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Diabetic retinopathy (DR) is a medical condition that can affect the patient's retina and cause leaks in the blood due to diabetes mellitus. The increase in cases of diabetes limits existing manual testing capability. Today new algorithms are becoming very important for assisted diagnosis. Effective diabetes diagnosis can benefit the victims and reduce the negative harmful effects, including blindness. If not treated in a timely manner, this disorder can cause different symptoms from mild vision problems to total blindness. Early signs of DR are the hemorrhages, hard exudates, and micro-aneurysms (HEM) that occur in the retina. Timely diagnosis of HEM is important for avoiding blindness This paper presents PSO feature selection algorithms with three classifications for the detection of Diabetic retinopathy using python.

Due to lack of server reliability and user data privacy encryption of data is required before the cloud is outsourced. We have found a compromised method within the blockchain in order to perform a keyword search which is secured on data that is encrypted against malicious service providers and users in cloud. SSE: Privately offers the cloud storage used in symmetric search encryption (SSE) systems, which cannot be regarded as a true cloud. The cloud service is also known to be credible. Let us start by emphasizing the importance of data storage within a public chain. The client is allowed by system to upload them in form which is encrypted, data content is distributed to the nodes of cloud and make sure that the data is available through encryption techniques. Presentation of a blockchain based system for providing the keyword search service with secure storage of distributed data. TKSE performs verifiability on server side so that true cloud servers are protected from being posed by owners of malicious data in the data storage process. Furthermore, technology of blockchain and hash functions are used to allow payment which is fair without third parties involvement for research fees, although if cloud or user is harmful. Our review of security and evaluation of performance show that TKSE is efficient and safe and be suited for cloud computing.

Parkinson disease is a common mass measurement problem in public health. Machine-based learning is used to differentiate between the stable and Parkinson's disease people. This paper provides a comprehensive review of the Parkinson disease buying estimate using machine-based learning approaches. A brief introduction is given to various methods of artificial intelligence, focused on strategies used to predict Parkinson disease. This paper also offers a study of the results obtained by using MRMR feature selection algorithms with four classifications for Parkinson’s disease detection using python

According to the survey 17.5 million deaths are happened due to the cardiovascular disease that leads to create heart attack, chest pain and stroke. Based on the survey it clearly concludes that most of the people affected by heart problem that need to be identified in the earlier stage for eliminating the future risk in patient health. The importance of the heart disease detection process helps to create the earlier detection system for identifying heart problem by using machine learning and optimized techniques but the developed forecasting systems are difficult to predict the heart problems in an accurate manner with minimum time. So, hybridized Ruzzo–Tompa memetic based deep trained Neocognitron neural network is introduced to analyze the heart disease related features and predict the heart disease in earlier stage. First, heart disease data has been collected from UCI repository, dimensionality of the data is minimized by hybridized Ruzzo–Tompa memetic approach. After reducing the number of features, that are trained by deep learning approach which analyze the features using maximum number of hidden layers that used to predict heart disease features successfully while making the Neocognitron neural network classification. Further efficiency of the system is evaluated using MATLAB based simulation results.

Diabetes is the key critical issue needs to be concerned for various problems in our body. Increase in glucose and fructose content in our body results in diabetes mellitus. When a body generates higher insulin level than the required, it results in increased urination and excessive thirstiness which in turn results in kidney failure and other cardio-related issues. Many research agencies invested their funds on defining the predictive methodology and finding the root cause of those results in mellitus. Mellitus results in the highest mortality rate compared to any other disease reported by the health organizations across the globe. In this, the predictive methodologies, various classification techniques are discussed, and the results are analyzed. The classification methodology could be on medications, food habits, personal behaviors, age factors and so on. The datasets are processed and analyzed with the neural network algorithms, and the results are compared with one another. The datasets are taken from the National Family Health Survey results published during the period of 2016–2017. The result implies that men between ages 15–49 among 1 billion people have reported with diabetes mellitus. Diagnose and forecast on this disease are done by recognizing the pattern formation and grouping the similar structures. Various algorithmic techniques like M-layer perceptron, nearest neighbor, vector machines, data regressions, binary regression and their accuracy of forecast, speed and sensitivity are calculated, analyzed and compared to define the accurate prediction methodology over a short span of time. The forecast methodologies are focussed to provide solutions to avoid the intensive care system provided proper medications with a long duration when it is been predicted to be a risk factor. A statistical method of analyzing is performed for the comparative analysis. The learning and training methodologies are discussed in this system. Accuracy, specificity, sensitivity are the key parameters to define the best forecast methodology. Classification on association, regression techniques and neural algorithmic techniques is analyzed and compared to refine the best predictive forecast methodology by processing 30 samples across the states of India with focus on determining the type of mellitus along with the accuracy on definition. The forecast data utilized to define the type of mellitus and the prediction on critical measures over a period of time.

Cardiotocography (CTG) records fetal heart rate (FHR) and uterine contractions (UC) simultaneously. The CTG,*which is one of the*most common*diagnostic techniques used during pregnancy and before delivery to evaluate maternal and fetal well-being. Doctors can understand the state of the fetus by observing the*Cardiotocography trace patterns. There are several techniques for interpreting a typical cardiotocography data based on signal processing and computer programming. Only a few decades after cardiotocography has been implemented into clinical*practice, the predictive potential of these approaches remains controversial and still unreliable This paper presents MRMR feature selection algorithms with four classification for Fetal risk prediction using python.

