Anonymization Based on Improved Bucketization (AIB): A Privacy-Preserving Data Publishing Technique for Improving Data Utility in Healthcare Data

Data sharing is essential in present biomedical research. A large quantity of medical information is gathered and for different objectives of analysis and study. Because of its large collection, anonymity is essential. Thus, it is quite important to preserve privacy and prevent leakage of sensitive information of patients. Most of the Anonymization methods such as generalisation, suppression and perturbation are proposed to overcome the information leak which degrades the utility of the collected data. During data sanitization, the utility is automatically diminished. Privacy Preserving Data Publishing faces the main drawback of maintaining tradeoff between privacy and data utility. To address this issue, an efficient algorithm called Anonymization based on Improved Bucketization (AIB) is proposed, which increases the utility of published data while maintaining privacy. The Bucketization technique is used in this paper with the intervention of the clustering method. The proposed work is divided into three stages: (i) Vertical and Horizontal partitioning (ii) Assigning Sensitive index to attributes in the cluster (iii) Verifying each cluster against privacy threshold (iv) Examining for privacy breach in Quasi Identifier (QI). To increase the utility of published data, the threshold value is determined based on the distribution of elements in each attribute, and the anonymization method is applied only to the specific QI element. As a result, the data utility has been improved. Finally, the evaluation results validated the design of paper and demonstrated that our design is effective in improving data utility.

An Efficient Watermarking Based Matrix Manipulation and Optimization Based Cryptographic Method for Privacy Preservation in Biomedical Data

The biomedical processing of images is an important aspect of the modern medicine field and has an immense influence on the modern world. Automatic device assisted systems are immensely useful in order to diagnose biomedical images easily, accurately and effectively. Remote health care systems allow medical professionals and patients to work from different locations. In addition, expert advice on a patient can be received within a prescribed period of time from a specialist in a foreign country or in a remote area. Digital biomedical images must be transmitted over the network in remote healthcare systems. But the delivery of the biomedical goods entails many security challenges. Patient privacy must be protected by ensuring that images are secure from unwanted access. Furthermore, it must be effectively maintained so that nothing will affect the content of biomedical images. In certain instances, data manipulation can yield dramatic effects. A biomedical image safety method was suggested in this work. The suggested method will initially be used to construct a binary pixel encoding matrix and then to adjust matrix with the use of decimation mutation DNA watermarking principle. Afterwards to defend the sub keys couple privacy which was considered over the logical uplift utilization of tent maps and purpose. As acknowledged by chaotic (C-function) development, the security was investigated similar to transmission in addition to uncertainty. Depending on the preliminary circumstances, various numbers of random were generated intended for every map as of chaotic maps. An algorithm of Multi scale grasshopper optimization resource with correlation coefficient fitness function and PSNR was projected for choosing the optimal public key and secret key of system over random numbers. For choosing the validation process of optimization is to formulate novel model more relative stable to the conventional approach. In conclusion, the considered suggested findings were contrasted with current approaches protection that was appear to be successful extremely.

A Novel Disease Detection and Classification Method Using Improved Fusion Random Weight Support Vector Machine

Health informatics is one of the main branch of engineering which provides a solution to a variety of problems like delayed, missed or incorrect diagnoses with the help of computational techniques. With the help of technologies such as bio-computing, health informatics, the disaster impacts on both human health and biological factors can be reduced to a large extend. Using these computational technologies, the country’s economy can also get boosted up and due to increased disease-causing pathogens, which directly impact the human health system. In this research work, a different type of sugarcane disease is detected and classified because manual identification is difficult and time-consuming. So, the farmers couldn’t find a better solution, than on the whole, they go for stubble burning, which is an alarming issue both on human and environmental wellness. The burning of bagasse causes bagassois, an interstitial lung disease that affects the tissues present in the lung through the air sacs. So, this sugarcane disease detection needs to be done early to avoid various health and environmental issues. The proposed work consists of the detection of four types of sugarcane leaf disease directly from the field. The sequence of methods is capturing images with WSN nodes, pre-processing with image enhancement and noise removal (IENR), segmentation with Fuzzy membership function and clustering (FMFC), feature extraction using Gray Level Co-occurrence Matrix Vector (GLCMV) and classification using Support Vector Machine (SVM). With the help of the effective proposed method, the highest parameters like precision, accuracy, sensitivity, and specificity for sugarcane leaf disease have been obtained. Based on the successful implementation process, the accuracy stated for the four sugarcane diseases along with the execution time is given below as Smut disease (87.12, 1.01 sec), Rust disease (90.23, 1.02 sec), Grassy Shoot disease (95.34, 1.047 sec), Red Rot disease (95.51, 1.04 sec).

Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

A Novel Image Segmentation Method for Cardiac MRI Using Support Vector Machine Algorithm Based on Particle Swarm Optimization

At present, heart disease not only has a significant impact on the quality of human life but also poses a greater impact on people’s health. Therefore, it is very important to be able to diagnose heart disease as early as possible and give corresponding treatment. Heart image segmentation is the primary operation of intelligent heart disease diagnosis. The quality of segmentation directly determines the effect of intelligent diagnosis. Because the running time of image segmentation is often longer, coupled with the characteristics of cardiac MR imaging technology and the structural characteristics of the cardiac target itself, the rapid segmentation of cardiac MRI images still has challenges. Aiming at the long running time of traditional methods and low segmentation accuracy, a medical image segmentation (MIS) method based on particle swarm optimization (PSO) optimized support vector machine (SVM) is proposed, referred to as PSO-SVM. First, the current iteration number and population number in PSO are added to the control strategy of inertial weight λ to improve the performance of PSO inertial weight λ. Find the optimal penalty coefficient C and γ in the gaussian kernel function by PSO. Then use the SVM method to establish the best classification model and test the data. Compared with traditional methods, this method not only shortens the running time, but also improves the segmentation accuracy. At the same time, comparing the influence of traditional inertial weights on segmentation results, the improved method reduces the average convergence algebra and shortens the optimization time.

