Feature Extraction on Medical Image using 2D Gabor Filter

Mammography is a specific type of imaging that produces an X-ray picture of the human breast. Detection of tumors at an early stage is important step in diagnosis of the abnormalities in mammograms. In many of the cases, preprocessing process of the raw image involving of enhancement, filtering and determination of textural features have been necessary for successful implementation of this study. Raw image is applied histogram equalization method in order to enhance the image intensity. Thus, the noise of that image is eliminated using Gaussian filtering method. Gabor wavelet based algorithm such Gabor filter is used to extract the feature of that images

Low Voltage CMOS Schmitt Trigger In 0.18μm Technology

This paper presents the effect of source voltage on performance of proposed Schmitt Trigger circuit. The proposed circuit was designed based on Conventional Schmitt Trigger by manipulating the arrangement of transistors and the width-length ratio. The simulation results have been carried out based on Mentor Graphics software in term of propagation delay. The circuit layout has been designed and checked by using design rule check (DRC) and layout versus schematic (LVS) method. From these results, the proposed full swing CMOS Schmitt Trigger was able to operate at low voltage (0.8V-1.5V

Solving classification problem using ensemble binarization classifier

Binarization strategy is broadly applied in solving various multi-class classification problems. However, the classifier model learning complexity tends to increase when expanding the number of problems into several replicas. One-Versus-All (OVA) is one of the strategies which transforming the ordinal multi-class classification problems into a series of two-class classification problems. The final output from each classifier model is combined in order to produce the final prediction. This binarization strategy has been proven as superior performance in accuracy than ordinal multi-class classifier model. However, learning model complexity (eg. Random Forest-RF ensemble decision trees) tends to increase when employing a large number of trees. Even though a large number of trees might produce a decent accuracy, generating time of the learning model is significantly longer. Hence, self-tuning tree parameter is introduced to tackle this matter. In such circumstances, a number of trees in the RF classifier are defined according to the number of class problem. In this paper, the OVA with self-tuning is evaluated based on parameter initialization in the context of RF ensemble decision tree. At the same time, the performance has also been compared with two classifier models such J48 and boosting for several well-known datasets

Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity

Widespread research on activity recognition is becoming an imperative topic for improving the quality of human health. The fast development of sensing technology has become a fundamental platform for researchers to implement a system that could fulfill human needs. Due to privacy interests and low cost, wearable sensing technology is used in numerous physical activity monitoring and recognition systems. While these systems have proved to be successful, it is crucial to pay attention to the less relevant features to be classified. In such circumstances, it might happen that some features are less meaningful for describing the activity. Less complex and easy to understand, feature ranking is gaining a lot of attention in most feature dimension problems such as in bioinformatics and hyperspectral images. However, the improvement of ranking features in activity recognition has not yet been achieved. On the other hand, an evolutionary algorithm has proven its effectiveness in searching the best feature subsets. An exhaustive searching process of finding an optimal parameter value is another challenge. Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. The experiments employed real-world physical acceleration data sets: WISDM and PAMAP2. As a result, rsaDE performed better than the current methods in terms of model performance and its efficiency in the context of random forest ensemble classifiers

Monitoring daily fitness activity using accelerometer sensor fusion

The advancement of sensing technology offers a platform to monitor our daily fitness activity in real time environment situations. The introducing of the wearable sensing device provides an opportunity to monitor and analyze the amount of daily fitness activity conducted. An accelerometer sensor embedded in the wearable device is used to sense the activity vibration. However, recognition performance is significantly correlated with the sensor placement. Hence, the sense from various sensor placements is fused to enhance the recognition performance. In this paper, physical activity monitoring is proposed by integrating the acceleration signal from various sensor placements. The amount of fitness activity could be monitored and analyzed by extending the internet connection linked with the wearable device. Thus, this system could extensively deploy for everyone who are looking for having a better lifestyle

Activity recognition based on accelerometer sensor using combinational classifiers

In recent years, people nowadays easily to contact each other by using smartphone. Most of the smartphone now embedded with inertial sensors such accelerometer, gyroscope, magnetic sensors, GPS and vision sensors. Furthermore, various researchers now dealing with this kind of sensors to recognize human activities incorporate with machine learning algorithm not only in the field of medical diagnosis, forecasting, security and for better live being as well. Activity recognition using various smartphone sensors can be considered as a one of the crucial tasks that needs to be studied. In this paper, we proposed various combination classifiers models consists of J48, Multi-layer Perceptron and Logistic Regression to capture the smoothest activity with higher frequency of the result using vote algorithm. The aim of this study is to evaluate the performance of recognition the six activities using ensemble approach. Publicly accelerometer dataset obtained from Wireless Sensor Data Mining (WISDM) lab has been used in this study. The result of classification was validated using 10-fold cross validation algorithm in order to make sure all the experiments perform well

