STUDY OF MEDICAL MECHATRONICS

Abstract Medical mechatronics explains innovative solutions for exploiting mechatronics in the medical instruments by optimizing the available conventional instruments and also creating new innovative, intelligent and accurate instrument. This paper gives a brief introduction about sensing and actuating technologies, automation and control systems used in the medical field. The paper also discuss about the principles and methods of processing and controlling mechanism in mechatronics system. In controlling, the artificial neural networks (ANNs) and fuzzy expert systems are commonly used one. ANNs are biologically inspired computer programs designed to simulate the way in which the human brain processes information. Whereas fuzzy expert system uses predefine membership functions and fuzzy inference rules to map numeric data into linguistic variable terms and to make fuzzy reasoning work

Resistance to the larvicide temephos and altered egg and larval surfaces characterize salinity-tolerant Aedes aegypti.

Aedes aegypti, the principal global vector of arboviral diseases and previously considered to oviposit and undergo preimaginal development only in fresh water, has recently been shown to be capable of developing in coastal brackish water containing up to 15 g/L salt. We investigated surface changes in eggs and larval cuticles by atomic force and scanning electron microscopy, and larval susceptibility to two widely-used larvicides, temephos and Bacillus thuringiensis, in brackish water-adapted Ae. aegypti. Compared to freshwater forms, salinity-tolerant Ae. aegypti had rougher and less elastic egg surfaces, eggs that hatched better in brackish water, rougher larval cuticle surfaces, and larvae more resistant to the organophosphate insecticide temephos. Larval cuticle and egg surface changes in salinity-tolerant Ae. aegypti are proposed to respectively contribute to the increased temephos resistance and egg hatchability in brackish water. The findings highlight the importance of extending Aedes vector larval source reduction efforts to brackish water habitats and monitoring the efficacy of larvicides in coastal areas worldwide

Anthropogenic Factors Driving Recent Range Expansion of the Malaria Vector Anopheles stephensi

The malaria vector Anopheles stephensi is found in wide tracts of Asia and the Middle East. The discovery of its presence for the first time in the island of Sri Lanka in 2017, poses a threat of malaria resurgence in a country which had eliminated the disease in 2013. Morphological and genetic characterization showed that the efficient Indian urban vector form An. stephensi sensu stricto or type form, has recently expanded its range to Jaffna and Mannar in northern Sri Lanka that are in proximity to Tamil Nadu state in South India. Comparison of the DNA sequences of the cytochrome oxidase subunit 1 gene in An. stephensi in Jaffna and Mannar in Sri Lanka and Tamil Nadu and Puducherry states in South India showed that a haplotype that is due to a sequence change from valine to methionine in the cytochrome oxidase subunit 1 present in the Jaffna and Mannar populations has not been documented so far in Tamil Nadu/Puducherry populations. The Jaffna An. stephensi were closer to Tamil Nadu/Puducherry populations and differed significantly from the Mannar populations. The genetic findings cannot differentiate between separate arrivals of the Jaffna and Mannar An. stephensi from Tamil Nadu or a single arrival and dispersion to the two locations accompanied by micro-evolutionary changes. Anopheles stephensi was observed to undergo preimaginal development in fresh and brackish water domestic wells and over ground cement water storage tanks in the coastal urban environment of Jaffna and Mannar. Anopheles stephensi in Jaffna was resistant to the common insecticides deltamethrin, dichlorodiphenyltrichloroethane and Malathion. Its preimaginal development in wells and water tanks was susceptible to predation by the larvivorous guppy fish Poecilia reticulata. The arrival, establishment, and spread of An. stephensi in northern Sri Lanka are analyzed in relation to anthropogenic factors that favor its range expansion. The implications of the findings for global public health challenges posed by malaria and other mosquito-borne diseases are discussed

Optimal Image Encryption in Frequency Domain using Hybrid Deer Hunting with Artificial Bee Colony with Hybrid Chaotic Map

