Search

Diabetic Retinopathy (DR) is a burgeoning malady in Asian territories. DR causes 5% to 7% of the total blindness throughout the region. The main aim of this research is to determine whether a patient is suffering from DR or not by the dint of 2-D color fundus retina scans. In this paper authors have proposed a GUI-based technique an Ensemble Diabetic Retinopathy Detection (EDRD). This method helps in detecting 2D color fundus scan with an efficient approach for finding DR-affected persons within a few seconds using ensemble techniques of CNN and RLU. The Visual Geometry Group (VGG-19) model and Adaptive Moment Estimation optimizer is used for training and reducing error for the developed technique. Maximum accuracy of 92% was obtained for 80% training set with 0.001 learning rate and 25 batch size. The proposed research contribution definitively detects whether the given OCT scan with an efficient approach for finding DR affected persons within few seconds.

RT-PCR is considered to be the standard gold diagnostic test for detecting COVID-19 causing SARS-CoV-2. Recent reports and recent pieces of evidence from scientific literature, however, tell a different story. There have been speculations of SARS-CoV-2 escaping the RT-PCR because of the series of mutations it has gone through. It is possible that host-dependent RNA editing and high person-to-person transmission have equipped the virus with mutations enabling it to spread faster and even evade the RT-PCR. Added to this is burnout among healthcare workers and technicians handling the RT-PCR machines and sampling. All of these factors may be working in unison to result in the deluge of false-negative cases India is facing during the second COVID-19 wave. The mutant strains are spreading to other parts, posing challenges to the whole world. These circumstances warrant supplementary diagnostic tests such as serological and radiological findings to deal with cases of high clinical suspicion. Even one misdiagnosed COVID-19 patient poses a risk to hundreds of others in the vicinity. Healthcare workers' burnout also has to be dealt with. Erroneous staff should be re-trained.

Zika virus is an RNA virus belonging to the Flavivirus family that is chiefly transmitted by the female Aedes mosquito. The Zika virus first infected humans in Uganda and Tanzania in 1952. Since, it has spread to several parts of the world causing outbreaks of variable extent. In India, these outbreaks have been reported from Gujarat, Tamil Nadu, Madhya Pradesh, Rajasthan, Kerala, and Maharashtra. The most recent outbreak is from the most populous state of India, Uttar Pradesh, where the climate is conducive to the breeding and transmission of other arboviral infections such as Dengue, Chikungunya, and Malaria. These infections also happen to share similar incubation periods and overlapping clinical manifestations with Zika virus (ZIKV) infection, leading to misdiagnoses or delayed diagnosis. We aim to provide an account of the outbreak, its repercussions, errors made in attempting to contain the spread of the disease, and, measures to be taken in the future. Efa Khan et al, The 2021 Zika outbreak in Uttar Pradesh state of India: Tackling the emerging public health threat, Tropical Doctor (52, 4) pp. . Copyright © 2022. DOI: 10.1177/00494755221113285. Users who receive access to an article through a repository are reminded that the article is protected by copyright and reuse is restricted to non-commercial and no derivative uses. Users may also download and save a local copy of an article accessed in an institutional repository for the user's personal reference. For permission to reuse an article, please follow our Process for Requesting Permission. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.

BACKGROUND The relationship between liver enzymes like alanine aminotransferase (ALT), aspartate aminotransferase (AST), Alkaline phosphatase (ALP) and diabetes has been studied, but the results of these are inconsistent. Several prospective studies have reported that ALT was associated with incident diabetes. We wanted to study the levels of serum alanine aminotransferase (ALT), serum aspartate aminotransferase (AST) and serum alkaline phosphatase (ALP) in patients of Type 2 DM. METHODS This is a hospital based observational study which was conducted in Narayan Medical College and Hospital, Jamuhar, Sasaram, Rohtas, Bihar. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), glycosylated haemoglobin (HbA1c), FBS, PPBS, total bilirubin, and hepatitis B surface antigen (HBsAg) were determined in all study participants. RESULTS The mean value was 96.35, 67.37, 152.78 and 1.098 for AST, ALT, ALP and total bilirubin. On multivariate analysis for effect of FBS, PPBS, HbA1c on the value of AST weak correlation was found with R square of 0.11. On the effect of FBS, PPBS, HbA1c on the value of ALT we found weak correlation with an R square of 0.079. CONCLUSIONS There is a weak correlation between deranged liver enzyme (AST, ALT and ALP) with HbA1c, FBS and PPBS. But still, liver functions should be monitored in diabetic patients. KEYWORDS ALT, AST, Diabetes, HbA1c, FBS, PPBS

