Browse Items (16481 total)

• 
  Patent

  Method for face-recognition on the basis of sketch using deep convolution neural network /

  "Patent Number: 201941045946, Applicant: Dr. Debabrata Samanta.
  The present invention relates to method for face- recognition on the basis of sketch using deep convolution neural network. The objective of the present invention is to overcome the inadequacies of the prior art in techniques for face- recognition on the basis of sketches."
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  Patent

  Method for face-recognition on the basis of sketch using deep convolution neural network /

  Patent Number: 201941045946, Applicant: Dr. Debabrata Samanta.
  The present invention relates to method for face- recognition on the basis of sketch using deep convolution neural network. The objective of the present invention is to overcome the inadequacies of the prior art in techniques for face- recognition on the basis of sketches.
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  Patent

  Method for synthesizing onion-like carbon nanostructures for high performance supercapacitor applications /

  Patent Number: 202141000172, Applicant: A V Ramya.The present invention provides a facile, cost-effective, and scalable method for the preparation of onion-like carbon nanostructures from paraffin oil. The method includes a wick-and-paraffin oil flame pyrolysis process in a limited supply of oxygen, where the soot generated during combustion is collected and processed to obtain onion-like carbon nanostructures. The synthesized nanostructures exhibit nearly spherical morphology with particle size ranging from 30 to 50 nm and a BET specific surface area of 104 m2/g. v.
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  Patent

  Method of computer added progressive die design with energy conservation /

  "Patent Number: 201941037095, Applicant: Debabrata Samanta.
  The present invention is related to a method of computer added progressive die design with energy conservation. The computer implemented method is presented including the simulation of requirements of design with bending the blank sheet metal strip used as a simulation, boundary conditions and applying the constraint nodes motion on the object. An adaptive algorithm processed by the processor of the computer system which used for the energy-saving measurement and adjustment of the progressive die design."
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  Patent

  Method of computer added progressive die design with energy conversation /

  Patent Number: 201941037095, Applicant: Dr. Debabrata Samanta.
  The present invention is related to a method of computer added progressive die design with energy conservation. The computer implemented method is presented including the simulation of requirements of design with bending the blank sheet metal strip used as a simulation, boundary conditions and applying the constraint nodes motion on the object.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
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  Patent

  Method of enhancing quality of services in cloud computing environment using load balancer /

  Patent Number: 202211006218, Applicant: Dr. Pratibha Giri.
• Article

  Method of preparing a document for survey instrument validation by experts

  Validation of a survey instrument is an important activity in the research process. Face validity and content validity, though being qualitative methods, are essential steps in validating how far the survey instrument can measure what it is intended for. These techniques are used in both scale development processes and a questionnaire that may contain multiple scales. In the face and content validation, a survey instrument is usually validated by experts from academics and practitioners from field or industry. Researchers face challenges in conducting a proper validation because of the lack of an appropriate method for communicating the requirement and receiving the feedback. In this Paper, the authors develop a template that could be used for the validation of survey instrument. In instrument development process, after the item pool is generated, the template is completed and sent to the reviewer. The reviewer will be able to give the necessary feedback through the template that will be helpful to the researcher in improving the instrument. 2021 The Author(s)
• Article

  Methodical investigation of filtering algorithms for human brain MRI

  Retrieving useful information from the given data through a systematic and organized way can help to learn more about the data in a much better and clear way. Information is hidden in medical images. The medical images like Magnetic Resonance Images (MRI), Computed Tomography (CT), ultrasound, X-ray are suggested by the physicians depending upon the available symptoms of the disease. These medical images contain valuable information about a particular disease in hidden format. Identification of that potentially useful information is crucial in further treatments of a particular disease. In image mining the images are processed and extraction or mining of knowledge is done, to get original, valid, potentially useful, and understandable patterns from the available images. The obtained patterns are a good source for further research work. This research work uses brain Magnetic Resonance Images (MRI) of human beings. Different image filtering algorithms were used to retrieve noise free images. International Science Press.
• Book

  Methodologies and Applications of Computational Statistics for Machine Intelligence

