Chemical leucoderma, often clinically mimicking idiopathic vitiligo and other congenital acquired hypopigmentation, has been increasing rapidly in incidence developing countries such as India.This study attempts to detect clinical epidemiological patterns of chemical leucoderma.Detailed history-taking, especially exposure contributory chemicals, examination, relevant investigations, data recording analysis were done.In a total 864 cases 65.6% started de novo patches pre-existing the...

This paper introduces a novel approach GPTFX, an AI-based mental detection with GPT frameworks. leverages embeddings and the fine-tuning of GPT-3. exhibits superior performance in both classifying health disorders generating explanations accuracy around 87% classification Rouge-L 0.75. We utilized machine learning models for disorders. Additionally, GPT-3 was fine-tuned related to predictions made by these models. Notably, proposed algorithm proves well-suited real-time monitoring deploying...

DIC (Differential Interference Contrast Image) images of cervical pre-cancer tissues are taken from epithelium region, on which wavelet transform and multi-fractal analysis applied. Discrete (DWT) through Daubechies basis done for identifying fluctuations over polynomial trends clear characterization differentiation tissues. A systematic investigation denoised is carried out the continuous Morlet wavelet. The scalogram reveals changes in coefficient peak values grade-I to grade-III. Wavelet...

Optical coherence tomography (OCT) enables us to monitor alterations in the thickness of retinal layer as disease progresses human retina. However, subtle morphological changes layers due early progression often may not lead detectable thickness. OCT images encode depth-dependent backscattered intensity distribution arising depth distributions refractive index from tissue microstructures. Here, such depth-resolved variations different were analyzed using multifractal detrended fluctuation...

Sentiment Analysis is a major element in Artificial Intelligence. Its applications include machine translation, text analysis, computational linguistics, etc. In most cases, classification of sentiment done into two or three classes. But some situations, for example rating product from Amazon, there are multiple One challenge such tasks the class imbalance which reduces accuracy by making model biased. To deal with this problem, we use oversampling to reduce dataset before training model....

The objective of the present work is to diagnose pre-cancer by wavelet transform and multi-fractal de-trended fluctuation analysis DIC images normal different grades cancer tissues. Our imaging methods (Discrete continuous transform, MFDFA) confirm ability detect early stage in cervical tissue.

A probabilistic robust diagnostic algorithm is very much essential for successful cancer diagnosis by optical spectroscopy. We report here support vector machine (SVM) classification to better discriminate the colon and cervical tissues from normal based on elastic scattering The efficacy of SVM with different kernel has been tested multifractal parameters like Hurst exponent, singularity spectrum width in order classify tissues.

Deep learning have paved the way for scientists to achieve great technical feats. In an endeavor hone and perfect these techniques, quantum deep is a promising important tool utilize fullest. Using techniques of supervised in framework, we are able propose convolutional neural network showcase its implementation. We keep our focus on training ten qubits system so that it can learn from labeling breast cell data Wisconsin cancer database optimize circuit parameters obtain minimum error....

We report the application of a hidden Markov model (HMM) on multifractal tissue optical properties derived via Born approximation-based inverse light scattering method for effective discrimination precancerous human cervical sites from normal ones. Two global fractal parameters, generalized Hurst exponent and corresponding singularity spectrum width, computed by detrended fluctuation analysis (MFDFA), are used here as potential biomarkers. develop methodology that makes use these parameters...

The fluctuations of spatial variation biological tissue refractive index has been analysed by using S-transform (ST). This study uses for identifying different grades cervical cancer differential interference contrast (DIC) images stromal region tissues. the subtle differences are reflected in frequency distribution containing random structures types

We are presenting two cases of Guillain-Barré syndrome where it is preceded by hepatitis E virus (HEV) and Japanese encephalitis (JEV) infection, respectively. Our first case a forty-three-year-old nondiabetic, nonhypertensive female who was initially diagnosed with acute HEV induced viral subsequently developed onset ascending quadriparesis lower motor neuron type bilateral facial nerve palsies respiratory failure. Second patient 14-year-old young male presented meningoencephalitis...

In this paper, we make use of the empirical mode decomposition (EMD) to discriminate cervical cancer tissues from normal ones based on elastic scattering spectroscopy. The phase space has been reconstructed through decomposing optical signal into a finite set bandlimited signals known as intrinsic functions (IMFs). It shown that area measure analytic IMFs provides good discrimination performance. Simulation results validate efficacy followed by SVM classification.

In our proposed hyper kurtosis based modified duo-histogram equalization (HKMDHE) algorithm, contrast enhancement has been done on the hyper-kurtosis application. The results are very promising of HKMDHE technique with improved PSNR values and lesser AMMBE than other classical techniques like CLAHE.

In this contribution, we report the application of higher order statistical moments using decision tree and ensemble based learning methodology for development diagnostic algorithms optical diagnosis cancer. The classification results were compared to those obtained with an independent feature extractors like linear discriminant analysis (LDA). performance efficacy these statistics as a classifier boosting has specificity sensitivity while being much faster other time-frequency domain methods.

The Renewable Energy resources vary with of the day and season year even some extent from to year. Aim this paper conserve energy, natural resources. For Solar resource, we are mainly interested find out solar cell characteristics by plotting I-V curve monthly average energy output a module. Also observe temperature versus module in month January order verify performance under fog, dust due Kolaghat Thermal Power plant area other external hazards. Then development is made on tracking system...

In this paper, the spectroscopy signals have been analyzed in recurrence plots (RP), and extract quantification analysis (RQA) parameters from RP order to classify tissues into normal different precancerous grades. Three RQA quantified important features data. These fed classifiers for classification. Simulation results validate efficacy of as potential bio-markers diagnosis pre-cancer.

Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor hone these techniques, quantum is budding serve as an important tool. Using techniques of deep supervised in framework, we are able propose a neural network showcase its implementation. We consider application cancer detection demonstrate working our network. Our focus train ten qubits so that it can learn label given data set optimize circuit parameters obtain minimum error. Thus,...

In this paper, we report a hidden Markov model based multiclass classification of cervical cancer tissues. This has been validated directly over time series generated by the medium refractive index fluctuations extracted from differential interference contrast images healthy and different stages The method shows promising results for with higher accuracy.

This paper discusses primarily the hardware based issues on early detection of diabetic retinopathy. Software algorithms for preprocessing, segmentation, and, classification stages are initially analyzed. Later those techniques were customized and implemented using TMS320C6713 DSP Kits Texas instruments with code composer studio retinopathy through fundus images retina. The implementation shows more effective results as compared to other existing approaches.

ARMA-Neural model is an established useful for the Wind Power forecasting purpose. In current work we introduced Discrete Hilbert Transform (DHT)-Neural Model which provides better result than Model. We know that a signal and its' DHT produces same Energy Spectrum. Based on this concept in paper used Speed Thereafter RBF neural network to forecast wind power. Taking data of measured speed from Weather Forecasting Bureau Report as example, validate method described above.