Intelligent Classification Technique Research Articles

Classification of Belts Status Based on an Automatic Generator of Fuzzy Rules Base System

The automation of maintenance is a growing field and consequently, predictive maintenance is achieving more importance. The main objective is to predict a breakage before it happens. In order to reach this, it is necessary to have an intelligent classification technique that analyzes the state of the key breakage elements and evaluates whether a replacement is necessary or not. This work presents a study to classify belts according to their state of use. For training, vibration data have been collected on a test bench using new belts, belts with half use and belts near the breaking point. The processing of these vibrations allows for extracting the characteristic parameters that can be related to its state of use, and then, after the initial analysis, these values are used as inputs for training the intelligent system. In particular, the Genetic Neuro-Fuzzy (GNF) technique has been chosen and, with the proposed algorithm, more detailed Fuzzy rules are obtained. Once the algorithm has been trained, it is possible to establish a relationship between the vibration shown by the belt and its state of use. The achieved results show that a good classifier has been built.

Fine-tuning Road Classification Models: Optimization Strategies for Deep Belief Networks in Transportation Big Data

Road traffic accidents are very essential for common people, consequential an estimated 1.2 million deaths and 50 million injuries all over the world every year. In this emerging world, the road accidents are among the principal reason of fatality and injury. The concern of traffic safety has heaved immense alarms across the manageable enhancement of contemporary traffic and transportation. The analysis on road traffic accident grounds can detect the major aspects quickly, professionally and afford instructional techniques to the prevention of traffic accidents and reduction of road traffic accident, which might significantly decrease personal victim by means of road traffic accidents. Data Mining techniques are used in the process of knowledge discovery for many domains’ problems. Feature Selection plays a vital role for a large number of datasets. In this paper, the classification of road accident in transportation domain was analyzed with the assistance of the proposed Intelligent classification technique. In this proposed technique, the DBN hidden layers weights are optimized by using evolutionary Genetic algorithm. This GA is utilized to enhance the classification accuracy by applying the hidden layers of Restricted Boltzmann Machine (RBM). The comparative results show that the proposed intelligent classifier gives the improved accuracy, specificity, precision, Sensitivity, F-Measure, and reduced false positive rate.

Secure and Efficient Data Storage Operations by Using Intelligent Classification Technique and RSA Algorithm in IoT-Based Cloud Computing

In mobile cloud services, smartphones may depend on IoT-based cloud infrastructure and information storage tools to conduct technical errands, such as quest, information processing, and combined networks. In addition to traditional finding institutions, the smart IoT-cloud often upgrades the normal impromptu structure by treating mobile devices as corporate hubs, e.g., by identifying institutions. This has many benefits from the start, with several significant problems to be overcome in order to enhance the unwavering consistency of the cloud environment while Internet of things connects and improves decision support system of the entire network. In fact, similar issues apply to monitor loading, resistance, and other security risks in the cloud state. Right now, we are looking at changed arrangement procedures in MATLAB utilizing cardiovascular failure information and afterward protecting that information with the assistance of RSA calculation in mobile cloud. The calculations tried are SVM, RF, DT, NB, and KNN. In the outcome, the order strategies that have the best exactness result to test respiratory failure information will be recommended for use for enormous scope information. Instead, the collected data will be transferred to the mobile cloud for preservation using the RSA encryption algorithm.

An enhanced approach to improve the encryption of big data using intelligent classification technique

An enhanced approach to improve the encryption of big data using intelligent classification technique

An enhanced intelligent classification approach to improve the encryption of big data

Progression in cloud applications have been hindered due to critical issues such as Data security and privacy. The aggravating concern remains the ability and access of cloud operators in acquiring sensitive data. Consequently, usage and excitement of cloud computing has remained lukewarm, with users remaining skeptical of the level of confidentiality promised. Principally, the financial industry and governmental agencies have shown lackluster and inimical enthusiasm of these indications. The paper serves to analyze the aforementioned issue, and present a creative proposal into cryptography, as a means to virtually restrict cloud service operator’s access to sensitive data. The free-will of cloud operators in handling sensitive data will be undermined. Through this method, file is divided with more accuracy using an intelligent classification technique. Alternatively, a different method can be utilized to find out whether data need splitting to obtain lesser operating time and minimize storage space. The results show that the approach can resolve innumerable risks associated with cloud computing, whilst requiring sufficient computing time using a very good intelligent machine learning classification techniques. As such, novel approach has been proposed which is entitled as a model for Sensitive Encrypted Storage (SES). In this model, three proposed algorithms Convolution Neural Network with Logistic Regression, Elliptic-curve Diffie Hellman-Shifted Adaption Homomorphism Encryption and Decryption have been integrated.

Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Classification of surrounding rock is the cornerstone of tunnel design and construction. The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience. To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification, it is necessary to reduce human participation. An intelligent classification technique based on information technology and artificial intelligence could overcome these issues. In this regard, using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou–Wanzhou high-speed railway in China, an intelligent-classification surrounding-rock database is constructed in this study. Based on a machine learning algorithm, an intelligent classification model is then developed, which has an overall accuracy of 91.9%. Finally, using the core of the model, the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated, and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock. This approach provides a foundation for the dynamic design and construction (both conventional and intelligent) of tunnels.

Intelligent Classification Technique for Breast Cancer

Breast cancer is also a leading cause of cancer death in the less developed countries of the world. This is partly because a shift in lifestyles is causing an increase in incidence. Breast cancer originates from the inner lining of milk ducts/lobes either in the form of invasive or non invasive disease in general. Mammography, particularly with Computer-Aided Detection (CAD), can now produce images detailed enough for diagnostic purposes, and digital mammography allows transmission of 3-dimensionssal images over long distances. The aim for the system is to design a Computer Aided Diagnosis systematic tool for perceiving non cancerous and perilous (cancer causing) mammogram. The aim of the research is proposed to develop an image processing algorithm for an automatic detection and classification of breast lesions accurately. CAD tool helped radiologist in expanding his assurance accuracy. Support vector machine (SVM) classifier is used to discriminate the tumors into benign or malignant. Incorporate best features of the find out that has significant responsibility in achieving the perfect turnout which are then designated and associated with ANN to train and classify.

Intelligent Classification Technique for Lung Cancer

Lung Cancer is the due to irregular cells that begin off in one or the two lungs, ordinarily in the cells that line the air entries. Metastasis alludes to malignancy spreading past its site of starting point to different pieces of the body. At the point when malignancy spreads, it is a lot harder to treat effectively. Essential lung malignancy begins in the lungs, while auxiliary lung disease begins elsewhere in the body, metastasizes, and achieves the lungs. In ebb and flow therapeutic conclusion, treatment, and medical procedure, restorative imaging plays a standout amongst the most critical jobs, since imaging gadgets, for example, Computerised Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound diagnostics yield a lot of data about sicknesses and organs. In any case, radiologists need to break down and assess some restorative pictures thoroughly in a brief timeframe, which is a gigantic weight. To help the weight, PC innovation research has been utilized all the more regularly to examine medicinal pictures lately. The proposed strategy which is observed to be exact for tumor discovery, utilizes Gray Level Co-event Matrix (GLCM). The Support Vector Machine (SVM) classifier characterizes the given info stage was ordinary or unhealthy and in the event that it is ailing, further it arranges the tumor pictures into considerate (non-destructive) or dangerous (malignant).

Classification of waxy crude oil odor-profile using gas sensor array

Nowadays, waxy crude oil becomes one of the major concerns in the oil and gas industry. The waxy crude oil affects the production and transportation of the crude oil from offshore to onshore. Differentiation by image visualization in determining the type of waxy crude oil, with or without wax sometimes appear rather similar between each other. Hence, a new method needs to be used to differentiate and classify the waxy crude oil type. An electronic nose (E-Nose) is one of the devices that could detect and measure the odor data of the waxy crude oil type using gas sensor array. This paper aims to classify four types of Malaysian waxy crude oil from different fields at room temperature. There are 16,000 odor data that has been collected by using the E-Nose. Then, the measured data were normalized and analyzed using boxplot analysis. The unique odor-profile for each type of waxy crude oil sample has been extracted and classified using intelligent classification technique. The four types of waxy crude oil have been classified 100% using k-NN intelligent classification technique with zero percentage of error in this paper.

Intelligent classification of waxy crude oil odor-profile at different temperature

Crude oil is one of the basic needs required for humans to ease their life. The quality of crude oil with the lowest wax content is very important, in order to sustain the transportation and production of crude oil from offshore to onshore. Based on literature from previous studies, the appearance of wax depends on the temperature which is called Wax Appearance Temperature (WAT). Hence, there is a need to propose a new method to classify the waxy crude oil at a different temperature. The main purpose of this paper is to classify Malaysian waxy crude oil odor profile at different temperatures using intelligent classification technique. There are 28,000 data measurement of the waxy crude oil that was taken using an electronic nose (E-nose). The data readings have been normalized and analyzed using a statistical method. Then, the odor profiles were classified using K-Nearest Neighbour. The classification performance shows that the technique was able to classify the Malaysian waxy crude oil odor profile at different temperatures with 100% accuracy.

