The healthcare domain is basically “data rich”, yet tragically not every one of the information are dug which is required for finding concealed examples and successful basic leadership used to find learning in database and for restorative research, especially in heart malady forecast. This article has examined forecast frameworks for heart disease utilizing more number of info attributes. In this article, we proposed an altered calculation for classification with decision trees which furnishes precise outcomes when contrasted and others calculations. The proposed work is planned to show the data mining method in disease forecast frameworks in medicinal space by utilizing avaricious way to deal with select the best attributes. Our investigation demonstrates that among various prediction models neural networks and Gini index prediction models results with most noteworthy precision for heart attack prediction. A portion of the discretization strategies like voting technique are known to deliver more precise decision trees. To improve execution in coronary illness finding, this research work examines the outcomes in the wake of applying a scope of procedures to various sorts of decision trees and accuracy and sensitivity are attained by the execution of elective decision tree methods.

A novel Gini index decision tree data mining method with neural network classifiers for prediction of heart disease

The majority of imaging techniques use symmetric and asymmetric cryptography algorithms to encrypt digital media. Most of the research works contributed in the literature focus primarily on the Advanced Encryption Standard (AES) algorithm for encryption and decryption. This paper propose an analysis for performing image encryption and decryption by hybridization of Elliptic Curve Cryptography (ECC) with Hill Cipher (HC), ECC with Advanced Encryption Standard (AES) and ElGamal with Double Playfair Cipher (DPC). This analysis is based on the following parameters: (i) Encryption and decryption time, (ii) entropy of encrypted image, (iii) loss in intensity of the decrypted image, (iv) Peak Signal to Noise Ratio (PSNR), (v) Number of Pixels Change Rate (NPCR), and (vi) Unified Average Changing Intensity (UACI). The hybrid process involves the speed and ease of implementation from symmetric algorithms, as well as improved security from asymmetric algorithms. ECC and ElGamal cryptosystems provide asymmetric key cryptography, while HC, AES, and DPC are symmetric key algorithms. ECC with AES are perfect for remote or private communications with smaller image sizes based on the amount of time needed for encryption and decryption. The metric measurement with test cases finds that ECC and HC have a good overall solution for image encryption.

https://www.mdpi.com/1424-8220/20/18/5162/pdf

Analytical Study of Hybrid Techniques for Image Encryption and Decryption.

This review paper summarizes various approaches developed in the literature for antenna sensors with an emphasis on flexible solutions. The survey helps to recognize the limitations and advantages of this technology. Furthermore, it offers an overview of the main points for the development and design of flexible antenna sensors from the selection of the materials to the framing of the antenna including the different scenario applications. With regard to wearable antenna sensors deployment, a review of the textile materials that have been employed is also presented. Several examples related to human body applications of flexible antenna sensors such as the detection of NaCl and sugar solutions, blood and bodily variables such as temperature, strain, and finger postures are also presented. Future investigation directions and research challenges are proposed.

https://www.mdpi.com/1996-1944/13/17/3781/pdf

A Review of Flexible Wearable Antenna Sensors: Design, Fabrication Methods, and Applications

System on chip (SoC) is a microchip which integrates many semiconductor devices into a single chip. The complete system that is integrated with many components and circuits has to be tested for its performance. At the same time, testing of SoC should not affect the final cost of the chip. The production cost of each and every chip can be reduced by minimizing the test time of each SoC. The testing time of each SoC can be minimized by using test scheduling techniques more efficiently and effectively. In this paper, artificial intelligence-based natural-inspired techniques such as ACO, MACO, ABC, bat and firefly algorithms are proposed to perform effective test scheduling, thereby reducing the total cost of the chip. The proposed algorithms are implemented on d695 and p22810 benchmark circuits for various values of TAM widths. The performance of the various algorithms was evaluated, and it is inferred that among the several algorithms used bat algorithm performs much better in reducing the overall testing time of SoC, and hence, the SoC cost is also reduced.

Minimization of test time in system on chip using artificial intelligence-based test scheduling techniques

The brain tumor can be created by uncontrollable increase of abnormal cells in tissue of brain and it has two kinds of tumors: one is benign and another one is malignant tumor. The benign brain tumor does not affect the adjacent normal and healthy tissue but the malignant tumor can affect the neighboring tissues of brain that can lead to the death of person. An early detection of brain tumor can be required to protect the survival of patients. Usually, the brain tumor is detected using MRI scanning method. However, the radiologists are not providing the effective tumor segmentation in MRI image due to the irregular shape of tumors and position of tumor in the brain. Accurate brain tumor segmentation is needed to locate the tumor and it is used to give the correct treatment for a patient and it provides the key to the doctor who must execute the surgery for patient. In this paper, a novel deep learning algorithm (kernel based CNN) with M-SVM is presented to segment the tumor automatically and efficiently. This presented work contains several steps that are preprocessing, feature extraction, image classification and tumor segmentation of brain. The MRI image is smoothed and enhanced by Laplacian of Gaussian filtering method (LoG) with Contrast Limited Adaptive Histrogram Equalization (CLAHE) and feature can be extracted from it based on tumor shape position, shape and surface features in brain. Consequently, the image classification is done using M-SVM depending on the selected features. From MRI image, the tumor is segmented with help of kernel based CNN method.. Experimental results of proposed method can show that this presented technique can executes brain tumor segmentation accurately reaching almost 84% in evaluation with existing algorithms.