A novel image encryption method in the frequency domain is proposed in this paper by introducing an optimized Hybrid Chaotic Map (HCM). In the initial step, the Discrete Wavelet Transform (DWT) transforms the image into the frequency domain. The developed image encryption in the frequency domain is composed of various steps, such as frequency domain conversion, key generation, image encryption using optimized HCM, and image decryption. The most important step in the chaotic-based image encryption is the generation of the key which is performed using the Secure Hash Algorithm (SHA-256) cryptographic Hash algorithm. Next, the image encryption is performed by incorporating Piece-Wise Linear Chaotic Map (PWLCM) and Two Dimensional Linear Chaotic Map (2DLCM) using HCM. Performance is enhanced by the parameter optimization when hybridizing the two chaotic maps. During the parameter optimization, the main aim is to maximize the information entropy. The HCM parameter tuning is done by the hybridized optimization algorithm known as the Deer Hunting-based Artificial Bee Colony algorithm (DH-ABC). Performance of the presented method is evaluated by correlating it with various state-of-the-art models

Feature Selection of Gene Expression Data for Cancer Classification: A Review

AbstractThe DNA microarray technology has capability to determine the levels of thousands of gene simultaneously in a single experiment. Analysis of gene expression is important in many fields of biological research in order to retrieve the required information. As time progresses, the illness in general and cancer in particular have become more and more complex and complicated, in detecting, analyzing and curing. We know cancer is deadly disease. Cancer research is one of the major area of research in medical field. Predicting precisely of different tumor types is a great challenge and providing accurate prediction will have great value in providing better treatment to the patients. To achieve this, data mining algorithms are important tools and the most extensively used approach to achieve important feature of gene expression data and plays an important role for gene classification. One of major challenges is to discover how to extract useful information from huge datasets. This paper presents recent advances in the machine learning based gene expression data analysis with different feature selection algorithms.Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. But compared to the number of genes involved, available training data sets generally have a fairly small sample size for classification. These training data limitations constitute a challenge to certain classification methodologies. Feature selection techniques can be used to extract the marker genes which influence the classification accuracy effectively by eliminating the un wanted noisy and redundant genes This paper presents a review of feature selection techniques that have been employed in micro array data based cancer classification and also the predominant role of SVM for cancer classification

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Performance Analysis of Improved K-Means & K-Means in Cluster Generation

ABSTRACT: K-means is the well known and most familiar algorithm among the other partition based clustering algorithms. It typically shows spectacular results even in significantly massive information sets of Image segmentation supported associate adaptive K-means clustering algorithm is conferred. The proposed method tries to develop Kmeans algorithm to get high performance and potency. This technique proposes data formatting step in K-means algorithmic rule. additionally, it solves a model choice variety by deciding the quantity of clusters victimization datasets from image by frame size and also the definite quantity between the means that, and extra steps for convergence step in K-means algorithm are supplementary. Moreover, so as to judge the performance of the proposed technique, the results of the proposed technique, customary K-means and recently changed K-means are compared. The experimental results showed that the proposed technique provides higher output

Effect of lay-up angle on mechanical properties of composites based on rib knit jute preforms

263-266<span style="font-size: 16.0pt;font-family:Fd839219-Identity-H;mso-bidi-font-family:Fd839219-Identity-H">The mechanical properties of knitted jute reinforced composites with different stacking sequences have been studied. Flat rib knitted preforms have been produced in manual flat bed knitting machine followed by composite laminate preparation using simple hand Jay-up technique. It is observed that the mechanical properties are dependent upon the stacking sequence. <span style="font-size: 16.0pt;font-family:Fd839219-Identity-H;mso-bidi-font-family:Fd839219-Identity-H">The improvement in course-wise mechanical properties of the laminates has been observed with [0°/±45°/0°] and [0°/90°/90°/0°] lay-up sequences compared to [0°)4 lay-up sequence which shows improvement in properties in wale-wise direction. </span

Artificial Intelligence Security Model For Privacy Renitence In Big Data Analytics

The smart city uses Information and Communication Technologies (ICT) to build, run and sustain the environment, economic methods to overcome the growing problems of urbanization. Security, security, secrecy and validity have all been important factors in smart city applications, and they are also important in smart city infrastructure interfaces.Hence In this research, an Artificial Intelligence-Big Data Model (AIBM) was developed to enhance the data protection elements of information management interfaces in different smart city applications to address these concerns. A divergent evolutionary method has been implemented in AIBM to provide adequate security for the Secret Data Domain Controller for smart city applications.In addition, the differentiated iterative method has been enhanced by the choice security method based on Big Data Analytics (BDA). It improves the flexibility and dissemination of data in an information authority based mostly on their associated storage site.In addition, ability to adapt interferences approach has been implemented and developed to improve the flexibility and security of information management interfaces in different smart city applications.The reliability of the proposed platform has been demonstrated through computer analysis based on security, accuracy, speed and adaptability