A feasibility cachaca type recognition using computer vision and pattern recognition

Brazilian rum (also known as cachaça) is the third most commonly consumed distilled alcoholic drink in the world, with approximately 2.5 billion liters produced each year. It is a traditional drink with refined features and a delicate aroma that is produced mainly in Brazil but consumed in many countries. It can be aged in various types of wood for 1–3years, which adds aroma and a distinctive flavor with different characteristics that affect the price. A research challenge is to develop a cheap automatic recognition system that inspects the finished product for the wood type and the aging time of its production. Some classical methods use chemical analysis, but this approach requires relatively expensive laboratory equipment. By contrast, the system proposed in this paper captures image signals from samples and uses an intelligent classification technique to recognize the wood type and the aging time. The classification system uses an ensemble of classifiers obtained from different wavelet decompositions. Each classifier is obtained with different wavelet transform settings. We compared the proposed approach with classical methods based on chemical features. We analyzed 105 samples that had been aged for 3years and we showed that the proposed solution could automatically recognize wood types and the aging time with an accuracy up to 100.00% and 85.71% respectively, and our method is also cheaper.

Automatic Brain MRI Slices Classification Using Hybrid Technique

This paper presents an intelligent classification technique to identify normal and abnormal slices of the magnetic resonance human brain images(MRI). The prtoposed hybrid technique consists of four subsequent stages; namely, dimensionality reduction, preprocessing, feature extraction, and classification. In the initial stages, the enhancement and removed unwanted informationare applied to provide a more appropriate image for the subsequent automated stages. In feature extraction stage, the most efficient features like statistical, and Haar wavelet features are extracted from each slice of brain MR images. In the classification stage, initially performs classification process by utilizing Fuzzy Inference System (FIS) and secondly Feed Forward Neural Network (FFNN) is used to classify the braintissue to normal or abnormal. The proposed automated system is tested on a data set of 572 MRI images using T1 horizontal transverse (axial) section of the brain. Hybrid method yields high sensitivity of 100%, specificity of 100% and overallaccuracy of 95.66% over FIS and FFNN. The classification result shows that the proposed hybrid techniques are robust and effective compared with other recently work. Keywords: Brain Tumor Classification; Fuzzy Inference System; Feed Forward Neural Network; MRI .

Comparative Study of Forces Involved in Different Styles of Handwriting

The project involves the usage of tactile sensors to study the forces applied on thumb, index and middle fingers for two different styles of handwriting cursive and non-cursive. A comparative study is made based on the finger forces by quantitative analysis using MATLAB. The result of this paper shows the non-cursive style of writing requires forces below 7N whereas the cursive style takes forces more than 7N, which might vary from person to person. An intelligent classification technique, SVM (Support Vector Machine) has been used to classify the forces between the two styles of writing. The result can be applied in choosing the appropriate surface forces for a

An incremental knowledge acquisition-based system for critical domains

In some real life situations, humans have to make decisions in critical environments. When a human analyzes a situation, (s)he has to decide whether there is a risky situation and what actions have to be performed. It is always desirable to detect these situations in advance, because the solution could be easier and the expert would have more time to make the best decision. An intelligent system may analyze the information, extract conclusions, format and order the causes leading to the severe condition, so becoming the decision-making process less dramatic. Multiple Classification Ripple Down Rules (MCRDR) are a successful intelligent classification technique which has proven its efficiency in several application domains, but it has some limitations to define complex situations. In this work, an extension to MCRDR to cover with complex domains is proposed. The validation of this methodological extension has been done through the development of a prototype for complex medical domains and this is also presented in this paper.

Brain MRI Slices Classification Using Least Squares Support Vector Machine

This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of image slices after identifying abnormal MRI volume, for tumor identification. In this research work, advanced classification techniques based on Least Squares Support Vector Machines (LS-SVM) are proposed and applied to brain image slices classification using features derived from slices. This classifier using linear as well as nonlinear Radial Basis Function (RBF) kernels are compared with other classifiers like SVM with linear and nonlinear RBF kernels, RBF classifier, Multi Layer Perceptron (MLP) classifier and K-NN classifier. From this analysis, it is observed that the proposed method using LSSVM classifier outperformed all the other classifiers tested.

SUPPORT VECTOR MACHINE BASED AUTOMATIC CLASSIFICATION OF HUMAN BRAIN USING MR IMAGE FEATURES

This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.