Segmentation of breast lesion using fuzzy thresholding and deep learning.

Breast cancer is a major cause of morbidity and mortality in women. In breast cancer screening, Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has shown promise as a technique, providing enhanced temporal patterns of breast tissues. This study proposes an enhanced segmentation method for identifying breast lesions.

A comprehensive review of titanium dioxide nanoparticles in cementitious composites.

Nanomaterials are incorporated to improve the characteristics of conventional concrete materials. Among them, Titanium dioxide (TiO) nanoparticles exhibit sustainable concrete features such as optimization of materials, improvement of structures, recycling, and innovative waste management in materials and processes. This paper thoroughly elaborates on the effect of TiO nanoparticles on the composites of cement which in turn modifies the physical, mechanical, and ability to resist any chemical action, climate change, and abrasion; Furthermore, this study emphasizes the small fragment size, increased surface area and photocatalytic properties of cementitious composites.

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks.

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts.

DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition.

Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process.

Amine functionalised graphene embedded polyvinyl alcohol (PVA) and PVA-chitosan hydrogel composites.

A novel amine-functionalized graphene oxide (AFG) doped polyvinyl alcohol (PVA)/chitosan (PVA-Ch) composite film was developed using an eco-synthesis approach, eliminating the need for halogenated compounds. The resulting AFG-doped PVA/Chitosan (PVA-Ch/AFG) polymer film exhibited promising properties for controlled delivery and biosensing applications. The investigation included assessing the swelling behaviour, dissolution percent, gel fraction, and mechanical properties of the polymer film.

Voxel representation of brain images inpainting via Regional Pixel Semantic Network and pyramidal attention AE - Quantile differential mechanism model.

Medical image inpainting holds significant importance in enhancing the quality of medical images by restoring missing areas, thereby rendering them suitable for diagnostic purposes. While several techniques have been previously proposed for medical image inpainting, they are not suitable for distorted images containing metallic implants due to their limited consideration of known shaped masking. To overcome this limitation, a novel Vectorized Box Interpolation with Arbitrary Auto-Rand Augment Masking technique has been proposed which involves scaling and vectorizing images to expand their details and generating asymmetrically shaped masking in an automatic random format.

Classification of stages in cervical cancer MRI by customized CNN and transfer learning.

Cervical cancer is the common cancer among women, where early-stage diagnoses of cervical cancer lead to recovery from the deadly cervical cancer. Correct cervical cancer staging is predominant to decide the treatment. Hence, cervical cancer staging is an important problem in designing automatic detection and diagnosing applications of the medical field.

Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier.

As a result of the COVID-19 (coronavirus) disease due to SARS-CoV2 becoming a pandemic, it has spread over the globe. It takes time to evaluate the results of the laboratory tests because of the rising number of cases each day. Therefore, there are restrictions in terms of both therapy and findings.

An Intelligent System to Enhance the Performance of Brain Tumor Diagnosis from MR Images.

In the human body, cancer is caused by aberrant cell proliferation. Brain tumors are created when cells in the human brain proliferate out of control. Brain tumors consist of two types: benign and malignant.

Experimental and Numerical Investigation on the Shear Behavior of Engineered Cementitious Composite Beams with Hybrid Fibers.

The shear behavior of innovative engineered cementitious composites (ECC) members with a hybrid mix of polyvinyl alcohol (PVA) and polypropylene (PP) fibers is examined. The overall objective of the investigation is to understand the shear behavior of ECC beams with different mono and hybrid fiber combinations without compromising the strength and ductility. Four different configurations of beams were prepared and tested, including 2.

Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach.

Background And Objective: This research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity.

An adaptive technique based blood glucose control in type-1 diabetes mellitus patients.

This study proposes Grasshopper Optimization Algorithm (GOA) based type 1 diabetes mellitus system utilizing the nonlinear Bergman minimal model with proportional integral derivative (PID) controller. GOA is the optimization algorithm, which is utilized for selecting the optimized tuning parameters of the PID controller also solves the nonlinear system parameter identification problem. The novelty of the proposed study is to stabilize the glucose level in blood for type 1 diabetic patients by infusion of insulin in reduced time with optimal quantity.

