Data science: Identifying influencers in social networks

Data science is a "concept to unify statistics, data analysis and their related methods" in order to "understand and analyze actual phenomena" with data. The common use of Online Social Networks (OSN)[2] for networking communication which authorizes real-time multimedia capturing and sharing, have led to enormous amounts of user-generated content in online, and made publicly available for analysis and mining. The efforts have been made for more privacy awareness to protect personal data against privacy threats. The principal idea in designing different marketing strategies is to identify the influencers in the network communication. The individuals influential induce “word-of-mouth” that effects in the network are responsible for causing particular action of influence that convinces their peers (followers) to perform a similar action in buying a product. Targeting these influencers usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network, we use centrality measures to identify assign an influence score to each user. The user with higher score is considered as a better influencer

Medical disease prediction using Grey Wolf optimization and auto encoder based recurrent neural network

Big data development in biomedical and medical service networks provides a research on medical data benefits, early ailment detection, patient care and network administrations.e-Health applications are particularly important for the patients who are unfit to see a specialist or any health expert. The objective is to encourage clinicians and families to predict disease using Machine Learning (ML) procedures. In addition, diverse regions show important qualities of certain provincial ailments, which may hinder the forecast of disease outbreaks. The objective of this work is to predict the different kinds of diseases using Grey Wolf optimization and auto encoder based Recurrent Neural Network (GWO+RNN). The features are selected using GWO and the diseases are predicted by using RNN method. Initially the GWO algorithm avoids the irrelevant and redundant attributes significantly, after the features are forwarded to the RNN classifier. The experimental result proved that the performance of GWO+RNN algorithm achieved better than existing method like Group Search Optimizer and Fuzzy Min-Max Neural Network (GFMMNN) approach. The GWO-RNN method used the medical UCI database based on various datasets such as Hungarian, Cleveland, PID, mammographic masses, Switzerland and performance was measured with the help of efficient metrics like accuracy, sensitivity and specificity. The proposed GWO+RNN method achieved 16.82% of improved prediction accuracy for Cleveland dataset

Importance of supervised learning in prediction analysis

Counterfeit medicines are fake medicines which are either contaminated or contain the wrong or no active ingredient. Up to 30% of medicines in developing countries are counterfeit. Using Supervised Machine learning techniques we build a predictive model for predicting sales figures given other information related to counterfeit medicine selling operations. Thus, by predicting the values we can identify these illegal operations and counter them. In this paper we have also mentioned the importance of Data mining and Machine Learning algorithms with some comparison analysis

Panorama of neoplasms of upper GI tract: a 5 year research study

Background: The diseases of the gastrointestinal tract (GIT) are the most common and leading cause of morbidity and mortality than the disorders of any other systems of the body. Gastrointestinal (GI) tract tumors are one of the most common cancers accounting for 11% of all cancers. Among these tumors, upper gastrointestinal tract malignancies are quite aggressive with a dismal prognosis. Malignant tumors are most common than benign. The most common carcinoma of the esophagus is Squamous cell carcinoma (SCC). Incidence of SCC is less than 5 per 100,000 populations in males and 1 per 100,000 populations in females. Gastric cancer was the second most common cancer in the World and 60% of them occurred in developing countries. The most common carcinoma of the Stomach is Adenocarcinoma.Aim & Objectives: To study the spectrum of neoplastic lesions of the upper gastrointestinal tract by the examination of endoscopic biopsies and surgically resected specimens. To determine the degree of severity of the malignancies by assessing the depth of invasion, Lymph nodal & Omental spread.Methods: The present study is both retrospective & prospective study for a period of 5 years from January 2007 to December 2011. The sample size includes all the endoscopic biopsies & surgically resected specimens of gastrointestinal tract received at Department of Pathology, S.V. Medical College, Tirupati. The study also obtained clearance from the ethical committee of the institution. The biopsy specimens thus obtained were fixed in 10% buffered neutral formalin. The sections were stained routinely with H & E. Special stains and IHC done wherever necessary.Results: we have received 120 specimens regarding the upper gastrointestinal system. Among these 120 specimens, 71 specimens were endoscopic biopsies & 49 specimens were surgically resected specimens. Out of 71 Endoscopic biopsies 28 biopsies were malignant among which 2 was esophagus and 26 were stomach. Out of 49 surgically resected specimens 1 was benign and 32 were malignant tumors. Out of 59 neoplasms of stomach there were single cases each of Sub mucosal Lipoma, Malignant lymphoma, GIST & 56 cases of Adenocarcinoma & its variants were noted.Conclusion: Most of the neoplasms are of stomach (97%). All the neoplasms are malignant except one benign lesion sub mucous lipoma of stomach. Most of the neoplasms of stomach were Adenocarcinoma (96.5%). Both tumors of esophagus were squamous cell carcinoma occurred after 50 years of age.

