A novel approach for neural networks based diagnosis and grading of stroke in tumor-affected brain MRIs.

Recognition and diagnosis of stroke from magnetic resonance Image (MRIs) are significant for medical procedures in therapeutic standards. The primary goal of this scheme is the discovery of stroke in tumour locale in brain tissues influenced image. The probability of stroke is categorized on brain tumour influenced images into mild, moderate, or serious cases.

Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection.

Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures.

Heart disease diagnosis using optimized features of hybridized ALCSOGA algorithm and LSTM classifier.

Cardiac disease is the predominant cause of global death mainly due to its hidden symptoms and late diagnosis. Hence, early detection is important to improve quality of life. Though traditional researches attempted to predict heart disease, most of them lacked with respect to accuracy.

Optimization of engine parameters using NSGA II for the comprehensive reduction of emissions from VCR engine fuelled with ROME biodiesel.

Diesel engines are most widely used as power plant for many applications, like automotive, agricultural purposes, portable machines and remote location power generation, because of their higher torque, power output, energy content per unit mass and cost of fuel. Because of the higher compression ratios, the diesel engines are able to produce greater cylinder pressures resulting in higher temperatures and thermal efficiency. On other hand, the diesel engines produce CO, NO, Soot and sulphur emissions which are harmful and these pollute the environment leading to acid rain, global warming and variety of human diseases.

A Hybrid CNN-GLCM Classifier For Detection And Grade Classification Of Brain Tumor.

A supervised CNN Deep net classifier is proposed for the detection, classification and diagnosis of meningioma brain tumor using deep learning approach. This proposed method includes preprocessing, classification, and segmentation of the primary occurring brain tumor in adults. The proposed CNN Deep Net classifier extracts the features internally from the enhanced image and classifies them into normal and abnormal tumor images.

Novel Low-Noise CMOS Bioamplifier for the Characterization of Neurodegenerative Diseases.

Conducting cells of the heart and nerve cells of the brain are having the ability to generate and transmit electrical signals. Recording of neural signals became an important research issue for better analysis and better control of neurological functions by using implantable devices. In neural recording systems, the most critical part is the power constraint neural amplifier.

Unsupervised End-to-End Brain Tumor Magnetic Resonance Image Registration Using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network.

Background: Image registration is the process of aligning two or more images in a single coordinate. Nowadays, medical image registration plays a significant role in computer-assisted disease diagnosis, treatment, and surgery. The different modalities available in the medical image make medical image registration an essential step in Computer Assisted Diagnosis (CAD), Computer- Aided Therapy (CAT) and Computer-Assisted Surgery (CAS).

Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images.

Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years.

Detection of pneumonia infection in lungs from chest X-ray images using deep convolutional neural network and content-based image retrieval techniques.

In this research, A Deep Convolutional Neural Network was proposed to detect Pneumonia infection in the lung using Chest X-ray images. The proposed Deep CNN models were trained with a Pneumonia Chest X-ray Dataset containing 12,000 images of infected and not infected chest X-ray images. The dataset was preprocessed and developed from the Chest X-ray8 dataset.

Classification of Heart Disease Using MFO Based Neural Network on MRI Images.

Background: Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths.

Fuzzy Gray Level Difference Histogram Equalization for Medical Image Enhancement.

Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed.

Fusion based Glioma brain tumor detection and segmentation using ANFIS classification.

The detection of tumor regions in Glioma brain image is a challenging task due to its low sensitive boundary pixels. In this paper, Non-Sub sampled Contourlet Transform (NSCT) is used to enhance the brain image and then texture features are extracted from the enhanced brain image. These extracted features are trained and classified using Adaptive Neuro Fuzzy Inference System (ANFIS) approach to classify the brain image into normal and Glioma brain image.

Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy.

Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite challenging as it may not be detected by humans.

Fetal Electrocardiogram Extraction and Analysis Using Adaptive Noise Cancellation and Wavelet Transformation Techniques.

