With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm’s execution time.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.

Breast-Cancer (BC) is a life intimidating illness among the women society and early revealing may assist to afford suitable treatment to reduce risk factor. Digital-Mammogram (DM) is an accepted practice to record and inspect BC. The work presented here implements a Hybrid Image-Processing-Tool by combining Tsallis-Thresholding (TT) and Level-Set-Segmentation (LSS). This tool helps to mine the apprehensive division of DM. Primarily, Jaya-Algorithm based TT using three-stage thresholding is executed to enhance the DM and later, the LSS is considered to mine the infected section. The mined region is then assessed with the GLCM to know the harshness of infection by investigating its texture-features. The investigational outcome of this paper authenticates the superiority of proposed tool in extracting the breast melanoma from the chosen DM dataset.

Condition of brain can be examined using the brain-signals and brain-images. Signal based evaluation is simple and offers essential information compared with the image based methods. This paper proposes an approach to evaluate the benchmark EEG signals. The implemented approach initially implements an amplitude based assessment to compute the peak-to-peak voltage value of the EEG signal. Later, it implements time-frequency conversation procedure to transfer the signal into image based on the wavelet transform. Further, the S-transform approach is considered to extract the essential signal features for the classifier system. Firefly-Algorithm (FA) based approach is also considered to choose leading signal features considered to train and test the classifier unit. In this work, classifiers, such as Support-Vector-Machine (SVM), Random-Forest (RF) and K-Nearest Neighbor (KNN) are implemented and the result of this work offered an average accuracy of 80.39%. The works confirms that, proposed procedure offers better result on the chosen EEG signals.

In this paper, a hybrid approach is proposed for detecting and classifying the vibration signal of induction motor. The proposed hybrid technique is the combination of S-transformation algorithm and adaptive neuro fuzzy inference system (ANFIS) method. Here, the proposed hybrid method contains two processes, such as, fault detection and classification process. Initially, the pre-processing is applied in the electric motor vibration signal. In the fault detection process, significant features from vibration signals are extracted through the S-transformation algorithm. Consequently, the ANFIS classification technique is employed to classify the signal into the faulty or the normal. The proposed hybrid technique is implemented in MATLAB working platform. The performance of the proposed hybrid technique is evaluated with five types of faulty vibration signals. The performance of the proposed hybrid method is compared with the existing method such as S-transform-RBFNN and S-transform-FFBNN. Analyze these methods with the help of statistical measures such as, accuracy, sensitivity and specificity value.

Extensive amount of detection and analysis of healthy working circumstance of Induction Motors (IM) are proposed and implemented in literature. This paper considers, heuristic approach assisted adaptive filter is recommended to detect and analyze the vibration signal of IM. In this work, a digital Infinite Impulse Response (IIR) filter is designed using recent heuristic algorithm known as the Firefly Algorithm (FA). During this study, a model squirrel-cage induction motor running at various load conditions, such as 25%, 50%, 75% and 100% are considered and the variation of the vibration level with respect to the variation in load is analyzed using the proposed procedure. The performance of the FA based IIR filter (FA-IIRF) is then validated against the IIR filter designed using PSO and BA based approaches. This experimental study confirms the capability of FA-IIRF in detecting the common induction motor faults like, vibration due to weak bearing and vibration due to the stator winding. Finally, the statistical significance of the FA-IIRF is confirmed using the Wilcoxon's rank-sum test. This test also proves that, FA based approach is better than PSO and BA based approach.