractional bandwidth of 48%, return loss above 14 dB and insertion loss of 1.1 dB in the passband. Also, the proposed filter has steep selectivity and wide upper stopband with 25 dB attenuation from 16.7 to 24 GHz.  相似文献   

    

Nitin Shukla  Mayank Pandey  Shashank Srivastava 《International Journal of Network Management》2019,29(5)

Unlike traditional networking devices, control and management plane are decoupled from data plane in software‐defined networks (SDN). The logically centralized control and management plane facilitate dynamic orchestration of network resources, services, and policies by writing software programs. This provides much needed flexibility and programmability where networking rules and policies can be modified dynamically depending upon the application context. As the operation of network services entirely depends on a program, a small fault may induce several issues which can adversely affect the expected behavior of the network. Formal modeling and verification help in catching inconsistencies and existence of errors prior to the deployment of the programs that control the behavior of a network. In this paper, we provide a comprehensive survey of tools and techniques available in the literature for formal modeling and verification of SDN. These tools and techniques are classified based on their types, the components of SDN where they can be applied, and the design and development phase when they are utilized. In particular, their respective benefits and limitations are discussed in terms of ease of use, interfaces, and the ability to capture and verify intended network properties.  相似文献   

    

Akhilesh K. Verma  Manish Kumar Chatli  Pavan Kumar  Nitin Mehta 《Journal of food science》2019,84(7):1844-1853

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