Enhanced Deep Learning Models for Efficient Stroke Detection Using MRI Brain Imagery

Deep learning models are widely used for solving problems in different applications. Especially Convolutional Neural Network (CNN) based models are found suitable for medical image analysis. As brain stroke is increasing in alarming rate, it is essential to have better approaches to detect it in time. Brain MRI is one of the medical imaging technologies widely used for brain imaging.we proposed certain advancements to well-known deep learning models like VGG16, ResNet50 and DenseNet121 for enhancing brain stroke detection performance. These models are optimized based on the brain stroke detection problem in hand as they are not specialized for a specific problem. We proposed an algorithm, named Deep Efficient Stroke Detection (ESD), that exploids enhanced deep learning models in pipeline. The experimental results revealed that there is performance improvement with optimized models. Highest accuracy is achieved by ResNet50 with 95.67%

ADL-BSDF: A Deep Learning Framework for Brain Stroke Detection from MRI Scans towards an Automated Clinical Decision Support System

Deep learning has emerged to be efficient Artificial Intelligence (AI) phenomena to solve problems in healthcare industry. Particularly Convolutional Neural Network (CNN) models have attracted researchers due to their efficiency in medical image analysis. According to World Health Organization (WHO), rapidly developing cerebral malfunction, brain stroke, is the second leading cause of death across the globe. Brain MRI scans, when analysed quantitatively, play vital role in diagnosis and treatment of stroke. There are many existing methods built on deep learning for stroke diagnosis. However, an automatic, reliable and faster method that not only helps in stroke diagnosis but also demarcate affected regions as part of Clinical Decision Support System (CDSS) is much desired. Towards this objective, we proposed an Automated Deep Learning based Brain Stroke Detection Framework (ADL-BSDF). It does not rely on expertise of healthcare professional in diagnosis and know the extent of damage enabling physician to make quick decisions. The framework is realized by two algorithms proposed. The first algorithm known as CNN-based Deep Learning for Brain Stroke Detection (CNNDL-BSD) focuses on accurate detection of stroke. The second algorithm, Deep Auto encoder for Stroke Severity Detection (DA-SSD), focuses on revealing extent of damage or severity of the stroke. The framework is evaluated against state of the art deep learning models such as EfficientNet, ResNet50 and VGG16

A Survey on Emerging Trends of Cyber Threats to Academic Research

Hackers target universities for the sensitive and proprietary information stored in their systems. Cutting edge research conducted by university researchers makes them a prime target to cyber attackers. Universities, by nature, support open networks and access concepts to their stakeholders. Recent attacks attempted to exploit this open strategy to compromise or steal institutions’ data, research innovations, intellectual property and sensitive personally identifiable information (PII). This research study proposes a methodology for threat modeling and presents an example attack tree depicting a phishing attack to compromise the university network and steal sensitive information in research. A black-box threat model is proposed to categorize and rank cyber-threats to academic research. Furthermore, we provide a set of best practices to prevent and mitigate information security threats to academic research

Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud

In the present era ,cloud computing provides us a efficient way to share data amoung cloud users with low maintenance.But in multi-owner group ,there is a serious problem with preserving data and identity privacy due to frequent change of membership Some trends are opening up the period of Cloud Computing, which is an Internet-based improvement and utilize of computer technology. Security must be in given due importance for the cloud data with utmost care to the data and confidence to the data owne In this project ,we are proposing a secure multi-owner sharing scheme,for dynamic groups in the cloud.We are using group signature and encryption techniques. One of the biggest concerns with cloud data storage is that of data integrity verification at untrusted servers. To preserve data privacy, a basic solution is to encrypt data files, and then upload the encrypted data into the cloud. To resolve this problem recently the best efficient method MONA presented for secured multi owner data sharing.In our project ,we have removed the problem that occurred in existing system.In existing system whenever there is a revocation of member form group.manager has to generate a new key and then distribute to other members,this was a very tedious work,so we use a new technique of group signature so that the revoked member is not able to upload or download files. Now there is no need for generating new key each time whenever there is a revocation of members. DOI: 10.17762/ijritcc2321-8169.150515

Synchronized separation of atorvastatin—an antihyperlipidemic drug with antihypertensive, antidiabetic, antithrombotic drugs by RP-LC for determination in combined formulations

AbstractA new rapid and sensitive high performance liquid chromatography (HPLC) method has been developed for the simultaneous determination of atorvastatin—an antihyperlipidemic drug along with most commonly prescribed drugs (antihyperlipidemic, antihypertensive, antidiabetic, antithrombotic) in bulk and marketed combined formulations. The chromatographic separation was carried out by gradient elution mode with acetonitrile as organic modifier and 0.1% triethylamine acetate (TEAA) buffer pH 5 at a flow rate of 1mL/min and a diode array detector at wavelength 230nm was employed for detection of the analytes. Calibration curves were linear in the range of 5–150μg/mL for all the drugs with correlation coefficients of determination (r2 values)≥0.999. Limits of detection (LODs) and Limits of quantification (LOQs) ranged from 0.1 to 0.27μg/mL and 0.3 to 0.89μg/mL respectively. Intra-day and inter-day precision was studied at three concentration levels (20, 60 and 100μg/mL). The intra-day and inter-day RSD for all compounds was less than 2.0%. The accuracy for all compounds was found to be between 98% and 102%. Thus, the performance of the method described allows its use in quantification of atorvastatin along with 9 most commonly prescribed drugs available in market as atorvastatin combined dosage forms

