Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.

Music Genre Classification and Recommendation

There are various sorts to group the music. Classes are for the most part various classifications wherein music is partitioned. In this day and age as music industry develops quickly, there are various kinds of music sorts made. It is essential to classify the music into these classifications, yet it is mind boggling task. In past times this is done physically and prerequisite for programmed framework for type grouping emerges. As a rule, AI techniques are utilized to group music types and profound learning strategy is utilized to prepare the model yet in this undertaking, we will utilize neural organization strategies for the characterization.

Social and emotion dimensional organizations in the abstract semantic space: the neuropsychological evidence

An essential aspect of human cognition is supported by a rich reservoir of abstract concepts without tangible external referents (e.g., “honor”, “relationship”, “direction”). While decades of research showed that the neural organization of conceptual knowledge referring to concrete words respects domains of evolutionary salience and sensorimotor attributes, the organization principles of abstract word meanings are poorly understood. Here, we provide neuropsychological evidence for a domain (sociality) and attribute (emotion) structure in abstract word processing. Testing 34 brain-damaged patients on a word-semantic judgment task, we observed double dissociations between social and nonsocial words and a single dissociation of sparing of emotional (relative to non-emotional) words. The lesion profiles of patients with specific dissociations suggest potential neural correlates positively or negatively associated with each dimension. These results unravel a general domain-attribute architecture of word meanings and highlight the roles of the social domain and the emotional attribute in the non-object semantic space.

Forecasting Stock Price using LSTM-CNN Method

Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.

Intelligent system for leaf disease detection using capsule networks for horticulture

Horticulture crops take a crucial part of the Indian economy by creating employment, supplying raw materials to different food processing industries. Mangoes are one of the major crops in horticulture. General Infections in Mango trees are common by various climatic and fungal infections, which became a cause for reducing the quality and quantity of the mangos. The most common diseases with bacterial infection are anthracnose and Powdery Mildew. In recent years, it has been perceived that different variants of deep learning architectures are proposed for detecting and classifying the problems in the agricultural domain. The Convolutional Neural Network (CNN) based architectures have performed amazingly well for disease detection in plants but at the same time lacks rotational or spatial invariance. A relatively new neural organization called Capsule Network (CapsNet) addresses these limitations of CNN architectures. Hence, in this work, a variant of CapsNet called Multilevel CapsNet is introduced to characterize the mango leaves tainted by the anthracnose and powdery mildew diseases. The proposed architecture of this work is validated on a dataset of mango leaves collected in the natural environment. The dataset comprises both healthy and contaminated leaf pictures. The test results approved the undeniable level of exactness of the proposed framework for the characterization of mango leaf diseases with an accuracy of 98.5%. The outcomes conceive the higher-order precision of the proposed Multi-level CapsNet model when contrasted with the other classification algorithms such as Support Vector Machine (SVM) and CNNs.

Detection and Classification of Plant Diseases by Image Processing

The point of this undertaking is to configuration, carry out and assess a picture handling programming based answer for programmed recognition and grouping of plant leaf infection. Anyway contemplates show that depending on unadulterated unaided eye perception of specialists to recognize and group infections can be tedious and costly, particularly in country regions and agricultural nations. So we present quick, programmed, modest and precise picture preparing based arrangement. Arrangement is made out of four primary stages; in the main stage we make a shading change structure for the RGB leaf picture and afterward, we apply shading space change for the shading change structure. Then, in the subsequent stage, the pictures are sectioned utilizing the K-implies bunching strategy. In the third stage, we figure the surface components for the portioned contaminated items. At long last, in the fourth stage the separated provisions are gone through a pre-prepared neural organization.

Detection Of Malarial Parasite Using Microscopic RBC Images

Malaria is an infectious sickness that influences a large number of lives each year. Customary conclusion of malaria in lab requires an accomplished individual and cautious investigation to segregate sound and contaminated red platelets (RBCs). It is likewise exceptionally tedious and may deliver wrong reports because of human mistakes. The target of this paper is to show how profound learning engineering, for example, convolutional neural organization (CNN) and Resnet-50 which can be valuable continuously malaria identification successfully and precisely from input pictures and to lessen difficult work with a portable application. To this end, we assess the presentation of a custom CNN model utilizing a repetitive stochastic inclination plummet (SGD) enhancer with a programmed learning rate locater and get an exactness in characterizing sound and tainted cell pictures with a serious level of accuracy and affectability. This result of the paper will work with microscopy determination of malaria to a portable application so unwavering quality of the therapy and absence of clinical mastery can be settled.

Implementation of Hybrid Algorithm in Cluster Head using Neural Networks

A Neural Network is a data processing system consisting of large number of simple, highly interconnected processing elements. In this paper a graphical convention is utilized where hubs are orchestrated in the form of cluster. An epic methodology is presented for choice of group head by the utilization of fake neural organization to expand organization's lifetime in WSN and calculation of steering convention dependent on neural organizations (NNs). In the proposed work, a standard burned-through energy is used for the choice of bunch head and and steering convention LEACH is used. The leftover energy is utilized for making group head and for the determination of bunch head using Neural Network. In the proposed system Using LEACHNN algorithm Throughput, packet delivery ratio ,energy is improved by 20%.

Predicting Invasive Lobular Carcinoma Using Machine Learning

The critical identification and prediction of the kind of malignant development should generate an interest in illness research, to assist and manage patients. The criticality of classifying illness patients into high or low risk groups necessitates that several examination groups from the biomedical and bioinformatics fields study and study the use of artificial intelligence (AI) technologies.An approach based on strategic regression and multi-classifiers has been presented to predict breast cancer.To develop deep projections in a different environment based on facts on bosom illness. This article examines the many information mining techniques that make use of classification that may be used to Breast Cancer data to provide deeper projections. Apart from that, this inquiry forecasts the best Model-generating elite by evaluating the dataset using several classifiers. Breast malignant growth dataset was gathered from the UCI AI vault and contains 569 instances with 31 attributes. The data gathering process begins with the simple logistic regression methodology, followed by IBK, K-star, Multi-Layer Perceptron (MLP), Random Forest, Decision table, Decision Trees (DT), PART, Multi-Class Classifiers, and REP Tree. Cross approval with a 10-overlap is used, and preparation is carried out to design and test new Models. The outputs are evaluated against a variety of criteria, including accuracy, root mean square error, sensitivity, specificity, F-Measure, ROC Curve Area, and Kappa measurement, as well as the time required to construct the model. The analysis of the results reveals that, of all the classifiers, Simple Logistic Regression produces the deepest predictions and obtains the best model that produces high and precise results, followed by other techniques. IBK: Nearest Neighbor Classifier, K-Star: Example-Based Classifier, and MLP-Neural Organization Different methods have a lower degree of accuracy when examined using the Logistic relapse methodology.