Digital Eprints Services at Vignan's Foundation for Science, Technology & Research
Not a member yet
721 research outputs found
Sort by
Earlier detection of occult peritoneal metastasis by Pro_Segment in gastric cancer employing augmented deep learning techniques in big data with medical IoT (MIoT)
Full text linkOccult peritoneal metastasis often emerges in sick persons having matured gastric cancer (GC) and is inexpertly detected with presently feasible instruments. Due to the existence of peritoneal metastasis that prevents the probability of healing crucial operation, there relies upon a discontented requirement for an initial diagnosis to accurately recognize sick persons having occult peritoneal metastasis. The proffered paradigm of this chapter identifies the initial phases of occult peritoneal metastasis in GC. The initial phase accompanies metabolomics for inspecting biomarkers. If the sick person undergoes the initial signs of occult peritoneal metastasis in GC, early detection is conducted. Yet, the physical prognosis of this cancer cannot diagnose it, and so, automated detection of the images by dissecting the preoperational Computed Tomography (CT) images by conditional random fields accompanying Pro-DAE (Post-processing Denoising Autoencoders) and the labeling in the images is rid by denoising strainers; later, the ensued images and the segmented images experience the Graph Convolutional Networks (GCN), and the outcome feature graph information experience the enhanced categorizer (Greywold and Cuckoo Search Naïve Bayes categorizer) procedure that is employed for initial diagnosis of cancer. Diagnosis of cancer at the initial phase certainly lessens the matured phases of cancer. Hence, this medical information is gathered and treated for diagnosing the sickness
A Deep Learning Approach for Sarcasm Detection in User generated Content
Full text linkUsing sarcasm in social media is a common way to express negative opinions using positive language, making identifying sarcasm an essential part of the sentimental analysis.
Identifying sarcasm is approached as a two-class classification problem (Binary). Both deep learning models and traditional models have been developed using features such as lexical,
semantic, and pragmatic elements. However, sarcasm can be challenging to detect in natural language processing as it involves language usage that is not always straightforward. Despite this, detecting sarcasm can be valuable in many contexts , which includes social media tracking or monitoring, sentimental analysis, and customer support. This research paper proposes a novel approach, BILSTM-GRU architecture, which uses text representations to learn difficult patterns and semantic structures in the text for identifying the sarcastic data. The approach which is going to propose has the ability to improve the accuracy of detecting sarcasm which contributing towards sentiment analysis on social media platfor
Quality by Design Based Optimization and Development of Cyclodextrin Inclusion Complexes of Quercetin for Solubility Enhancement
Full text linkThe purpose of the current work was to enhance the solubility of Quercetin (QUE) by developing inclusion complexes. The current research focused on preparing ternary cyclodextrin inclusion complexes of Quercetin (ICQs) with tween-80 as surfactant employing quality by the design tool. The concentration of hydroxypropyl-β-cyclodextrin, tween-80, and preparation method was taken as critical process parameters and evaluated to get the final product with desired solubility and dissolution rate as the responses. The central composite design was used for the systemic development of ICQs. The % yield and drug content of the prepared ICQs were found to be greater than 90% & 98%, respectively. X-ray diffraction and differential scanning calorimetry results showed the QUE was converted into an amorphous form after the formulation of ICQs. Improved solubility and dissolution rate was observed for the prepared ICQs than pure drug. The design method was optimized, and design validation studies were also performed. All the factors significantly influenced both solubility and dissolution at p < 0.05. Based on the suggested combinations by overlay plot of graphical optimization, a new formulation was developed and evaluated for solubility and dissolution, which were impressively increased from pure QUE
Assessing the effect of FDM processing parameters on mechanical properties of PLA parts using Taguchi method
No full textFused deposition modeling (FDM) is a fast-expanding additive manufacturing technique for fabricating various polymer components in engineering and medical applications. The
mechanical properties of components printed with the FDM method are influenced by several process parameters. In the current work, the influence of nozzle temperature,
infill density, and printing speed on the tensile properties of specimens printed using polylactic acid (PLA) filament was investigated. With an objective to achieve better
tensile properties including elastic modulus, tensile strength, and fracture strain; Taguchi L8 array has been used for framing experimental runs, and eight experiments were conducted. The results demonstrate that the nozzle temperature significantly influences the tensile properties of the FDM printed PLA products followed by infill density. The optimum processing parameters were determined for the FDM printed PLA material at a nozzle temperature of 220C, infill density of 100%, and printing speed of 20 mm/s
Energy and Exergy Analysis of the Impact of Renewable Energy with Combined Solid Oxide Fuel Cell and Micro-Gas Turbine on Poly-Generation Smart-Grids
No full textIn this study, the thermodynamic performance of a combined gas turbine system equipped with a tubular solid oxide fuel cell and hydrogen fuel was investigated. All components of the
system were separately modeled using thermodynamic relations. The simulation results showed that the efficiency of the combined system decreased with an increase in the turbine inlet temperature, whereas the power of the system increased. In addition, increasing the temperature entering the
turbine and increasing the pressure ratio increased the production entropy and, as a result, increased the irreversibility of the system. The results of the research at the design point showed that 65% of the irreversibility of the system was caused by the combustion chamber and fuel cell (35% of the amount