BACKGROUND Diabetes is the most important risk factor for chronic kidney disease. Because of this, clearance of many oral hypoglycaemic drugs from our body is hampered and time of exposure to these drugs increases. Therefore, patients of diabetes need to be assessed frequently for the development of diabetic nephropathy and the dosage of oral hypoglycaemic drugs should also be modified accordingly. So, the purpose of this study was to evaluate the doses of prescribed OHAs in newly diagnosed diabetic nephropathy patients. METHODS A hospital based observational study was conducted at Narayan Medical College and Hospital, Sasaram, Bihar, India. A total of 600 diagnosed patients of diabetic nephropathy of age more than 18 years visiting for the 1 st time in OPD of general medicine / nephrology department were included in the study after obtaining an informed consent. The results were expressed as a percentage of the assessed population. RESULTS Only about 38% of the total study population was taking doses of Oral Hypoglycaemic Agents (OHAs) according to their GFR status. 62% were taking wrong doses of OHAs with their respective renal function. CONCLUSIONS Treatment of diabetes in a CKD patient is more difficult than in non-CKD patient. GFR status should be monitored on regular basis in patients of diabetes with CKD. A significant number of these patients are still treated with wrong dosage of OHAs for their respective renal function. KEYWORDS Wrong Dose of OHA, Diabetic Nephropathy, Oral Hypoglycaemic Drugs

Sheehan syndrome, also referred to as postpartum hypopituitarism is often a sequela of massive post-partum hemorrhage in resource poor healthcare settings where blood loss during delivery is often neglected. The diagnosis of this rare but fatal is disease is often delayed because the symptoms are vague and the pituitary dysfunction is insidious in nature. This condition sometimes takes more than a decade before it is diagnosed. The classic signs and symptoms include agalactia, secondary amenorrhea and dry wrinkling skin. Often an empty or a partially empty sella is seen on magnetic resonance imaging of the brain. We present the case of a 35-year-old multiparous female with anhedonia and raised serum transaminases who presented to our side with mild hyponatremia and hypothyroidism. She got diagnosed with Sheehan syndrome at our set up six years after her last obstetric event. Through this case report, we intend to provide a clinical account of the patient’s condition and management.

In the present era the demand for fossil fuels is increasing at an alarming rate and to meet the need of time, it is required to switch to renewable sources for our energy needs. Hydrogen is reportedly considered as a future fuel as it can store three times more energy as compared to conventional fuel and emit zero emission [1]. However, it’s safe and efficient storage is quite a challenge. Out of the three modes of storage, solid storage of hydrogen is proved to be the safest [2]. The common materials used for the electrochemical hydrogen storage are reported collectively in this paper. These include complex hydrides, activated carbon (aC), metal hydrides, metal organic frameworks, capillary arrays, clathrate hydrates, metal nitrides, doped polymer and zeolites. This paper presents a comparative study of selective materials used so far for electrochemical hydrogen storage with broader prospects in the field of hydrogen fuel technology. The literature review in this paper provides an insight of the materials that have already been used for storing hydrogen in solid-state. The study also highlights the limitations of using these materials

Systemic lupus erythematosus (SLE) is a multisystemic autoimmune disease that can affect almost every organ in the body. Its complications can often be fatal. The fatal complications include lupus cerebritis, lupus nephritis, and cardiac manifestations such as pericardial effusion. In this report, we discuss the case of a 23-year-old female who presented with complaints of high-grade fever, seizures, and altered mental state (AMS) and was found to have generalized lymphadenopathy (LAP). Various blood and urine analyses and radiological findings (chest X-ray, MRI of the head) were suggestive of lupus nephritis, lupus cerebritis, massive pericardial effusion, and thrombocytopenia. Her anti-double stranded DNA (anti-dsDNA) antibody was positive, and her pericardial fluid was positive for anti-nuclear antibodies (ANAs). She was administered IV glucocorticoids and phenytoin. She reported improvements in her symptoms gradually for a few days but eventually succumbed to the disease. Although generalized LAP is a rare initial presentation of SLE, it should be included in the differential diagnosis of the disease.

