European Journal of Molecular & Clinical Medicine

Diabetic retinopathy is an eye-related neurological disorder, the diabetic patient eye damaged by blood vessel in the retina area of the eye. Computational methodology is a proper way for detecting and predicting the diabetic retinopathy disease. The aim is to identify and detect the Diabetic Retinopathy, so this present work focusses on detection of Diabetic Retinopathy. This work proposed the novel WMD-MSVM -Weighted Mahalanobis Distance based Multiclass Support Vector Machine oriented; upon Diabetic Retinopathy diagnosis system for the purpose of feature selection, also ROI extraction method being utilized to fetch features from Diabetic Retinopathy images. From the results, it is clear that the performance of WMD-MSVM on instance selected training dataset yields improved detection accuracy compared with the performance of WMD-MSVM on full-training-dataset. There is an improvement of around 1% of detection accuracy in case instance selected dataset. This proposed work is benefit for diabetic patients to gain the proper treatment by physicians at an early stage for Diabetic Retinopathy. This computational approach to detect   the diabetic which results the best solutions for ophthalmology. The diabetic image analysis and machine learning approach considered as a challenging research area that aims to provide a computational approach to assist in the early diagnosis and detection of Diabetic Retinopathy problems.

Adhesive dentistry has undergone great progress in the last decades. There have been changes in chemistry, mechanism, number of bottles, application technique, and clinical effectiveness.  The first, second and third generations are classified under historical title while; etch&rinse, self-etch and multi-mode adhesives are classified under the title of current clinical practice procedures strategies. The preferred properties of adhesive systems are biocompatibility with dental tissues, improved bond strength to dental tissues, and better resistance to chewing stresses. In recent years, the success rate of restorative treatment of teeth that have suffered from caries or loss of supporting tissue has increased considerably due to the developments in dentin donding systems.

Background: The impaction rate is much higher for third molars as compared to other teeth. While impacted third molars may remain asymptomatic for an indefinite period, it can cause various delinquents, such as pericoronitis, swelling with pain, distal caries, bone loss, root resorption of adjacent teeth, odontogenic cysts, and tumors. That’s why the most frequent surgical procedure in dentistry is third molar tooth extraction.
Objective: The aim is to propose a new classification for impacted mandibular third molars on CBCT images which cover all aspects of the anatomical situation
Methodology: A total of 143 images of molar teeth was collected and classified according to a relationship with the IAN canal and molar tooth. For statistical calculation, SPSS software was used.
Results: Classes 0-7 were planned and in which classes 1-6 were sectioned into two subtypes (subtypes A-B). The distribution of the classes presented an occurrence of buccal or apical course of the mandibular canal tailed by lingual position and inter-radicular one. Results emphasized that a close relationship of molar roots with the lingual side of IAN was more in female versus male other than it no anatomic differences occur in terms of IAN relationships between males and females. Younger patients displayed an increased rate of direct contact with a reduced calibre of the canal and/or without corticalization. This increases the chances of IAN damage, especially in the young woman (age range 25-30 years) with a lingual course of the mandibular canal.
Conclusion: The use of this classification is appreciated to get an equal definition of the impacted tooth on CBCT images worldwide.

Inductive learning was found to depend on acceptance. The basis of induction research is the
decision tree method . This method uses inductive methods to correctly classify objects based on the quality they
provide. These calculations are very useful for characterizing articles and are often used in the main frame. When
any new quality is added, the ID3 decision tree learning calculation is used to see if there is any progress in the
selection principle currently granted to PUCC (Pollution Treatment Certificate). Three studies were conducted on
the PUCC issuance, each of which added a new feature to the data set, thereby providing decision rules.

This article raises the question of how complex sentences are presented in university and school textbooks, what are the basics and principles of their classification, what types of connections exist between the components of complex sentences, are there ways to bridge the gap between school and university teaching. It also talks about the grammatical tradition characteristic of compound sentences of open and closed structures

Chronic Kidney disease (CKD) is a most predominant public health concern with increasing occurrence. CKD consists of an extensive variety of path physiological processes which will be experimental along with irregular function of kidneys and progressive decrease in Glomerular Filtration Rate (GFR).In CKD prediction various data mining methods play major important role and discovering the association among effective features in this stare canister lend a hand to detect or slow progression of this CKD disease. The information is serene from the patients’ medical records. The major intention of this effort is introducing a Hybrid Filter Wrapper Embedded (HFWE) based Feature Selection (FS) to select optimal subset of features from CKD dataset. This HFWE-FS algorithm combines the procedure of filter, wrapper and embedded algorithm. Filter algorithm is executed based on the three major functions: Relief, One- R, Gain Ratio (GR) and Gini Index (GI). Wrapper algorithm is accomplished placed on the Improved Bat Algorithm (IBA) to choose analytical Attributes from the CKD dataset. Embedded algorithm is accomplished placed on the Support Vector Machine-t-statistics (SVM-t) to choose analytical attributes. The results of all feature selection algorithms are combined and named as HFWE- FS algorithm.