Birth defect-related demise is mainly due to congenital heart defects. In the earlier stage of pregnancy, fetus problem can be identified by finding information about the fetus to avoid stillbirths. The gold standard used to monitor the health status of the fetus is by Cardiotachography(CTG), cannot be used for long durations and continuous monitoring.

A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.

The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution.

A Review on Detection and Treatment Methods of Sleep Apnea.

This paper presents a review on detection and treatment methods of sleep apnea. Sleep apnea is the most common type of breathing-related sleep disorder. It manifests in a variety of behaviours from teeth grinding to night terrors as involuntary night-time events.

Road Accident Prevention with Instant Emergency Warning Message Dissemination in Vehicular Ad-Hoc Network.

A Road Accident Prevention (RAP) scheme based on Vehicular Backbone Network (VBN) structure is proposed in this paper for Vehicular Ad-hoc Network (VANET). The RAP scheme attempts to prevent vehicles from highway road traffic accidents and thereby reduces death and injury rates. Once the possibility of an emergency situation (i.

A Low-Complexity Euclidean Orthogonal LDPC Architecture for Low Power Applications.

Low-density parity-check (LDPC) codes have been implemented in latest digital video broadcasting, broadband wireless access (WiMax), and fourth generation of wireless standards. In this paper, we have proposed a high efficient low-density parity-check code (LDPC) decoder architecture for low power applications. This study also considers the design and analysis of check node and variable node units and Euclidean orthogonal generator in LDPC decoder architecture.

Emergency Situation Prediction Mechanism: A Novel Approach for Intelligent Transportation System Using Vehicular Ad Hoc Networks.

In Indian four-lane express highway, millions of vehicles are travelling every day. Accidents are unfortunate and frequently occurring in these highways causing deaths, increase in death toll, and damage to infrastructure. A mechanism is required to avoid such road accidents at the maximum to reduce the death toll.

A novel image recuperation approach for diagnosing and ranking retinopathy disease level using diabetic fundus image.

Retinal fundus images are widely used in diagnosing and providing treatment for several eye diseases. Prior works using retinal fundus images detected the presence of exudation with the aid of publicly available dataset using extensive segmentation process. Though it was proved to be computationally efficient, it failed to create a diabetic retinopathy feature selection system for transparently diagnosing the disease state.

Diagnosing and ranking retinopathy disease level using diabetic fundus image recuperation approach.

Retinal fundus images are widely used in diagnosing different types of eye diseases. The existing methods such as Feature Based Macular Edema Detection (FMED) and Optimally Adjusted Morphological Operator (OAMO) effectively detected the presence of exudation in fundus images and identified the true positive ratio of exudates detection, respectively. These mechanically detected exudates did not include more detailed feature selection technique to the system for detection of diabetic retinopathy.

Ethylenediaminium di(4-nitrophenolate): a third order NLO material for optical limiting applications.

Single crystals of ethylenediaminium di(4-nitrophenolate) [EDA4NP] were grown by slow evaporation solution growth technique using ethanol as solvent at constant temperature. It crystallizes in monoclinic centrosymmetric space group C2/c with cell dimension a=11.326Ǻ, b=7.

A model of Occupational Safety and Health Management System (OSHMS) for promoting and controlling health and safety in textile industry.

Background: The development of Occupational Safety and Health Management System in textile industry will rejuvenate the workers and energize the economy as a whole. In India, especially in Tamil Nadu, approximately 1371 textile business is running with the help of 38,461 workers under Ginning, Spinning, Weaving, Garment and Dyeing sectors. Textile industry of contributes to the growth of Indian economy but it fails to foster education and health as key components of human development and help new democracies.

Dynamic quality of service model for improving performance of multimedia real-time transmission in industrial networks.

Nowadays, quality of service (QoS) is very popular in various research areas like distributed systems, multimedia real-time applications and networking. The requirements of these systems are to satisfy reliability, uptime, security constraints and throughput as well as application specific requirements. The real-time multimedia applications are commonly distributed over the network and meet various time constraints across networks without creating any intervention over control flows.