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A bacterial or bone infection in the feet causes diabetic foot infection (DFI), which results in reddish skin in the wound and surrounding area. DFI is the most prevalent and dangerous type of diabetic mellitus. It will mainly occur in people with heart disease, renal illness, or eye disease. The clinical signs and symptoms of local inflammation are used to diagnose diabetic foot infection. In assessing diabetic foot ulcers, the infection has significant clinical implications in predicting the likelihood of amputation. In this work, a diabetic foot infection network (DFINET) is proposed to assess infection and no infection from diabetic foot ulcer images. A DFINET consists of 22 layers with a unique parallel convolution layer with ReLU, a normalization layer, and a fully connected layer with a dropout connection. Experiments have shown that the DFINET, when combined with this technique and improved image augmentation, should yield promising results in infection recognition, with an accuracy of 91.98%, and a Matthews correlation coefficient of 0.84 on binary classification. Such enhancements to existing methods shows that the suggested approach can assist medical experts in automated detection of DFI.

The main objective of this paper is to analyze the characteristics and features that affects the fluctuations of cryptocurrency prices and to develop aninteractive cryptocurrencychatbot for providing the predictive analysis of cryptocurrency prices. The chatbot is developed using IBM Watson assistant service. The predictive analytics is performed by analyzing the datasets of various cryptocurrencies and applying appropriate time series models. Time Series Forecasting is used for predicting the future values of the prices. Predictive models like ARIMA model is used for calculating the mean squared error of the fitted model. Facebook’s package prophet () which implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly and weekly seasonality are further used to predict cryptocurrency prices.

Parkinson's disease (PD) is one of the common neurodegenerative diseases. The severity of the disease increases with age. The primary reason for Parkinson's disease is the deterioration of dopamine-producing brain cells in substantia. The cardinal symptoms of PD comprise of bradykinesia, tremor and rigidness which can be detected by the gait movements. In this study, the pattern recognition technique was utilized for feature extraction and classification using ANN showed virtuous accurateness when LTP technique was used. Eight statistical features were taken for the classification. The performance parameters such as accuracy, sensitivity, specificity and F1 score achieved for the lower bound are 97.02%, 96.55%, 99.57%, 97.09 % respectively.

In a heterogeneous distributed computing environment, developing a fault tolerance mechanism is a key research issue. Most of the existing fault tolerance approaches for distributed computing envir...

Skyline query is being widely adopted by the database community in today’s technological era. In a dynamic decision-making environment, the skyline query helps in producing effective results. Data produced in a dynamic decision-making scenario is prone to be affected by incomplete data. Executing the skyline query in a partially complete environment or incomplete data involves excessive pairwise comparisons which incurs degradation in the performance. This also affects the properties of skyline query. To address this challenge, we propose MIFCM (MICE Imputed Fuzzy C Means Clustering). Multivariate Imputation by Chained Equations (MICE) deals with data which has missing values. This proposed approach is a combination of MICE imputation and FCM clustering in order to improve the efficiency of skyline queries by reducing excess pairwise comparisons and search-time complexity of Skyline query. This manuscript captures our theoretical analysis and experimental results. From the experimental analysis, it is observed that the proposed approach significantly reduces the execution time for an incomplete dataset with an optimal result set size.

Skyline query is one of the types of user preference-based query which is used to retrieve non-dominated results. This is extensively used in applications like recommender system, decision-making applications and e-commerce. Existing traditional skyline algorithms are well suited only for complete data. However, we find in the real time dataset we come across one or more missing data and this will lead to incorrect results during skyline query processing. Due to which the user may not get accurate information while they give some preferences. In this paper, we propose the Multiple Prediction based Imputation Algorithm (MPIA)for incomplete data and finally execute the skyline query which gives better results than the existing approaches like Mean, Median, K- Nearest Neighbor (KNN)and Multiple Imputation Chained Equations (MICE).

Coronary artery disease (CAD) is a heart disease which contributes to the majority of deaths. It happens when the arteries becomes contracted due to the accumulation of cholesterol. The risk factors associated with the disease can be reduced to a greater extent if they are predicted at an early time. A detailed study of research publications, existing technology and other related works has been performed to gain insight to a novel Coronary Artery Disease risk assessment scoring system. Regression analysis of the produced model using R language helps in establishing a programmed report on the prospects of the research. An analogy of possible associations with the research topic along with necessary references has been performed in the paper. The existing work related to the project has been extensively exploited by extracting all possible information as well as using it as a source of inspiration for deeper insight and understanding the work and the project.