An Enhancement of Deep Learning Algorithm for Brain Tumor Segmentation Using Kernel Based CNN with M-SVM

Falls are considered as risky for the elderly people because it may affect the health of the people. So, in the recent years many elderly fall detection methods has been developed. In the present years many fall detection method had been developed but it uses only accelerometer sensor to detect the fall. It may fail in finding in the difference between actual fall and fall like activities such as sitting fast and jumping. In the proposed approach I have suggested a fall detection algorithm to detect the fall of elderly people. Daily human activities are divided into two parts such as static position and dynamic position. With the help of tri-axis accelerometer proposed fall detection can detect four kinds of positions such as falling front, front backward, jumping and sitting fastly. Acceleration and velocity is used to determine kind of fall. Our algorithm uses accelerometer and gyroscope sensor to predict the fall correctly and reduce the false positives and false negatives and increase the accuracy. In addition to that our method is made out of low cost and it can be used in real-time.

IoT based fall detection and ambient assisted system for the elderly

The optimal usage of scarce and inadequate resources is the current need of wireless sensor networks (WSNs). Reducing power consumption and increasing WSN’s lifetime can contribute to this. Energy-efficient algorithms should be proposed for effective routing, as a lot of energy is consumed during the communication between sensor nodes. A principal practice followed to bring down energy consumption is, combining together in-network data aggregation and standardized routing. Numerous conventional data aggregation algorithms exist, and each of these have their own disadvantages like delayed delivery of packets, time constraints, or high costs. The most suitable solution can therefore be achieved via optimization alone. The current research seeks to address these challenges faced in the wireless network by using the dual-cluster heads technique based on Krill Herd Optimisation (DC-KHO) Routing algorithm. Since the existing wireless routing approaches choose a random path for data transmission, the end- to- end delay, which is directly proportional to energy consumption, is high. So, the current study chooses an optimized path by calculating its path trust value using the devised krill herd maximization algorithm. The proposed DC-KHO algorithm is energy- efficient, and invariably amplifies the network’s lifetime. The study’s results reveal the ability of the proposed method in overcoming the challenges faced in transmission time, residual energy and computational time. Further, the method increases the life span of the network by 64.58%, when a 9 V battery is used for the nodes.

Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks

Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors' (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation.

Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Multimedia applications in wireless communication have been increased in recent years. A variety of wireless access technologies is introduced for various needs. The abundant increase in mobile computing devices and different networking systems leads to the support of user's mobility on heterogeneous network. In general, the roaming users migrate between two different wireless technologies and their service must be supported by vertical handover (HO). Since the roaming users expect a rapid handover experience while switching from one wireless network to another, the handover operation must be enhanced by the networks. Various wireless technologies such as wireless LAN, Worldwide Interoperability for Microwave Access (WiMAX), and the 3G Partnership Project (3GPP) are interlaced to support many wireless services in rural, urban, and global scenarios. Moreover, quality of service (QoS) has become more significant in many applications where wireless network resources are utilized. In this paper, a handover management scheme is proposed for QoS enhancement in roaming users between WiMAX and WLAN by subscribers of networks belonging to the 3GPP standards. The proposed algorithms genetic queuing, proportionally fair queuing, and WiMAX aware load balancing are analyzed in the scheduling process during handover. The simulation is implemented using NS---2 and the experimental results are obtained for the proposed algorithms and compared with the standard scheme.

Optimization of QoS parameters using scheduling techniques in heterogeneous network

Cancer is the uncontrolled growth of abnormal cells; if not detected and treated at the early stage cancer would become fatal. Breast cancer remains the second leading cause of cancer induced deaths, despite the advances taken place in treatment methods. The incidence of breast cancer in India is on the rise that one in 22 women in India is likely to suffer from breast cancer during her lifetime. One in eight women is affected by this dangerous disease in America. Cancer always a major problem to the world however various methods are available. Some of methods for breast cancer detection are X-ray mammography, MRI and ultrasound. However, they have some limitations to detect the breast cancer but microwave imaging is a safer method as compared to the X-ray. In this Paper cancer tumour in the breast is detected by I-shaped dual C-slotted microstrip patch antenna is designed for 10 GHz. The proposed antenna has a bandwidth of 7.51 dB covering the frequency range 4.9–7.89 GHz, the return loss below − 37 dB and antenna gain of 2.45 dB. The flexible this patch antenna holds good efficiency of 87.1%.

N. Sivakumar

Papers

IoT based fall detection and ambient assisted system for the elderly

Bio-inspired dual cluster heads optimized routing algorithm for wireless sensor networks

Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Optimization of QoS parameters using scheduling techniques in heterogeneous network

Design of I-shaped dual C-slotted rectangular microstrip patch antenna (I-DCSRMPA) for breast cancer tumor detection