3D Stacked Cache Data Management for Energy Minimization of 3D Chip Multiprocessor

In this model a runtime cache data mapping is discussed for 3-D stacked L2 caches to minimize the overall energy of 3-D chip multiprocessors (CMPs). The suggested method considers both temperature distribution and memory traffic of 3-D CMPs. Experimental result shows energy reduction achieving up to 22.88% compared to an existing solution which considers only the temperature distribution.  New tendencies envisage 3D Multi-Processor System-On-Chip (MPSoC) design as a promising solution to keep increasing the performance of the next-generation high performance computing (HPC) systems. However, as the power density of HPC systems increases with the arrival of 3D MPSoCs with energy reduction achieving up to 19.55% by supplying electrical power to the computing equipment and constantly removing the generated heat is rapidly becoming the dominant cost in any HPC facility

A descriptive study on awareness and attitude of medical undergraduates about the transgender population and their healthcare needs

Background: Medical education in transgender health care can empower physicians to identify and change the systemic barriers to care that cause transgender inequities as well as improve knowledge about transgender specific care. The objective of this study is to estimate the awareness among medical students about the terms related to transgender and their healthcare needs, to create an environment of inclusivity and educate the undergraduate MBBS students to provide highest level of care and advocacy for transgender population and to stress on the fact that the transgender education in the medical curriculum is an inevitable one in the modern medical practice. Methods: Knowledge questionnaire and TABS scale questionnaire was distributed to the 150 students through Google forms and their responses were collected. Knowledge questionnaire was given a score as 1 point for each (28 points). TABS questionnaire was assessed with Likert scale. Results: 52.6 percentage of the students have scored below the median and remaining 48.4 percentage of students have scored above the median score. The undergraduate students have a basic understanding about the healthcare needs of the transgender population and attitude towards transgender is on par with population of similar age group Conclusions: It is been observed that more knowledge regarding the terms about transgender population and their healthcare needs is needed for undergraduate students. A good curriculum to teach about basic health needs of transgender population has to be discussed

An unusual case of posterior vaginal wall cyst

Vaginal cysts are rare and are mostly detected as an incidental finding during a gynecological examination. The commonest type of simple vaginal cyst is the Mullerian cyst arising from paramesonephric duct remnants. These are typically lined by columnar epithelium and contain serous or mucinous fluid. A 41 year old multiparous woman presented with mass per vagina since 6 months. On examination, posterior vaginal wall cyst of 8 x 4 x 3 cm was detected. Surgical excision of the cyst was done under spinal anaesthesia by sharp and blunt dissection. The cyst was filled with mucoid material and histopathological examination confirmed mullerian origin. This is a rare presentation of mullerian cysts developing posteriorly

Laser guided automated calibrating system for accurate bracket placement

Background: The basic premise of preadjusted bracket system is accurate bracket positioning. It is widely recognized that accurate bracket placement is of critical importance in the efficient application of biomechanics and in realizing the full potential of a preadjusted edgewise appliance. Aim: The purpose of this study was to design a calibrating system to accurately detect a point on a plane as well as to determine the accuracy of the Laser Guided Automated Calibrating (LGAC) System. Materials and Methods: To the lowest order of approximation a plane having two parallel lines is used to verify the accuracy of the system. On prescribing the distance of a point from the line, images of the plane are analyzed from controlled angles, calibrated and the point is identified with a laser marker. Results: The image was captured and analyzed using MATLAB ver. 7 software (The MathWorks Inc.). Each pixel in the image corresponded to a distance of 1cm/413 (10 mm/413) = 0.0242 mm (L/P). This implies any variations in distance above 0.024 mm can be measured and acted upon, and sets the highest possible accuracy for this system. Conclusion: A new automated system is introduced having an accuracy of 0.024 mm for accurate bracket placement.Keywords: Hough transforms, Indirect bonding technique, Laser, Orthodontic bracket placemen

A CLINICAL STUDY OF PAPILLARY CARCINOMA OF THYROID

Objectives: The main objectives of the present study are to analyze the incidence of papillary carcinoma of the thyroid, study the clinical presentation and behavior of papillary carcinoma of the thyroid, and analyze various surgical modalities of the treatment of papillary carcinoma of the thyroid. Methods: It is a prospective study. A proforma for the study of all papillary carcinoma of thyroid patients was used. The presentation, clinical findings, investigations, and management line were documented. The study was conducted during the period December 2016–November 2018. A total of 50 cases of papillary carcinoma of thyroid in GIMSR-Visakhapatnam were selected based on simple random sampling techniques were clinically evaluated. Results: Among the 66 cases reported with different thyroid types of carcinoma, papillary thyroid cancer constitutes 75%. The results showed that 76% of cases occurred between the age group of 21–50 years. The incidence of female to male ratio was 3.2:1. In 62% of cases, goiter was the most presenting symptom and 92%of patients were euthyroid at the time of presentation. Conclusion: The incidence of papillary thyroid carcinoma in the present study is 75%, following the results of the previous studies. The most common mode of clinical presentation was thyroid swelling which was lower than those in a comparative study. The proportion of different histopathological types of papillary thyroid cancer was similar to those reported in the literature. The most common complication was transient hypoparathyroidism which resolved with calcium supplementation