Biosynthesis of JC-LaCoO magnetic nanoparticles explored in catalytic and SMMs properties.

We have reported the synthesis of JC-LaCoO magnetic nanoparticles from Jatropha Curcas L. leaf extract in aqueous medium and potential application study in catalytic & Single Molecule Magnets (SMMs). Several techniques were used to investigate the structural, morphological, and elemental composition, particle size, optical properties, catalytic and magnetic properties by XRD, FTIR, SEM, EDAX, XPS, UV-visible and squid magnetic measurement.

Manufacturing of Pure Iron by Cold Rolling and Investigation for Application in Magnetic Flux Shielding.

The presented work investigates a novel method to manufacture 98.8% pure iron strips having high permeability and better saturation flux density for application in magnetic flux shielding. The proposed method uses electro-deposition and cold rolling along with intermediate annealing in a controlled environment to manufacture 0.

Review on diabetic retinopathy with deep learning methods.

The purpose of our review paper is to examine many existing works of literature presenting the different methods utilized for diabetic retinopathy (DR) recognition employing deep learning (DL) and machine learning (ML) techniques, and also to address the difficulties faced in various datasets used by DR. DR is a progressive illness and may become a reason for vision loss. Early identification of DR lesions is, therefore, helpful and prevents damage to the retina.

Brain Tumor Detection Using Machine Learning and Deep Learning: A Review.

According to the International Agency for Research on Cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as Magnetic Resonance Iimaging (MRI) and computed tomography scans using a computer-aided design.

Accelerating Chan-Vese model with cross-modality guided contrast enhancement for liver segmentation.

Accurate and fast liver segmentation remains a challenging and important task for clinicians. Segmentation algorithms are slow and inaccurate due to noise and low quality images in computed tomography (CT) abdominal scans. Chan-Vese is an active contour based powerful and flexible method for image segmentation due to superior noise robustness.