Air contamination in urban areas has risen consistently over the past few years. Due to expanding industrialization and increasing concentration of toxic gases in the climate, the air is getting more poisonous step by step at an alarming rate. Since the arrival of the Coronavirus pandemic, it is getting more critical to lessen air contamination to reduce its impact. The specialists and environmentalists are making a valiant effort to gauge air contamination levels. However, its genuinely unpredictable to mimic subatomic communication in the air, which brings about off base outcomes. There has been an ascent in using machine learning and deep learning models to foresee the results on time series data. This study adopts ARIMA, FBProphet, and deep learning models such as LSTM, 1D CNN, to estimate the concentration of PM2.5 in the environment. Our predicted results convey that all adopted methods give comparative outcomes in terms of average root mean squared error. However, the LSTM outperforms all other models with reference to mean absolute percentage error., 8 pages, This paper is accepted and presented in the IEEE 6th I2CT 2021 conference. The final version of this paper will appear in the conference proceedings

Introduction: Coronavirus, a novel menace, is exacting terrible human toll and mass death till date. It was thought that COVID-19 primarily affects the respiratory system, but recent studies have reported gastrointestinal manifestations as well. However, the incidence of acute pancreatitis in the setting of COVID-19 is a rising debate due to unknown mechanisms, especially in the absence of any precipitating factors for pancreatitis Case Description/Methods: An otherwise healthy 30-year-old female presented to the emergency department with pain in the lower abdomen radiating to the back, fever, cough, vomiting, diarrhea, and sore throat for the past seven to ten days. Her initial examination revealed a temperature of 100°F, a pulse rate of 85 beats /minute, blood pressure of 110/70 mmHg, respiratory rate of 20 breaths/minute, and oxygen saturation of 94% on room air. Her swab result for SARS-CoV-2 reverse transcriptase-polymerase chain reaction (RT-PCR) came positive. Laboratory testing revealed a rise in serum lipase and serum lipase level. Her ultrasonography (USG) of the abdomen was suggestive of fatty liver changes (hyperechoic parenchymal echogenicity) and no gallstones. She was managed conservatively with intravenous fluid, analgesics and antibiotics for bacterial infection. Absence of any predisposing factor for pancreatitis in the setting of COVID led to the diagnosis of acute pancreatitis induced by COVID-19. Her contrast-enhanced computed tomography (CECT) of the abdomen also confirmed our suspicion. Discussion: The rationale behind the development of acute pancreatitis in the presence of COVID- 19 infection, with no past medical history and precipitating risk factor for pancreatitis, is suggestive of COVID-19 as a triggering factor. This case highlights the importance of pancreatic enzyme monitoring if patients have abdominal complications and without further delay, proper management can be set up if diagnosed early. Future complications such as chronic pancreatitis and pseudocyst formation can also be prevented if timely diagnosed. (Figure Presented) .

Antisynthetase syndrome is an autoimmune condition that manifests clinically through signs and symptoms, such as interstitial lung disease, myositis, Raynaud's phenomenon, fever, hyperkeratotic fingertips (mechanic's hands), and arthritis. It is associated with antibodies against aminoacyl tRNA synthetase enzyme, the most common autoantibody being the anti-Jo-1. An 18-year-old girl presented with weakness of both the upper and lower limb, swelling and generalized body pain, difficulty in swallowing. MRI of the thigh was highly suggestive of myositis with symmetrical bilateral involvement. Based on proximal muscle weakness, elevated creatine phosphokinase (CPK), and lactate dehydrogenase (LDH), strongly positive anti-nuclear antibodies human epithelial cell type-2 (ANA-HEp2), and a normal nerve conduction velocity test with precise MRI findings, a diagnosis of polymyositis was made. She was given bolus intravenous methylprednisolone for five days, followed by oral methylprednisolone with subcutaneous methotrexate weekly. She reported a 50% improvement in muscle weakness; however, partial bulbar weakness persisted at the time of discharge. On her next follow-up, her blood investigations for auto-antibodies were done. The autoantibodies anti-Jo-1 (3+), Ro-52 (2+), and Mi-2β (2+) were found to be positive. These investigations, coupled with the clinical features she was presenting, finally led us to conclude that it was a case of polymyositis complicated by the antisynthetase syndrome.

