European Journal of Molecular & Clinical Medicine

Forecasting the Stock worth Movements is one among the most popular topics in Finance True profits for sellers and customers will be enormous as a result of stock prediction. It is frequently said that prognosis is turbulent rather than stochastic, suggesting that it could be meticulously predicted. Examining each stock firm's historical development. Firms benefit from forecasting the stock market. To determine the long run values of company stocks, is associate indicator of the state of the Economy, and helps to form personal wealth. Costs of shares might have an effect on thanks to several factors such as well as enterprise news, and governmental, cultural, and environmental issues. Machine Learning Models are getting used to predict the long run stock worth values, Create the Dynamic Panel to see and compare the anticipated price with the particular price over a period of time.

The affective quality called Valence refers to the intrinsic goodness (positive valence) or badness (negative valence) of an event, object, or situation. For this purpose, a model for classification and characterization of emotions have been developed which is discussed in this paper. In this model, the images are smoothened using an Average Filter and are first identified through a Convolutional Neural Network which uses the ReLU activation function. Then, the valence is classified using a Support Vector Machine (SVC) classifier, which uses a Radial Basis Function (RBF) kernel. For this reason, the emotions are labeled according to their nature. The positive emotions are labeled 1 (inclusive of the neutral emotion) and the negative emotions are labeled as 0. The images from the FER 2013 dataset is used for Valence Recognition and is given via a RBF Kernel in a SVM, which classifies whether the emotion recognized is positive or negative. The haarcascade algorithm is implemented to detect the face. In this paper, the 7 human emotions (happiness, surprise, fear, anger, fear, disgust, sadness and neutral) have been identified and their valence recognized.

Diabetes people who also take antibiotics to combat different infections are particularly vulnerable to fungal mouth and tongue infection. The fungus prospers in the saliva of uncontrolled diabetes to high glucose levels. An efficient method for Tongue image classification using Non-Negative Matrix Factorization (NNMF) and various Support Vector Machine (SVM) kernels are presented in this study. The input tongue images are given to NNMF for feature extraction and stored in feature database. Finally, SVM kernels like linear, polynomial, quadratic and Radial Basis Function (RBF) are used for prediction. The system produces the classification accuracy of 92% by using NNMF and different SVM kernels

Malaria is an infectious disease transmitted by mosquitoes that affects humans and other animals. Malaria is responsible for the effects of fever, tiredness, vomiting and headaches. Yellow skin, convulsions, a coma, or death can lead to severe cases. Symptoms usually start 10-15 days after a mosquito is bitten. The early diagnosis is required for malaria. In this study, the automatic classification of malaria system is discussed. Initially, the input images are given to Fourier–Mellin transform for feature extraction and Support Vector Machine (SVM) classifier is used for classification. The performance of malaria system produces the classification accuracy of 92%using SVM classifier.

The Global Processing System (GPS) of Non-Subsampled Shearlet Transform (NSST) features for dermoscopic image classification with Support Vector Machine (SVM) is presented. If a skin cancer is diagnosed early in its development, i.e., when the tumour is thin, it has a good prognosis which significantly worsens as the thickness increases. The NSST is decomposed by 4 levels with 8 directions. Finally, the SVM classifier is used for classification. The proposed system produces the classification accuracy of 96 % and its sensitivity is 93.33 % and specificity 100 %.
 

Here current creation online social networks (OSNs) become more and more common and the social life of people has become more linked to these pages. They use OSNs to remain in finger with everyone else, distribute news, prepare dealings and still run their personal e-. Out of control of the OSN's evolution and the huge extent of their supporters 'individual developments, they have been attackers and impostors who take individual information, share fake news and disseminate vindictive exercises. Researchers in various fields began inspecting environmentally friendly techniques in order to perform abnormal activity and counterfeit money that is based on accounting and classification algorithms [1]. However, the use of stand-alone classification algorithms no longer yields a straightforward outcome, some of the factors that are manipulated by the account have a low influence or have no impact in the closing results. The paper proposes to use the SVM-NN as a modern algorithm to effectively identify suspected Twitter accounts and bots, to add four choices and to restrict measurements. Three laptop classification mastering algorithms were used to determine the actual or false identity of target accounts. They included the SVM, the Neural Network and our recently urbanized SVM-NN method that utilizes far less hardware but is still able to correctly identify about 98% of the money due to the training data set.