Activity recognition using one-versus-all strategy with relief-f and self-adaptive algorithm

Many researchers dealing with smartphone sensors to recognize human activities using machine learning algorithms. In this paper, we proposed One-versus-All (OVA) strategy with relief-f and self-adaptive algorithm to recognize these activities. Relief-f used to rank the features and prune insignificant features, self-adaptive algorithm selects the relevant ones, and OVA transform features into a series of two-class classification problems, and later recognized by based classifier. Experiments were carried out to study the performance of our proposed algorithm using publicly activity datasets namely Physical Activity Monitoring for Aging People. It covers eighteen activities comprising various simple and complex activities. The performance of our method is compared to One-versus-One algorithm. The results have significantly promised an improvement of activity recognition level, mainly involving very similar activities

Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model

In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. In the former category, the training example is learned and the hidden class is predicted to represent the appropriate class. The class is known, but it is hidden from the learning model. Unlike supervised, unsupervised directly build the learning model for unlabeled example. Clustering is one of the means in data mining of predicting the class based on separating the data categories from similar features. Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. Meanwhile, Kmeans clustering algorithm has also been reported has widely known for solving most unsupervised classification problems. Unlike EM, K-means performs the clustering by measuring the distance between the data centroid and the object within the same cluster. On top of that, random forest ensemble classifier model has reported successive perform in most classification and pattern recognition problems. The expanding of randomness layer in the traditional decision tree is able to increase the diversity of classification accuracy. However, the combination of clustering and classification algorithm might rarely be explored, particularly in the context of an ensemble classifier model. Furthermore, the classification using original attribute might not guarantee to achieve high accuracy. In such states, it could be possible some of the attributes might overlap or may redundant and also might incorrectly place in its particular cluster. Hence, this situation is believed in yielding of decreasing the classification accuracy. In this article, we present the exploration on the combination of the clustering based algorithm with an ensemble classification learning. EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. In our experimental analysis, ten widely used datasets from UCI Machine Learning Repository and additional two accelerometer human activity recognition datasets are utilized

Bio-inspired robotic locomotion model: Response towards food gradient changes and temperature variation

The nervous system is a complex yet efficient structure - with superior information processing capabilities that surely surpass any man-made high-performance computer. Understanding this technology and utilising it in robotic navigation applications is essential to understand its underlying mechanism. One of the approaches is using a nematode’s biological network model, as having a simple network structure while holding a complex locomotion behaviour. For instance, its ability to navigate via local concentration cue (chemotaxis) and the ability to dynamically respond towards surrounding temperature (thermotaxis). To date, the simulation of currently available models is on static environment conditions and the nematode’s movement decision is based on the deterministic non-linear response towards gradient changes. Commonly, parameters of these models were optimised based on static conditions and require adjustment if simulated within a dynamic environment. Therefore, this work proposed a new nematode’s biological locomotion model where the movement trajectory is determined by the probability of “Run” and “Turn” signals. The model is simulated within a 2D virtual environment with complex concentration gradient and variants of temperature distribution. The analysis result shows the nematode’s movement of the proposed model agreed with the finding from experimental studies. Later, the proposed model in this work will be employed to develop a biological inspired multi-sensory robotic system for navigating within a dynamic and complex environmen

Multi label classification on multi resident in smart home using classifier chains

Rapid development in smart home environment are driven by the development of computing and sensing technology, has been changing the landscape of home resident’s daily life. Among others, activity recognition has become an interesting area of exploration in the domain of smart home. Activity recognition describes the paradigm of obtaining raw sensor data as inputs and predicting a home resident’s activity accordingly consist from environmental-based sensors that are embedded into the environment. The recognized patterns are based on Activity of Daily Living (ADL). In this paper, we design a multi label classification framework to cater multi resident in smart home environment using Classifier Chains approach. Human activities, everyday are gradually becoming complex especially relating with multi resident requirement and thus complicate the inferences of activities in smart home. Hence, this paper will highlight the methodology of sensing technology involved as well as important research works done in activity recognition area specifically on multi resident complex activity recognition involving interaction activity of multi resident within the same environment. Furthermore, this paper also discussed potential directions for future research in the activity recognition