of entropy produced, the contribution of the combustion chamber, and 30% of the contribution of the solid oxide fuel cell) and 19% was due to the contribution of the heat exchanger. In addition, the combined system has an efficiency of 9.81%, while the system without a fuel cell has an efficiency of
33.4%, which shows the extraordinary performance of the combined system
Optimization of dead metal zone to reduce cutting forces in micro milling of Inconel 718 using RSM
No full textThis study focuses on the mechanism of DMZ (dead metal zone) creation, as well as the impact of cutting edge geometries (sharp, chamfered, double chamfered, and blunt edges), cutting speed, and coefficient of friction on DMZ formation while milling Inconel 718 material (FEM). A non-contact type sensor called a
laser doppler vibrometer (LDV) is used to monitor the vibration of rotating surfaces. In current research work, the LDV is used to measure the mill cutter vibration in micro-milling of Inconel 718 in terms of acoustic optic emission signals. A FFT (fast fourier transformer) is used for signals processing in to frequency domain. Design of experiments as per Taguchi, experiments were performed on the alloy at three levels of spindle speeds, depth of cuts, feed rates. Experimental results on the amplitude o vibration of tool along X and Y directions, surface roughness were measured and analysed using response surface
methodology. Analysis obtained from the variance was used to recognize the significant parameters which effect the vibration of tool and roughness of surface. RSM was implemented and optimized process parameters for the minimum vibration amplitude and surface roughnes
Preparation, characterization and study of magnetic induction heating of Co-Cu nanoparticles
No full textThe communication describes prospects of ferrite with composition Co0.88 Cu0.12 Fe2 O4 suitable for magnetic hyperthermia. Samples were processed by sol-gel method
using polyethylene glycol (PEG) as chelating agent keeping ferrite to PEG weight ratios of 1:1, 1:2, and 1:3. Mean particle sizes of annealed powders at 400℃ ranging from
7.1 nm to 5.4 nm were in good agreement with the estimated crystallite sizes from Xray diffraction patterns using Williamson-Hall analysis. Based on the variation of saturation magnetization with annealing temperature, the optimum weight ratio of
ferrite to PEG was found to be 1:2. The heating efficiency of nanoparticles fabricated with ferrite-PEG weight ratio 1:2 was demonstrated using the magnetic induction heating experiment. The values of specific absorption rate 54 W/g and 83.3 W/g for the nanoparticle concentrations, 10 mg/mL and 15 mg/mL reveal the ability of Co-Cu ferrite nanoparticles as a heating agen
Development of secrete images in image transferring system
Full text linkThis paper addresses a model to secrete the information of one image under another without losing quality of image. Different approaches have been utilized for image hiding as needed,
but multiple images maintain secrecy with information under another image is a challenging task. Thus, the framework is proposed to sustain the secrecy of an original image from
another image. The proposed system collects random images through ImageNet and uses them as per the requirements of secrete images. The framework is used the deep neural
networks method to build secrete information of multiple images under a single image. The
enormous transfer of images is used to select standard image modifications using advanced deep learning approaches. It develops the significance of the critical framework that alleviates the choice of finding the hidden image information. Two vital methods such as Peak Signal to Noise Ratio (PSNR) and the Structural Similarity Index (SSIM) are used to find out difference between host and secret image by their corresponding evaluation scores. It produces the
confidentiality of the image with the help of the host image. Therefore, data from several images are protected under a single image. The different image data are experimented with
good performance. For comparative analysis, the accuracy is better in retrieving two secrete images on all experiments, like approximate accuracy is 100%. Still, when we considered
PSNR and SSIM scores on the same two secrete images, accuracy became less than 50
A blue enzyme from marine bacterium for green technological applications
No full textLaccases are the green tools that can find potential applications in various industries. There are many reports available on laccases from plants and fungal sources but very few reports are available on bacterial laccases. Bacterial laccases show broad range of substrate specificity and it is easy to isolate and purify the bacterial extracellular laccases as compared to fungal laccases. Therefore, there are many advantages of bacterial laccases over fungal laccases
Generation of fractals via iterated function system of Kannan contractions in controlled metric space
Full text linkThe fixed point theory is one of the most essential techniques of applicable mathematics for solving many realistic problems to get a unique solution by using the well known Banach contraction principle. It has paved the ways for numerous extensions, generalization and development of the theory of fixed points in very diverse settings. Our intention in the present paper is to study the Kannan contraction maps defined on a controlled metric space. The generalization of the fixed point theorem for Kannan contraction on controlled metric space is investigated in this paper. We construct an iterated function system called Controlled Kannan Iterated Function System (CK-IFS) with Kannan contraction maps in a controlled metric space and use it to develop a new kind of invariant set, which is called a Controlled Kannan Attractor or Controlled Kannan Fractal (CK-Fractal). Subsequently, the collage theorem for controlled Kannan fractal is also proved. The multivalued fractals are also constructed in the controlled metric space using Kannan
and Reich-type contraction maps. The newly developing iterated function system and fractal set in the controlled metric space can provide a novel direction in the fractal theor