Parkinson’s disease is a dynamic neurodegenerative disorder influencing over 6 million people
worldwide. However there is no recognized test for PD for patients, particularly in the early stages. This
results in increased mortality rate. Thus detection system of Parkinson’s disease with easy steps and
feasible one to detect parkinson’s disease at the early stage is essential. The proposed system invokes
parkinson’s disease detection using voice and spiral drawing dataset. The patients voice dataset is
analyzed using RStudio with kmeans clustering and decision tree based machine learning techniques.
The patients spiral drawing is analyzed using python. From these drawings principal component analysis
(PCA) algorithm for feature extraction from the spiral drawings. From the spiral drawings : X ; Y; Z;
Pressure; Grip Angle; Timestamp; Test ID values are been extracted. The extracted values are been
matched with the trained database using machine learning technique (Support vector machine) and
results are produced. Thus our experimental results will show early detection of disease which facilitates
clinical monitoring of elderly people and increase their life span by improving their lifestyle which leads
to a peaceful life.

Human activity recognition is one among the foremost vital rising technology. Principle parts from the body parts territory are utilized for human movement acknowledgment to scale back spatial property. A multi scale delineation human action acknowledgment is done to save the segregate data before spatial property decrease. This paper could be a human action Recognition system for identification of person. It takes input a video of COVID-19 patients and searches for a match within the hold on pictures. This method is predicate d on Gabor options extraction mistreatment Gabor filter. For feature extraction the input image is matching with Gabor filter and further personal sample generation formula is employed to pick out a collection of informative and non redundant Gabor options. DNN (Deep learning Models) is used for matching the input human action image to the hold on pictures. This method is used in hospital management application for detecting the COVID-19 patient activity from surveillance cameras. By using the SVM and deep learning the human activity is recognized using matlab tool.

These days the world is very much dependent on web applications. Hence providing security to
these applications is of great importance. Information is maintained in the backend databases in
the majority of applications. Among the vulnerabilities is the Structured Query Language
Injection Attack (SQLIA). There are several applications to retrieve session/HTTP cookies
nowadays. There is quite a range of techniques used to stop these attacks.The proposed work
discusses the flaws in a few of these techniques that handle these attacks and implement an
efficient hashing technique to prevent this technique. To overcome the above-mentioned attacks,
the machine learning concept with the Support Vector Machine (SVM) algorithm was
introduced. It is used to detect and prevent SQL injection. In this technique, the SVM algorithm
will be trained with all possible malicious expressions and then generate the model. Whenever a
user gives any new query then SVM will be applied to that model to predict whether a given
query contains any malicious expressions or not. If the user invents the new technique then also
SVM can detect that malicious expression by matching with a minimum number of syntax.

Glaucoma has arisen as the one of the main sources of visual impairment. A typical technique for diagnosing glaucoma is through assessment optic nerve head by an experienced ophthalmologist. This methodology is arduous and burns-through a lot of time. Despite the fact that the analysis of this infection has not yet been discovered, the period of primary identification can preserve from the glaucoma. Subsequently, customary glaucoma screening is basic and suggested. The issue can be settled by applying machine learning techniques for glaucoma detection. We present an automated glaucoma screening framework using a pre-trained Alexnet model with SVM classifier to enhance the classification accuracy . In this study, we used three publicly available dataset as HRF, Origa and Drishti_GS1 dataset. The proposed model achieved the image classification accuracy of 91.21%. This study showed that using pre-trained CNN with SVM for glaucoma detection showed greater accuracy in automatic image classification than just CNN or SVM.