Social Internet of Things is a critical digital tool for companies of different sizes. Good governance of the Social Internet of Things allows successful strategy to emphasise brands, making products no longer discretionary. Buzz marketing is a powerful advertising strategy that focuses on exploiting the value of a promotion or item, through conversations between family members and friends, or through wider conversations on the Social Internet of Things platforms. Buzz's forecast allows for an overview of the ranking of retailers by posts shared by future consumers or members on social media platforms. Buzz estimation in the Social Internet of Things Platforms, like Twitter, is a daunting challenge over real-time data by defining multiple aspects for buzz exploration. The bulk of buzz predictor analysis studies are modelled on machine learning methods like Radial Base Feature, Neural Network Models, Ant Colony Optimization, and Support Vector Devices. In this study, Random Forest is being used to make buzz predictions as it creates multiple decision trees and mixes them together again to make predictions more precise. The findings of comparative studies with Support Vector Machine (SVM) in three separate kernels and Radial Basis Function (RBF), it suggest that Random Forest is better at forecasting buzz and has also established a rating list of important features in order to achieve reasonable prediction performance. The features suggest that the main important characteristics to define the subject being generated are either “Buzz” or “Not Buzz”. Random Forest showed an average improvement in accuracy with a rate of 99% and the highest training time. However, although the Radial Based Function method has proved to be quite efficient, the Support Vector Machine solution is of mediocre accuracy.

Social networking site Twitter, in particular, has become a popular spot for gossip. Rumors or false news spread very easily through the Twitter network by re-tweeting users without understanding the real truth. These reports trigger popular confusion, threaten the authority of the government and pose a major threat to social order. It is also a very necessary job to dispel theories as quickly as possible. In this research, multiple descriptive and consumer-based features via tweets are retrieved and integrated these features with the TF-IDF system to develop a composite set of features. This composite set of features is then used by several machine learning techniques like Support Vector Machine (SVM), Linear regression, K-Nearest Neighbor (KNN), Naive Bayes, Decision Tree, Random Forest, and Gradient Boosting. Along with these machine learning classification models, a Convolutional Neural Network (CNN) algorithm is proposed to distinguish rumour and non-rumor tweets. The proposed model is evaluated with freely accessible twitter datasets. The existing machine-based learning models have acquired an Fl-score of 0.46 to 0.76 for rumour detection, while the CNN model attained an Fl-score of 0.77 for rumour class. Overall, the CNN model yields greater results with a weighted average Fl-score of 0.84 for both rumour and non-rumor categories. The potential mechanism will help to detect misinformation as quickly as possible to counteract the dissemination of rumours and build users' deep confidence in social media sites.

With the epidemic progression in resources on IoT, discovery emerges as an eminent challenge due to requirement of their self-automation. The traditional resource discovery approaches do not provide efficient methodologies due to continuously changing IoT search metrics such as syntax, access, architecture, etc. To address the gap, the paper proposes an optimized technique, namely, Modified Genetic Algorithm for Resource Selection (MGA-RS) that intends to discover optimum data (resources) is short period of time by considering the bit strings of chromosomes. It is evaluated on datasets of Ionosphere from machine learning repository of university college, London. The best and mean fitness are selected in a way that they should be close to each other at the time when MGA-RS reaches termination condition and to minimize classification error from kNN. It is found that MGA-RS outperforms well with kNN based fitness function and is approximately 14% and 15% better than simple and rastrigin fitnesses, respectively, for selecting the optimal resources in IoT.