  With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians. 2021, IGI Global. All rights reserved.
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  Patent

  Methods and model for worklife balance of women entrpreneurs /

  Patent Number: 202011039249, Applicant: Dr. Purvi Pareek.
  The increase in the rate of capital formation is an integral part of the economic development of a country. An women entrepreneur stimulates the economic forces in capital formation through his undertakings. When there is industrial development by means of establishing new industries at different locations, employment is generated, regional disparity is reduced and the better standard of living is achieved.
• Article

  METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES

  This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering.
• Article

  METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FOR PREDICTING FRUIT DISEASES

  This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNNGB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. 2025 Published by Faculty of Engineering.
• Article

  MHD flow and nonlinear thermal radiative heat transfer of dusty prandtl fluid over a stretching sheet

  Boundary layer flows and melting heat transfer of a Prandtl fluid over a stretching surface in the presence of fluid particle suspensions has been investigated. The converted set of boundary layer equations are solved numerically by RKF-45 method. Obtained numerical results for flow and heat transfer characteristics are deliberated for various physical parameters. Furthermore, the skin friction coefficient and Nusselt number are also presented in Tabs. 2 and 3. It is found that the heat transfer rates are advanced in occurrence of nonlinear radiation compered to linear radiation. Also, it is noticed that velocity and temperature profile increases by increasing Prandtl parameter. 2020 Tech Science Press.
• Article

  MHD flow of SWCNT and MWCNT nanoliquids past a rotating stretchable disk with thermal and exponential space dependent heat source

  The main purpose of this investigation is to analyze the impacts of a novel exponential space dependent heat source on MHD slip flow of carbon nanoliquids past a stretchable rotating disk. The flow is created due to rotation and stretching of the disk. Aspects of the convective condition and cross-diffusion (Soret and Dufour effects) are also accounted. A comparative study of nanofluids made up of SWCNTs (single-walled carbon nanotube) and MWCNTs (multi-walled carbon nanotube) is presented. The governing partial differential equations system is reduced to nonlinear ordinary boundary value problem. The RungeKuttaFehlberg is utilized for numerical simulations. Embedded dimensionless parameters on the flow fields are examined via graphical illustrations. The rate of heat mass transfer can be controlled by cross-diffusion, exponential space-based heat source and thermal-based heat source effects. It is also proved that q( ) (? ) x q x SWCNT nanoliquid MWCNT nanoliquid -. A novel idea of the exponential space dependent heat source is implemented in the investigation of the slip flow over a rotating deformable disk under the effects of cross-diffusion, temperature based heat source and magnetic field for the first time. A comparison between two different fluids namely SWCNT-H2O nanoliquid and MWCNT-H2O nanoliquid are studied. 2019 IOP Publishing Ltd Printed in the UK.
• Article

  MHD Maxwell nanofluid flow over a porous conical surface: A fractional approach

  The current novel study focuses on the two-dimensional magnetohydrodynamic flow of fractional Maxwell nanofluid through porous conical geometry under convective boundary conditions. The nanofluids considered for the study are suspensions of single and multi-walled carbon nanotubes with blood as the base fluid. Fractional-ordered governing equations are transfigured into non-dimensional forms using appropriate transformations. The finite difference approximations are obtained by discretizing the momentum and energy profiles. The results of both profile are plotted against various physical flow-pertaining parameters. It is evident, that multi-walled carbon nanotubes consistently show higher velocity profiles and lower temperature phases than single-walled carbon nanotubes nanofluid across all embedded parameters. Further, the study revealed that the absence of magnetic parameter improves by 11.36% of velocity distribution and the presence of heat source parameter improves by 18.37% of temperature distribution. This framing highlights the convergence criterion of the findings with previous work, emphasizing both reliability and accuracy within the range of 10?4 to 10?6. Graphical representation concludes that the model involving the fractional technique is superior to the integer one. Thus, achievement demonstrates practical application potential in optimizing the efficiency of fluid heating and cooling processes, underscoring its importance in thermal management. 2025