Estimation of protein from the images of health drink powders.

Using new technologies to know the nutrition contents of food is the new emerging area of research. Predicting protein content from the image of food is one such area that will be most useful to the human beings because monitoring the nutrition intake has many health benefits. Patients with rare diseases like maple syrup urine disease need to be in good diet practices in order to survive.

Performance evaluation of pattern recognition networks using electromyography signal and time-domain features for the classification of hand gestures.

The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class.

Comprehensive characterization of natural cellulosic fiber from Coccinia grandis stem.

The anatomical, physico-chemical, mechanical, thermal and surface characteristics of Coccinia grandis fiber (CGF) were studied for a potential substitute to the harmful synthetic fibers. The anatomical analysis of Coccinia grandis stem reveals the presence of high strength xylem fibers. Polarized light microscopy and scanning electron microscopy of CGF shows a hierarchical cell structure composed of a primary and a secondary cell wall, cell lumen and the middle lamellae.

Diagnosis of coronary artery disease using an efficient hash table based closed frequent itemsets mining.

This paper proposes an efficient hash table based closed frequent itemsets (HCFI) mining algorithm to envisage coronary artery disease early. HCFI algorithm generates closed frequent itemsets efficiently by performing intersection operation on transaction id's of itemset without considering the name of item/itemset. The employed hash table reduces search efficiency to O(1) or constant time.

Growth studies on microalgae Euglena sanguinea in various natural eco-friendly composite media to optimize the lipid productivity.

The environmental and culture conditions of a potential microalgae Euglena sanguinea were optimized for maximizing the biomass productivity. It was found that the organic macronutrient from poultry litter and organic micronutrient of seaweed extract are very effective to enhance the lipid production. Complementing, these organic nutrients with commercial NPK fertilizer, boost up the lipid productivity by 30.

Denoising of Ultrasound Cervix Image Using Improved Anisotropic Diffusion Filter.

Objective: The purpose of this study was to evaluate an improved oriented speckle reducing anisotropic diffusion (IADF) filter that suppress the speckle noise from ultrasound B-mode images and shows better result than previous filters such as anisotropic diffusion, wavelet denoising and local statistics.

Methods: The clinical ultrasound images of the cervix were obtained by ATL HDI 5000 ultrasound machine from the Regional Cancer Centre, Medical College campus, Thiruvananthapuram. The standardized ways of organizing and storing the image were in the format of bmp and the dimensions of 256 x 256 with the help of an improved oriented speckle reducing anisotropic diffusion filter.

Efficient Scheduling of Scientific Workflows with Energy Reduction Using Novel Discrete Particle Swarm Optimization and Dynamic Voltage Scaling for Computational Grids.

One of the most significant and the topmost parameters in the real world computing environment is energy. Minimizing energy imposes benefits like reduction in power consumption, decrease in cooling rates of the computing processors, provision of a green environment, and so forth. In fact, computation time and energy are directly proportional to each other and the minimization of computation time may yield a cost effective energy consumption.

Characterization of new natural cellulosic fiber from Cissus quadrangularis stem.

Polymer composite has contributed tremendously for energy efficient technologies in automotive and aero industries. Environmental and health concerns related to the carcinogenic nature of artificial fiber in polymer composite needs a retrofit. Eco friendly natural cellulosic fiber extract from the stem of Cissus quadrangularis plant is extensively characterized to consider as a viable alternative for man-made hazardous fibers.

Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation.

Background: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities.

Objective: To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images.

Methods: Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering.

Characterization of new natural cellulosic fiber from Cissus quadrangularis root.

Fiber reinforced polymer composites are replacing many metallic structures due to its high specific strength and modulus. However commonly used man-made E-glass fibers are hazardous for health and carcinogenic by nature. Comprehensive characterization of Cissus quadrangularis root fiber such as anatomical study, chemical analysis, physical analysis, FTIR, XRD, SEM analysis and thermo gravimetric analysis are done.