SCA is a legacy community of diseases of red blood cells. Persons with sickle cells in their red blood cells have abnormal protein. Sickle cell anemia is a red cell condition that is inherited and does not produce enough rotary cells in the body to hold oxygen. It may cause severe pain, anaemia and other symptoms. It is dangerous. The early diagnosis is required for sickle cell anemia. In this study, the integration of multi-modal features for sickle cell anemia identification using multilayer perceptron of SCA system is discussed. Initially, the input images are given to multimodel feature is used for feature extraction and Multilayer Perceptron (MP) classifier is used for classification. The performance of SCA system produces the classification accuracy of 95%using MP classifier.

Most of the women are affected by BC (Breast Cancer) which is one of the dreadful diseases in the entire world and considered as subsequent threatening reason of cancer death in women. The likelihood of death can be significantly reduced by means of early detection and prevention. Hybrid Bayesian frameworks were utilized previously for breast cancer prediction and handling missing values in patient’s characterization. WCP (Weight Canonical Polyadic) algorithms manage continuous missing values in the data by using least squares recursively. A main bottleneck in using WCP is the unfolding of multiple relationships of discovered modes (N). The complexity increases when the value of N is large. This paper uses imputations in attribute dependencies for enhancing BC detections. This work divides the dataset into discrete and continuous subsets where discrete fields are assigned values using BN (Bayesian Networks) followed by Tensor factorization on an integrated dataset using MFWCP (Mode Fuzzy Weight based Canonical Polyadic). The new dataset is created from full/missing value subsets for assigning values to fields with missing values. MFWCP operations result in operations where N value of WCP is greater than three. This third order is reduced to first order WCP by applying Khatri-Rao product. DT (Decision Trees), KNN (K-Nearest Neighbors) and ANFIS (Adaptive Neuro Fuzzy Inference System) classifiers are combined to classify BC and the proposed hybrid method is evaluated using defined performance measures enhanced imputation accuracy.

Lung cancer is an early lung cancer. The world's greatest cause of death is lung cancer. Lung cancer typically does not cause signs or symptoms in its early stages. In order to reduce the mortality rate early detection of lung cancer is required. Computed Tomography (CT) lung images classification for nodule detection is discussed in this study. At first energy features are used for feature extraction. Then maximum likelihood classifier is used for classification. The experimental results show the performance of pulmonary nodules classification in terms of accuracy.

The cancer starts in the lungs is known as lung cancer. The leading world cause of death is lung cancer. In its early stages, lung cancer does not usually cause signs and symptoms. The early diagnosis of lung cancer is required to reduce the mortality rate. A parallel algorithm for pulmonary nodule classification with the help of Bayes theorem is discussed in this study. At first parallel algorithms like task, pipeline and data parallel for feature extraction. Then Bayes theorem is used for classification. The experimental results show the performance of pulmonary nodules classification in terms of accuracy using parallel algorithms and Bayes classification.

Dental implants are commonly used in situations for replacement of natural teeth. Despite many advances, techniques, and implant-design, implant failure is a significantconcern for the patient and dentist. The dental implants are designed that best suits the various types of bone. Different etiology for the implant failure and their contributing factors has been discussed in this review article. The purpose of this concise review is to discuss the etiology of implant failure by highlighting the various classification put forth by different authors

In this paper we have applied various classification algorithm to demonstrate better classifier to produce a hybrid selection of classifier. It is enlarged with weighted balloting on the idea of Out_Of_Bag blunders charge of man or woman decision bushes. As pre work, we first done evaluation of Random Forest the usage of five one-of-a-kind break up procedures; a unmarried split quantity is used at a time for whole forest. In this paper, we to start with proposed an superior random Forest that utilizes polluting affect optimization strategies just like the bushes in random forests. If there have to be an occurrence of accuracy development, inquire approximately is finished using exceptional belongings assessment procedures and consolidate capacities. A pass breed selection tree model alongside weighted balloting is proposed which recovers the accuracy. Development in getting to know time principally issues on diminishing range of base choice bushes in Random Forest with the aim that learning and for this reason, class is quicker. The methodologies proposed in the bearing of this direction are separate parcels of schooling datasets to get acquainted with the bottom choice bushes, and ranking of training bootstrap samples primarily based on first rate range. Both these methodologies are prompting efficient gaining knowledge of Random Forest classifier.