The fundamental requirement for communication and computation across distinct application areas on Social Internet of Things (SIoT) is the resource discovery that demands appropriate reasoning for the optimal selection. With exponential growth of resources and their produced huge amount of heterogeneous data, various activities face challenges due to interoperability. In order to eliminate the challenge, the paper focuses on to propose an optimal resource selection technique namely, Modified Cuckoo Search (MCSA). The technique helps in reducing traffic congestion on network by selecting optimal resources in less time. The technique is tested on random dataset. The obtained results show that MCSA outperforms 22% approximately in comparison to nature-inspired, meta heuristic based machine learning algorithms i.e., Particle Swarm Optimization and Binary Genetic Algorithm.

Energy production from renewable sources is increasing at a quite high rate in order to have a refined phase of energy consumption. Hydrogen has emerged as a clean and efficient energy carrier since 1839. The storage of hydrogen is one of the essential requirements for the development of renewable-based energy system but, it still remains a great challenge for technologists. Many researchers have reported about the numerous materials being used for hydrogen storage. Nanostructure oxide and activated carbon (aC) material seem to be more suitable for storing the hydrogen efficiently. In the present review, numerous hydrogen storage materials those are being used for electrochemical hydrogen storage have been discussed. The relative comparison of different hydrogen storage materials may be very useful for future experiments as electrochemical hydrogen storage.

Fingerprints are considered as a unique identification of a person and due to easy access its the best and one of the fastest method used in biometric identification systems. They are unique, so secure and reliable to use and doesnt change for one in a lifetime. And beside these things fingerprint recognition specially using minutiae matching technique is cheap, reliable and accurate up to a satisfactory limits. In this thesis work, we propose a method for fingerprint matching based on minutiae matching. However, unlike conventional minutiae matching algorithms our algorithm also takes into account region and line structures that exist between minutiae pairs. This allows for more structural information of the fingerprint to be accounted for thus resulting in stronger certainty of matching minutiae. Also, since most of the region analysis is preprocessed it does not make the algorithm slower.

Fingerprint matching is the process used to determine whether two sets of fingerprint ridge detail come from the same finger. There exist multiple algorithms that do fingerprint matching in many different ways. Some methods involve matching minutiae points between the two images, while others look for similarities in the bigger structure of the fingerprint. In this paper, we provide a study of the existing techniques in the area of finger print matching algorithms.

Clustering is a mechanism of arranging data points of a data set in groups which are similar. The similarity is found on the basis of some metric like Euclidian distance, density, etc. One of the major data clustering methods is the K-Means in which initial centroids are selected randomly. Here, we have presented an effective and modified algorithm, the Sorted K-Means which determines initial centroids after sorting the data points and provides stable clusters using lesser number of iterations. The tool used for implementation is Matlab to find initial centroids so as to reduce number of iterations for K-Means cluster formation. As a final result, stable clusters are obtained with minimized complexity.

Electricity bill generation and meter reading is a very complex process in today scenario. The existing method of billing process uses manual work of taking reading of the meter, updating details of meter and sending bill to the customer. Till now, this is being implemented by using OCR but it does not allow user to track the consumption each second. We are developing a method on IoT platform which is more efficient and can be used for keeping track of electricity consumption. User can get the reading on android any time. Our electricity distributor will no longer need to take reading manually. The data will be directly transferred to them which will be used to create electricity bills. For multiple meters in single household, combined result can be viewed. Also, Client is able to view the graphical usage for a particular duration.

The Underwater Acoustic Sensor Networks (UWASN) has sensors which are deployed underwater for gathering information for the unexplored parts of oceans or rivers. UWASN consists of variable number of floating and anchored sensors, sink and vehicles that are deployed over an area to be explored. Thus the deployment efforts of UWASN have curbed with preposterous budgetary price and eminent power depletion of hardware related to acoustics targeted a long range revelation in deep water. For solving this problem, software driven approach for UWASN is discussed which will confide various electronics device that are coupled with modem for establishing revelation links underwater. The revelation can be classified in two parts: Single and multi hoping. But in underwater we use multi- hop revelation for sending data from end nodes to sink nodes. The various units like intonation, filtering, symbol synchronization and denotation etc are embedded in software driven underwater acoustic sensor network (SD-UWASN) and thus the application opportunities are addressed related to SD-UWASN is presented. Hence, in this paper we have focussed on various challenges related to software driven UWASN with discussing various software design parameters.