Signal Processing and Classification for Electroencephalography Based Motor Imagery Brain Computer Interface

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

Melanoma Skin Cancer Recognition and Classification Using Deep Hybrid Learning

Melanoma skin cancer is a common disease that develops in the melanocytes that produces melanin. In this work, a deep hybrid learning model is engaged to distinguish the skin cancer and classify them. The dataset used contains two classes of skin cancer–benign and malignant. Since the dataset is imbalanced between the number of images in malignant lesions and benign lesions, augmentation technique is used to balance it. To improve the clarity of the images, the images are then enhanced using Contrast Limited Adaptive Histogram Equalization Technique (CLAHE) technique. To detect only the affected lesion area, the lesions are segmented using the neural network based ensemble model which is the result of combining the segmentation algorithms of Fully Convolutional Network (FCN), SegNet and U-Net which produces a binary image of the skin and the lesion, where the lesion is represented with white and the skin is represented by black. These binary images are further classified using different pre-trained models like Inception ResNet V2, Inception V3, Resnet 50, Densenet and CNN. Following that fine tuning of the best performing pre-trained model is carried out to improve the performance of classification. To further improve the performance of the classification model, a method of combining deep learning (DL) and machine learning (ML) is carried out. Using this hybrid approach, the feature extraction is done using DL models and the classification is performed by Support Vector Machine (SVM). This computer aided tool will assist doctors in diagnosing the disease faster than the traditional method. There is a significant improvement of nearly 4% increase in the performance of the proposed method is presented.

Design of Self Powered Internet of Medical Things Using Robust Wolf Optimization Based PI Controller for Health Care Monitoring System

In order to gather, transmit, and develop input from the patients for monitoring their health condition through smart devices or devices which use embedded systems, such as processors and transducers and equipment for communication in the healthcare system, the Internet of Medical Things (IoMT) maintains a huge network infrastructure. These devices therefore comprise of a powerful, scalable, lightweight storage knot, which requires power and batteries to run from a practical standpoint. The above shows that the energy collection plays a significant part in the enhancement of IoMT devices’ efficiency and lifespan for its application in healthcare systems. Moreover, in view of the energy acquisition from the operational environment, energy collection is required to make the IoMT devices network more ecologically sustainable. In large solar PV generating systems, partly shading situations usually develop, causing system losses. Thus, in power-voltage curves characteristic of solar systems, the appearance of several peak levels is conceivable. These kinds of problems can be handled by using new multilayer link inverter monitoring techniques. A Maximum Point Tracking Scheme (MPPT) is being suggested for self-proposed Internet of Medical Things for the purpose of optimizing harvesting of solar power on entire PV chain with the usage of RGWO (Robust Wolf Optimization) dependent PI with PWM. The mistaken PV error might create inconsistent power supply to the 7-level H-bridge inverter linked to a grid. The modulation compensation is included in the control system in order to stabilize the grid power. The suggested technique is applied to a 7-level inverter under partial shade conditions. The multi-level modular H-bridge inverter is used for the grid-linked PV system. In addition to a DC link across all H-bridges, a short PV panel string is used for feeding each phase of n H-bridge converters which is connected in series. For pulse switching inverters, the usage of RGWO-based PI with PWM is used. The PWM is used. Then L filters used to reduce the switch harmonics found in the grid are used to link the Cascade multilevel inverter with the grid. A seven-level threephase inverter with three H-bridges allows the individual MPPT control need. The harvester is under direct sunlight and sometimes overcast circumstances realistically tested outside. The wearable IoMT sensor node uses a mean power of 20, 23 mW in a wake-up mode for one hour, and the node’s service life is 28 hours. The performance analysis is finally performed and MATLAB/SIMULINK simulation is performed.

Efficient and Secure Remote Health Management in Cloud in Vehicular Adhoc Network Environment

Vehicle Ad Hoc Networks (VANETs) is a crucial communications framework for transferring messages between any healthcare systems. The dilemma of fixing the safest efficient route is a tedious issue in VANET. Hence the secure and most reliable way will give the appropriate solution for the routing issues in the VANET. In this paper, by using the Multi-Objective Bio-inspired Heuristic Cuckoo Search Node optimization algorithm is designed to find the efficient safest route for transferring health data within a short period. After seeing the efficient route, the node can be distinguished upon the traffic and security by using the Stochastic Discriminant Random Forest Node Classifier. Then in the selected route, the nodal distance can be calculated by applying the delay-based weighted end-to-end approach for traffic analysis. Then the authentic vehicle node can be analyzed through the Trust Aware extreme Gradient Boosting Node Classification based Secured Routing (TAXGBNC-SR) Technique. The obtained information that can be stored in the cloud. It deal with the multiple number of tasks gives to the ARM micro-controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of Things technology (Iot).