The growth of irregular cells within the brain region with the prearrangement of tissues is characterized as a Brain Tumor that leads death to people. Comparing to other categories of cancers, a brain tumor is the most deadly disease that has to be detected and treated in the previous stage. Due to cells' complex formation, the tumor detection process is complicated with simple image processing methodologies. Moreover, for providing proper and efficient treatment to the patients, an exact cancer segmentation and classification technique must process the input of brain images as Magnetic Resonance Imaging (MRI) scans. Based on that, this paper develops a novel approach called Soft Computing based Brain Tumor Detection and Classification (SC-BTDC) with the obtained MRI. In the present scenario of tumor detection from image processing, soft computing techniques play a significant role. Hence, it is adopted in this work. The method contains phases such as pre-processing, Fuzzy c-Means clustering-based segmentation, feature extraction, and image classification. The median pre-processing filter and edge detection methods are incorporated for noise removal and clearly define the image in the stage of the median pre-processing filter. Further, FCM based clustering is performed for the image segmentation process, following, factors of Gray Level Co-Occurrence Matrix (GLCM) found feature extraction is established. The final phase includes the classification process using the soft computing technique called Artificial Neural Network (ANN) classification. The proposed system acquires a higher accuracy rate and is compared with various existing algorithms for proving the efficiency and minimum loss of the proposed algorithm.

Ethnicity is a complex phenomenon. It is a highly controversial, disputable, problematic term that does not have any fixed meaning and is used as a less emotive term for a race. Ethnicity is a social construct like race, but the main difference is that race refers to phenotypical differences, whereas ethnicity refers to cultural differences. Ethnicity helps an individual to identify with a larger group, and one’s ethnicity may identify an individual. Too many writers like Max Weber, Michael Brown, Wsevolod Isajiw, etc. have discussed many features to define 'ethnicity.' To explain and outline an apparent meaning for ethnicity, authors like Fredrik Barth, Abner Cohen, Michael Hechter, etc. have postulated different approaches such as primordialism, instrumentalism, materialism and constructivism. Robert E. Park has contributed to the theory of ethnicity by focusing on assimilation theory. Authors like Omi and Vinant have openly opposed this theory and considered the immigrant as the basis of ethnicity. The chief motive of this paper is to discuss the shifting meanings of ethnicity and to depict the purpose behind the divisions based on ethnicity.

With increase in population the need for food is on rise, in such circumstances, plant diseases prove to be a major threat to agricultural produce and result in disastrous consequences for farmers. Early detection of plant disease can help in ensuring food security and controlling financial losses. The images of diseased plants can be used to identify the diseases. Classification abilities of Convolutional Neural Networks are used to obtain reliable output. Google’s pretrained model ‘Inception v3’ is used. The Inception v3 model is trained over a dataset of diseased plants obtained from ‘Plant Village Dataset’. The developed detection approach is evaluated on measures of F1 score, precision and recall.

Data mining has become a usually utilized strategy for the examination of hierarchical information, for motivations behind summing up information in valuable manners and recognizing non-insignificant examples and connections in the information. The paper presents consequences of exploration on impact of information discretization on proficiency of Naive Bayes classifier. The investigation has been carried on datasets established on writings of two male and two female writers utilizing the WEKA Data mining programming system. The paired grouping was performed independently for both datasets for wide scope of boundaries of discretization measure so as to examine reliance between methods of discretization and nature of arrangement utilizing Naive Bayes technique. The mathematical aftereffects of tests have been thought about and examined and a few perceptions and ends planned

Identification of disease at a beginning stage is very essential for higher treatment. It’s a awfully complicated task for medical researchers to predict the illness within the early stages because of delicate symptoms. Typically the symptoms turn out to be evident once it's too late. to beat this issue, this project aims to boost disease designation victimization machine learning approaches. The most objective of this analysis is to use categorization techniques to spot the liver patients from healthy people. This project conjointly aims to match the categorization techniques supported their presentation factors. To serve the medical community for the designation of disease between patients, a graphical computer interface is urbanized victimization python (Node RED). The GUI will be promptly used by doctors and medical practitioners as a screening tool for the disease.

Association rules consist of the discovery of association between mining transaction items. This is one of the most important information mining jobs. It has been integrated into many commercial data mining software and has a wide variety of applications on a number of domains. So, computing the prediction rules in top rank data set is very difficult task. Finding the pattern in large data set require memory computational power high rate of I/O. and it is possible only on high computational machine. In this paper, selection of parameter which is used to compute is chosen based on minimum support and minimum confidence value. In this paper proposed a new algorithm which generates the association rule for the input parameters to finding the pattern in large data set. The algorithm starts searching the rules. As soon as a rule is found, it is added to the list of order rules list by support. The list is used so far to maintain top N rules found. Once valid rules are found, the minimum support for the internal minsup variable list is raised to support the rule. When the Minsup value is raised, the search space is robbed while searching for more rules. Then, every time a valid rule is found, the list is inserted into the list, the lists that are not listed in the list are excluded from the list and the minsup is raised for the price of the least fun rules in the list. Result shows that new method is efficient technique to mine data set from standard data with minimum configuration system.

