With recent advances in the field of data, there are many advantages of speedy growth of internet and mobile phones in the society, and people are taking full advantage of them. On the other hand, there are a lot of fraudulent happenings everyday by stealing the personal information/credentials through spam calls. Unknowingly, we provide such confidential information to the untrusted callers. Existing applications for detecting such calls give alert as spam to all the unsaved numbers. But all calls might not be spam. To detect and identify such spam calls and telecommunication frauds, the authors developed the application for suspicious call identification using intelligent speech processing. When an incoming call is answered, the application will dynamically analyze the contents of the call in order to identify frauds. This system alerts such suspicious calls to the user by detecting the keywords from the speech by comparing the words from the pre-defined data set provided to the software by using intelligent algorithms and natural language processing.
With the tremendous technological growth, the world is shifted to adapt the different food and life style by the people that results in the improper working of the body organs. The change in the food habits leads to a major problems that we face in the current scenario is the presence of hypothyroid in the body. The likelihood of hypothyroid still ruins as a challenging issue due to the uncertainty of proper symptoms. With this background, the machine learning can be used towards health care scenarios for the prediction of disease based on the patients past history. This paper focus on predicting the existence of hypothyroid with respect to the patients’ medical parameters. The hypothyroid patient dataset is taken from the UCI Metadata repository with 24 columns and 3163 unique patient’s records is used for the experimentation of hypothyroid with the following contributions. Firstly, the hypothyroid dataset from UCI machine repository is subjected with the data processing and exploratory analysis of the dataset. Secondly, the unrefined data set is fixed with different classifier algorithm to find the presence of hypothyroid and to examine the efficiency metrics before and after feature scaling. Thirdly, the data is processed to PCA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Fourth, the data is processed to LDA with various combination of components as 5, 7 and 10 and is fixed with different classifier algorithm to examine the efficiency metrics before and after feature scaling. Experimental results show that the Kernel Support Vector Machine classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced PCA dataset. Similarly, the Logistic Regression, Kernel Support Vector Machine and Gaussian Naïve Bayes classifier is found to have the accuracy of 99.52% for all the 10, 7, 5 component reduced LDA dataset.
Metal-organic frameworks (MOFs) are an emerging class of materials with unique properties such as extensive surface area, good stability, and high porosity, which facilitate their deployment in various fields of science, including nanomedicine. Numerous strategies have been proposed for designing nanoscale MOF-polymer composites with tailored properties. Polymers can be incorporated inside and outside of the MOF pores to prepare such composites. Polymers are directly grafted to the MOF wall via covalent linkages or physical coordination with the host. Though MOFs are associated with drawbacks like unrestrained liberation, placing of biomolecules/drugs, and less resilience under various physical conditions, a set of advantageous attributes have also been noticed, such as tuning capability and pore size of undecorated MOFs. Novel strategies have been developed to improve MOFs' functioning for bio-imaging, cancer treatment, and drug delivery. For this, the introduction of polymers has proved helpful in expanding the functionalities and diversities of MOFs. Owing to the benefits like a controlled release of drugs in response to extrinsic stimuli, boosted inclination towards targeted cells, intensified MOF durability, and increased biocompatibility, MOF-polymer composites are excellent sources of helpful implementation in the biomedical field. This study provides insight into the synthesis and performance of MOF-polymer composite as a novel candidate in the biomedical sector.
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