Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

As far as the present state is concerned in detecting the behavioral pattern of humans(subject)using morphological image processing,a considerable portion of the study has been conducted utilizing frontal vision data of human faces.The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach.In this example,hybridization includes an artificial neural network(ANN)with a genetic algorithm(GA).We researched the geometrical properties extracted from side-vision human-face data.An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering.The close vicinity ofminimum distance measurements is done for these clusters,mapped for proper classification and decision process of behavioral pattern.To identify the data acquired,support vector machines and artificial neural networks are utilized.A method known as an adaptiveunidirectional associative memory(AUTAM)was used to map one side of a human face to the other side of the same subject.The behavioral pattern has been detected based on two-class problem classification,and the decision process has been done using a genetic algorithm with best-fit measurements.The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET,Multi-PIE,Yale Face database,RTR,CASIA,etc.The complexity measures have also been calculated under worst-case and best-case situations.

Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.

A Novel Soft Clustering Approach for Gene Expression Data

Gene expression data represents a condition matrix where each rowrepresents the gene and the column shows the condition. Micro array used todetect gene expression in lab for thousands of gene at a time. Genes encode proteins which in turn will dictate the cell function. The production of messengerRNA along with processing the same are the two main stages involved in the process of gene expression. The biological networks complexity added with thevolume of data containing imprecision and outliers increases the challenges indealing with them. Clustering methods are hence essential to identify the patternspresent in massive gene data. Many techniques involve hierarchical, partitioning,grid based, density based, model based and soft clustering approaches for dealingwith the gene expression data. Understanding the gene regulation and other usefulinformation from this data can be possible only through effective clustering algorithms. Though many methods are discussed in the literature, we concentrate onproviding a soft clustering approach for analyzing the gene expression data. Thepopulation elements are grouped based on the fuzziness principle and a degree ofmembership is assigned to all the elements. An improved Fuzzy clustering byLocal Approximation of Memberships (FLAME) is proposed in this workwhich overcomes the limitations of the other approaches while dealing with thenon-linear relationships and provide better segregation of biological functions.

Robust Watermarking Scheme for NIfTI Medical Images

Computed Tomography(CT)scan and Magnetic Resonance Imaging(MRI)technologies are widely used in medical field.Within the last few months,due to the increased use of CT scans,millions of patients have had their CT scans done.So,as a result,images showing the Corona Virus for diagnostic purposes were digitally transmitted over the internet.The major problem for the world health care system is a multitude of attacks that affect copyright protection and other ethical issues as images are transmitted over the internet.As a result,it is important to apply a robust and secure watermarking technique to these images.Notably,watermarking schemes have been developed for various image formats,including.jpg,.bmp,and.png,but their impact on NIfTI(Neuroimaging Informatics Technology Initiative)images is not noteworthy.A watermarking scheme based on the Lifting Wavelet Transform(LWT)and QR factorization is presented in this paper.When LWT and QR are combined,the NIfTI image maintains its inherent sensitivity and mitigates the watermarking scheme’s robustness.Multiple watermarks are added to the host image in this approach.Measuring the performance of the graphics card is done by using PSNR,SSIM,Q(a formula which measures image quality),SNR,and Normalized correlation.The watermarking scheme withstands a variety of noise attacks and conversions,including image compression and decompression.

Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the world.There are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory etc.Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being.At present,the highly stressful life with bad lifestyle activities causes heart disease at a very young age.The main aim of this research is to predict the premature heart disease based on machine learning algorithms.This paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage itself.Support Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical observation.The experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

Enhanced photocatalytic degradation of 4-nitrophenol using polyacrylamide assisted Ce-doped YMnO_(3) nanoparticles

This research article mainly reports on the precise structural,optical,and photocatalytic properties of cerium(Ce)-substituted yttrium manganite(YMnO_(3))nanoparticles synthesized by the polyacrylamide gel method.The characteristics of YMnO_(3)were investigated by the substitution of Ce into the Y site at various molar percentages.The Raman and X-ray diffraction(XRD)analyses confirmed the pure phase of hexagonal YMnO_(3),supported by the Rietveld refinement.The microstructural studies indicate inhomogeneous and irregular particle distribution.The X-ray photoelectron spectroscopy(XPS)results show the presence of two ionic states of Mn and Ce along with Y^(3+)state and oxygen vacancies.Extensive optical exploration using photoluminescence(PL)spectroscopy and UV-Vis-NIR analysis indicates that the intensity of absorption peak increases in the visible region,while the bandgap decreases from 1.42 to1.30 eV with the Ce ion doping(5 mol%-15 mol%).Photocatalytic properties of the polycrystalline nanoparticles were investigated by degradation of the pollutant 4-nitrophenol.The process of amplified photocatalysis process was elucidated by the lowered bandgap and rate of charge carrier recombination.It can be conjugated from this study that the synthesized nanoparticles may be employed as highly efficient(92.8%)visible light-triggered photocatalysts in a variety of real-world applications.

Call for Papers Special Issue on Deep Learning and Evolutionary Computation for Satellite Imagery

Satellite images are humungous sources of data that require efficient methods for knowledge discovery.The increased availability of earth data from satellite images has immense opportunities in various fields.However,the volume and heterogeneity of data poses serious computational challenges.The development of efficient techniques has the potential of discovering hidden information from these images.This knowledge can be used in various activities related to planning,monitoring,and managing the earth resources.Deep learning are being widely used for image analysis and processing.Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.

Call for Papers Special Issue on AI-Enabled Internet of Medical Things for Medical Data Analytics

In the modern era of Big Data,there is always an exponential growth in the amount of data generated and stored in various fields like education,energy,environment,healthcare,fraud,detection,and traffic.At the same time,there is a significant paradigm shift in business and society across the world due to huge growth in various fields like artificial intelligence,machine learning,deep learning and data analytics.This poses significant challenges for decision-making and creates a potential transformation in the economy,government,and industries.Artificial Intelligence tools,techniques,technologies and big data improve the predictive power of the systems created,which enables the government,public and private sectors to discover new patterns and trends.