The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Accurately predicting a user's rating to a service is a challenging task in the presence of malicious users that manipulate the ratings to services. Many existing service rating systems lack the ability that counter the manipulation of rating systems. This paper proposed an artificial neural network (ANN) based service rating scheme that counters the manipulation of service ratings. The scheme takes...
In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting...
Herein we consider the comparison of two neural networks: the Extreme Learning Machine (ELM) and the Fast Support Vector Classifier (FSVC, also known as RBF-M). Classification tasks are considered showing that FSVC has similar performance to ELM while having the advantage of a unique radius and of a precise result (no randomness is here involved)
A 1.82mm2 65nm neuromorphic object recognition processor is designed using a sparse feature extraction inference module (IM) and a task-driven dictionary classifier. To achieve a high throughput, the 256-neuron IM is organized in four parallel neural networks to process four image patches and generate sparse neuron spikes. The on-chip classifier is activated by sparse neuron spikes to infer the object...
This paper presents an application of cognitive networking paradigm to the problem of inter-cell interference coordination (ICIC) in Long-Term Evolution-Uplink (LTE-UL). We describe state-of-the-art, research challenges involved, and a novel random neural network (RNN) based power controller and interference management framework. The RNN based cognitive engine (CE) learns how the electromagnetic environment...
Obstructive Sleep Apnea (OSA) is traditionally diagnosed using multiple channel physiological signal. This often leads to incorrect apnea event detection and weakens the performance of OSA diagnosis. Furthermore, there is a dire need of an automatic OSA screening system in order to alleviate the burden of the clinicians and to make a portable home sleep monitoring system feasible. In this work, an...
To meet the growing demand of wireless and power efficient neural recordings systems, we demonstrate an unsupervised dictionary learning algorithm in Compressed Sensing (CS) framework which can be implemented in VLSI systems. Without prior label information of neural spikes, we extend our previous work to unsupervised learning and construct a dictionary with discriminative structures for spike sorting...
A machine learning co-processor in 0.35μm CMOS for motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm, time delayed sample based feature dimension enhancement, low-power analog processing and massive parallelism, it achieves an energy efficiency of 290 GMACs/W at a classification rate of 50 Hz. A portable external unit based...
Extreme Learning Machines (ELM) is a class of supervised learning models that have three basic steps: A random projection of the input space followed by some nonlinear operation and finally a linear output layer of weights. The basic ELM uses pseudo matrix inverse to estimate the output layer weights which usually leads to over fitting. Recent research suggested the use of L2-norm regularization to...
Nowadays, data classification is still one of the most popular fields of machine learning problems. This paper presents a new, adaptive, and easily applicable method for the solution of such problems. The method uses rules derived from the training data. The rules are processed by a rule-based inference network that is based on the classic Radial Base Function networks, with modifications in the output...
Polycystic Ovary Syndrome (PCOS) is an endocrine abnormality that occurred in female reproductive cycle. This paper designed an application to classify Polycystic Ovary Syndrome based on follicle detection using USG images. The first stage of this classification is preprocessing, which employs low pass filter, equalization histogram, binarization, and morphological processes to obtain binary follicle...
Echo state networks (ESNs) are gaining popularity as a method for recognizing patterns in time series data. ESNs are random, recurrent neural network topologies that are able to integrate temporal data over short time windows by operating on the edge of chaos. In this paper, we explore the design of a hardware ESN with bi-stable memristor-based synapses. Hybrid CMOS/memristor hardware implementations...
Automatic gender recognition is an emerging problem in computer visions. An accurate gender recognition system can be used to reduce the search space in face recognition system for about half. However, since there is no definitive features of sexual dimorphism on human face that can be applied to all kind of face shapes from any race and age, it needs more studies to optimize the recognition system...
The unsupervised learning of Self Organizing Map (SOM) is an effective computational tool in data mining exploration processes. It provides topology preserved data mapping from high-dimensional input space into low-dimensional representation such as two-dimensional map. The visualization and classification of clustered data even with good topological preservation between input and output spaces however...
Monitoring the boiling point of a diesel fuel is an important step to understand the characteristics of the diesel fuel. This study evaluated the feasibility of adaptive linear neuron (Adaline) as a predictive model to predict the boiling point of diesel fuel based on near infrared spectrum. The parameters of learning rate and training cycle that involved in the optimization process were examined...
Today in data mining research we are daily confronted with large amount of data. Most of the time, these data contain redundant and irrelevant data that it is important to extract before a learning task in order to get good accuracy. The fact that today's computers are more powerful does not solves the problems of this ever-growing data. It is therefore crucial to find techniques which allow handling...
Human activity recognition (HAR) is the basis for many real world applications concerning health care, sports and gaming industry. Different methodological perspectives have been proposed to perform HAR. One appealing methodology is to take an advantage of data that are collected from inertial sensors which are embedded in the individual's smartphone. These data contain rich amount of information...
In this work, an effort has been made to identify vocal and non-vocal regions from a given song using signal processing techniques and machine learning algorithm. Initially spectral features like mel-frequency cepstral coefficients (MFCCs) are used to develop the baseline system. Statistical values of pitch, jitter and shimmer are considered to improve performance of the system. Artificial neural...
This paper presents an attempt to solve the challenging problem of Devanagari numeral and character recognition. It uses structural and geometric features to represent the Devanagari numerals and characters. Each image is zoned in 9 blocks and 8 structural features are extracted from each block. Similarly 9 global geometric features are extracted. These 81 features are used for representing the image...
This paper addresses the classification of multispectral remote-sensing images by the neural-network approach. In particular, an experimental comparison on the performances provided by different neural models for classifying multisensor remote-sensing data is reported. Four neural classifiers are considered in the comparison: the Multilayer Perceptron, Probabilistic Neural Networks, Radial Basis Function...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.