Machine learning is used as an effective support system in health diagnosis which contains large volume of data. More commonly, analyzing such a large volume of data consumes more resources and execution time. In addition, all the features present in the dataset do not support in achieving the solution of the given problem. Hence, there is a need to use an effective feature selection algorithm for finding the more important features that contribute more in diagnosing the diseases. The Particle Swarm Optimization (PSO) is one of the metaheuristic algorithms to find the best solution with less time. Nowadays, PSO algorithm is not only used to select the more significant features but also removes the irrelevant and redundant features present in the dataset. However, the traditional PSO algorithm has an issue in selecting the optimal weight to update the velocity and position of the particles. To overcome this issue, this paper presents a novel function for identifying optimal weights on the basis of population diversity function and tuning function. We have also proposed a novel fitness function for PSO with the help of Support Vector Machine (SVM). The objective of the fitness function is to minimize the number of attributes and increase the accuracy. The performance of the proposed PSO-SVM is compared with the various existing feature selection algorithms such as Info gain, Chi-squared, One attribute based, Consistency subset, Relief, CFS, Filtered subset, Filtered attribute, Gain ratio and PSO algorithm. The SVM classifier is also compared with several classifiers such as Naive Bayes, Random forest and MLP.

Over the past couple of years, the conception of Emotional Intelligence (EI) is a significant attribute of effective leadership. EI skills are crucial to boost students' achievements and make sure of the well-being of a faculty as a learning community. This study analyzes the EI in college financial professional teaching from the perspective of Inclusive Finance (IF). It outlines the concept of EI and determines the importance of EI skills associated with IF. This paper explores how the EI influences with the IF. A qualitative approach to collect information through interviews with twelve participants as well as four principals, four deputy principals, and four lecturers was made. The analysis of the info indicated that EI contained a positive influence on IF. Moreover, this work employed a large variety of skills to achieve success and substantive, and EI is one of the abilities set that will facilitate them in their endeavors to attain effective teaching, meet the educational and teaching desires of scholars and lecturers.

Cloud computing has the potential to change the world we see today, and this has been duly recognised due to the amazing advantages it offers. One of the main advantages it offers is that cloud computing can reduce the wastage of resources and efficient upgrade effectiveness, increasing the proper application of business computer resources or improving computing device’s effectiveness and efficiency of the cloud computing itself can achieve the best applications.Universities and Colleges are the core of innovation through their advanced research and development. Higher education libraries of different size and type all over the world suffer from common problems such as shrinking budgets, accommodating resources within the budgetary constraints, Countering increased scope of interdisciplinary and multidisciplinary nature of subjects, flexibility associated with the e-data, diminished levels of efficiency, and huge cost involved in managing the entire IT infrastructure in the modern day academic libraries. Cloud computing can overcome these problems and transform the library infrastructure through digitization.In the proposed paper, the plan is to design a “Library Cloud” which would be the consortium of many digital libraries of different universities/academic institutions and also discuss the security features required to implement this cloud. This “Library Cloud” would help in saving so many resources of the libraries involved and sharing of knowledge amongst the members of the institutions concerned, which otherwise individually would not be feasible and possible.

Mobile devices are playing a vital role in information processing systems in day to day life. Current trend suggests that smart phones are going to be the first-most computing devices which many people will carry with them for maximum time everywhere. However, smart phones are still lacking behind the essential resources. This demands the need for Mobile Cloud Computing, where the key elements of data storage and data processing are carried outside the mobile devices. So this provides flexibility to mobile users as, the application and the data stored remotely can be accessed on-the-fly with high speed networks. Variation among the mobile network on basis of elements such as, bandwidth, unstable network and its uncertain availability are barriers for Quality Of Services in the performance. Thus the solution is to connect the mobile devices heterogeneously where they work collaboratively as resource providers. Our mechanism suggest for “Mobile to Hub to Cloud” model which can tackles the network disparity problem. A scenario gives the perfect example for the need of such model, mobile users in areas with infrastructure where the costs of accessing the network is too high, such as traveler who doesn’t wished to pay high roaming charges. Thus the suggested model can evaluate the following options: storing data in mobile hubs and improving the message delivery performance. So as in our example, traveler now can push the data onto the mobile buffers or hubs which in turn will send it to the cloud server.

In this paper the, Antimonide-based HEMTs have been simulated with an In 0.1 As 0.9 Sb channel and Al 0.1 In 0.9 Sb barrier and back-barriers. These HEMTs with 50 nm gate length exhibit improved electron density, mobility, DC and analog/RF performances.

A potential approach to the treatment of genetic disorders is gene therapy. The goal of gene therapy is to introduce therapeutic genetic material into the target cell to exert the intended therapeutic effect. Gene therapy has already shown promising results for the treatment of monogenic disorders such as severe combined immunodeficiency and haemophilia. Now the procedure has been extended to the level of treating malignant conditions such as cancer of the lungs, breast, colon etc. The prevalence of tumours of the larynx and oral cavity has increased in both developed and developing countries. This increase underscores the need for a novel therapeutic modality that would decrease or completely terminate the proliferation of malignant cells. This review highlights various types of gene therapy procedures with respect to oral squamous cell carcinoma.

A 2-year-old girl presented with coarse, thick hairy skin all over the body, a tuft of hair in the parietal region, coarse facial features and a prominent forehead with a large tongue, hepatosplenomegaly and skeletal deformities. Mucopolysaccharides excretion spot test of the urine was positive; and an assay for glycosaminoglycans in the urine was also high, which confirmed the clinical diagnosis of Hurler syndrome. We present this rare case to discuss the possibility of the association of mental retardation with a tuft of hair in this syndrome.