Cardiac arrhythmia can be identified using abnormal electrical activity of heart, this is a great menace to humans. In order to diagnose cardiac problems ECG signal is widely used. When the background noise is rejected from the ECG signal we obtain a QRS component. This QRS component consists of high frequency and high energy waves that are very easy to detect and study. Once QRS component is obtained, it is further spited into various classes that can aid in diagnosing the abnormalities. Previously extracted features are compared to find the heart abnormalities. In this paper Feed-Forward neural network is selected and data base are used to store and analyze the data.

-Plants are very necessary for the earth and for all living organisms. Plants maintain the atmosphere. Plant illness, an impairment of the traditional state of a plant that interrupts or modifies its very important functions. All species of plants, wild and cultivated alike, are subject to illness. These diseases occur totally on leaves, but some might also occur on stems and fruits. Leaf diseases are the foremost common diseases of most plants. Plant pathology is the science study of pathogens and environmental circumstances causing illnesses in crops. Organisms causing transmissible disease include fungi, oomycetes, bacteria, viruses, viroids, etc. The latest technique involves automated classification of diseases from plant leaf images neural networks persecution approach called hunting enhancement of microorganisms primarily focused on executing Neural system relies on planar basic principle. Throughout this article, classic neural network algorithms are used to detect and classify the areas infected with multiple illnesses on the plant leaves in order to increase the velocity and precision of the network. The region's increasing formula will improve the network's potency by searching and grouping seed points with prevalent feature extraction method characteristics. The scheduled methodology achieves greater precision in disease detection and classification.

The traditional system for detecting the infection has been the manual process of diagnosing the stained slides under a microscope. This manual process might consume more time for producing the results and the availability of medical experts is not always assured. Considering this as the primary concern we proposed a strategy which limits the human error while recognizing the presence of malarial parasite in the blood sample by using Image Processing. Hence by automating the diagnosis process, results can be acquired relatively quicker and more accuracy can be expected. The technologies and techniques to patently extract the required features and efficiently classify the infected samples are surveyed. This paper presents a survey of various approaches to automate the detection and classification of infected and uninfected cells.

Abstract. More often, the injuries happenas a result of direct trauma, and are rarely
caused by non-direct actiontraumatic force. The definition of tarano-calcaneus corner and
higher index (rentgenological) has great influence on diagnostics and on curing calcaneus
bones in children.On moderate displacement, closed reposition with the skeleton stretching
or compression apparatus distinction osteosinthesis is recommended.On crude
displacement, it is recommended to perform operative treatment and replace defects of
bones bytissues free auto-transplantation

Brain-computer interface (BCI) decoding connects the human nervous world to the external world.
People's brain signals to commands that computer devices can detect. In-depth study the performance
of brain-computer interface systems has recently increased. In this article, we will systematically
Investigate brain signal types for BCI and explore relevant in-depth study concepts for brain signal
analysis. In this study, we have a comparison of different traditional classification algorithms new
methods of in-depth study. We explore two different types Deep learning methods, i.e., traditional
neural networks Architecture with Long Short term Memory and Repetitive Neural Networks. We
check the classification Accuracy of Recent 5-Class Study-State Visual Evoked Opportunities dataset.
The results demonstrate in-depth expertise learning methods compared to traditional taxonomy
Algorithms.

Automatic plant leaf curl detection is an important step towards the development of Computer-aided crop damage analysis systems. It helps in analyzing the health condition of the plants through leaf images. Image processing techniques are recently being used to analyze the condition of the leaf and identify the disease that inflicted the crop. Leaf curl disease can be identified by analyzing the edges of the leaf. This paper presents a procedure to identify the curl disease occurring in plant leaves using active contour, Fourier feature descriptor, and deep learning. Active contour is used to identify the shape of the leaf. The edge contour of the leaf is then given to the Fourier feature descriptor. The feature extracted using the Fourier descriptor is invariant to the angle and size of the leaf. The same feature vector is produced in any given angle and size of the leaf in the image. The features are trained using 1D CNN. The model can then be used to classify new images and automatically identify the leaf have curl disease or not. The experimental results prove that the proposed algorithm produces good results in identifying the leaf curl disease.