Study of various congenital anomalies in fetal and neonatal autopsy

Background: The study of dead is to save the livings. The growing awareness that still births and infant mortalities are unable to reduction has led to a wide spread desire for more information regarding the cause of these deaths. Congenital malformations have become important cause of fetal and neonatal (perinatal) mortality in developed countries and would very soon be increasingly important determinants of fetal and neonatal mortality in developing countries like India. In spite of antenatal diagnostic modality still the fetal autopsy plays the vital role in the conformation as well as identification of congenital anomalies and also for the counseling of the parents, to prevent the fetal congenital anomalies in further pregnancies. This study was undertaken with the purpose of finding out cause of death during the perinatal period at government maternity hospital and pediatric department S.V.R.R.G.G.H. & S.V. medical college Tirupati, and to study the clinical and pathological findings (Gross & microscopic) in fetal and neonatal death.Methods: The present study of congenital anomalies in fetal and neonatal deaths was done at S.V. medical college, Tirupati, over a time period of 2 years from September 2008 to 2010 August. Consent for autopsy in requested compassionately, respectfully and fully informed. The present study included dead fetus and neonates with gestational age above 20 weeks of intra uterine life and within 7 days of post natal life. All fetuses of gestational age <20 weeks and all neonates above 7 days of age were excluded from the study. The study also obtained clearance from the ethical committee of the institution. Autopsy was performed by standard technique adopted by Edith L. Potter. External and internal findings followed by histopathological examination, and autopsy findings were compared with available ultrasound findings.Results: A total of 46 Autopsies performed, 40 (87%) were fetal deaths, 6 (13%) were early neonatal deaths. In a total of 46 fetuses, there were 13 male and 33 female babies. On external examination of 46 fetal and Neonatal (perinatal) deaths, 8 (17.39%) babies showed congenital malformation. On internal examination of the 46 fetal and Neonatal (perinatal) deaths, 4 babies showed internal congenital anomalies. A total of 46 anatomical and histopathologic examinations were done among fetal and neonatal (perinatal) deaths. Out of 13 autopsies on male babies, 2 had congenital malformation and 33 autopsies on female babies, 7 had congenital malformations. Congenital anomalies were commonest in the birth weight group of 1000-1500 grams accounting for 9 cases. Malformations of central nervous system (33.33%) were most common followed by musculoskeletal system (16.66%), genitourinary and respiratory system (8.33%) respectively.Conclusion: Most number of perinatal deaths occurred in low birth weight and preterm babies. Study of malformations greatly helpful in genetic counseling and prenatal diagnosis in successive pregnancies

DATA MINING AND TEXT MINING: EFFICIENT TEXT CLASSIFICATION USING SVMS FOR LARGE DATASETS

The Text mining and Data mining supports different kinds of algorithms for classification of large data sets. The Text Categorization is traditionally done by using the Term Frequency and Inverse Document Frequency. This method does not satisfy elimination of unimportant words in the document. For reducing the error classifying of documents in wrong category, efficient classification algorithms are needed. Support Vector Machines (SVM) is used based on the large margin data sets for classification algorithms that give good generalization, compactness and performance. Support Vector Machines (SVM) provides low accuracy and to solve large data sets, it typically needs large number of support vectors. We introduce a new learning algorithm, which is comfortable to solve the dual problem, by adding the support vectors incrementally. It majorly involves a classification algorithm by solving the primal problem instead of the dual problem. By using this, we are able to reduce the resultant classifier complexity by comparing with the existing works. Experimental results done and produce comparable classification accuracy with existing works

Sustainable development of Flexible Assertion on Multi-Modal Classification of Brain Tumours using Deep Learning

In the field of medical science, classifying brain tumours is vital. In order to get an effective and proper treatment for the disease, accurate and finding type of the brain tumour is much essential in the case of brain tumour treatment. In addition to providing treatment for tumours as early as possible, it also helps in saving a life by allowing medication to be administered in due course. DL has developed into a fantastic tool for medical professionals and researchers to act quickly and decisively with tumour patients. In this paper, we suggest Sustainable development of flexible approach aimed at multi-model organization of brain tumours using the popular deep learning architecture ResNet-50. By leveraging the flexibility of ResNet-50, we aim to achieve improved accuracy and robustness in classifying brain tumours across a diverse range of datasets. Our approach integrates multiple ResNet-50 models, each specialized in identifying specific tumour types, enabling a comprehensive classification framework. Experimental findings show that our strategy is successful and more accurate than other approaches. In this paper we provide an interface that can be used to classify and label the tumours. We used Keras and Tensorflow to create a cutting-edge Convolutional Neural Network (CNN) architecture to categorise 3 kinds of growth or tumours namely - Meningioma, Gliomaand Pituitary using ResNet50 algorithm. It is estimated that this model has a maximum mean accuracy score of 98.88%

Performance Comparison of CNN and DNN Algorithms for Automation of Diabetic Retinopathy Disease

Automation of medical image analysis helps medical practitioners to ensure early detection of certain diseases. Diabetic Retinopathy (DR) is a widespread condition of diabetes mellitus and a main global cause of vision impairment. The manual diagnosis of diabetic retinopathy by ophthalmologists requires a significant amount of time, causing inconvenience and discomfort for patients. However, the use of automated technology makes it possible to quickly identify diabetic retinopathy, permitting the continuation of therapy without interruption and averting future ocular damage. This paper presents a comprehensive comparative analysis of six Convolutional Neural Networks and Deep Neural Networks based machine learning models, including simple CNN, VGG16, DenseNet121, ResNet50, InceptionV3, and EfficientNetB3, for the recognition of diabetic retinopathy using fundus photographs. The accuracy of various models is evaluated using the Cohen Kappa metric. The results of this study add a contribution to the research on the application of machine learning models for diagnosing diabetic retinopathy

Predicting the Spread of the Corona Virus Disease Requires Analyzing Data from Cases across Multiple States in India

Data analysis is very sophisticated tool in recent corona virus pandemic to find the trend of spreading pattern for controlling the infection. In this perspective, predictive analytics can be useful for data analysis to forecast the corona virus pandemic. This paper presents the infection pattern of corona virus disease, termed as COVID-19 in top seven states in India. Prophet Algorithm forecasting model was used to analyze state-wise spreading pattern of corona virus disease with respect to confirmed, deaths and cured cases. This predictive model can be very helpful to government and health care communities to combat this deadly virus by initiating suitable actions to control its spread