This paper presents a novel method for pattern recognition problem in terms of linear regression. Normally, patterns from a single-object class lie on a linear subspace. Using this concept, we develop a linear model representing a probe image as a linear combination of class-specific galleries. Linear Regression Classification (LRC) algorithm for pattern recognition belongs to the category of nearest subspace classification. This algorithm is extensively evaluated on several standard digit and English character databases and our own Tamil character database. A comparative study with different databases and methods clearly reflects the efficiency of LRC approach for pattern recognition.
Unstructured data are irregular information with no predefined data model. Streaming data which constantly arrives over time is unstructured, and classifying these data is a tedious task as they lack class labels and get accumulated over time. As the data keeps growing, it becomes difficult to train and create a model from scratch each time. Incremental learning, a self-adaptive algorithm uses the previously learned model information, then learns and accommodates new information from the newly arrived data providing a new model, which avoids the retraining. The incrementally learned knowledge helps to classify the unstructured data. In this paper, we propose a framework CUIL (Classification of Unstructured data using Incremental Learning) which clusters the metadata, assigns a label for each cluster and then creates a model using Extreme Learning Machine (ELM), a feed-forward neural network, incrementally for each batch of data arrived. The proposed framework trains the batches separately, reducing the memory resources, training time significantly and is tested with metadata created for the standard image datasets like MNIST, STL-10, CIFAR-10, Caltech101, and Caltech256. Based on the tabulated results, our proposed work proves to show greater accuracy and efficiency.
An intelligent transport system (ITS) is fully valuable only if it can dynamically and aptly integrate all the latest cutting-edge technologies. An ITS focuses on providing services like promptly offering real-time road traffic information to interested parties, finding ways to reduce the average waiting time and offer secure and reliable services for commuters using past statistics. Short-term traffic prediction is one such area in which the research community has focused in the past decade. Existing models developed for prediction has scope to improve in terms of accuracy and training time. There is a necessity to develop a best-performing model that is computationally affluent to train with the optimal hyperparameter configuration as input which leads to improved performance. This article proposes a model that captures the traffic flow trend present in the past data to predict the flow for a future time interval. This model is an amalgamation of seasonal global trend (SGT) model and long short-term memory (LSTM) model with attention mechanism. A novel hyperparameter tuning algorithm is also proposed which is based on multi-armed bandit strategy with context, incorporating the right trade-off between exploitation and exploration of the hyperparameter space using successive halving. Experimental results conducted proves that our forecast model in combination with the proposed hyperparameter tuning
Nowadays digital libraries have become the source of information, sharing across the globe in the fields of education, research and knowledge. The full usage of digital libraries will be realized only when people can have access to the material from any location. The advantage of multimedia is that people of all ages can understand more clearly by seeing or hearing rather than reading. Considering the exponential growth in various technologies, developing a multimedia digital library in wireless is not an complicated task. Grid computing enables the virtualization data resources, process network bandwidth and storage capacities to create a single system image granting the user a seamless access to vast IT capabilities. By adopting peer-to-peer overlay networks, which are taking a central position in information systems, the storage space problem can be solved and by using grid computing the security can be maintained. In this article, we propose a framework for wireless multimedia digital library, built using Grid and P2P technology. Using this proposed framework, the digital data is stored in a cluster built of commodity components and users can access those data from anywhere, anytime securely. Advantages of this framework are: (i) existing capital investments are used for storing the multimedia files, (ii) increased access to data, (iii) balancing workloads among different systems connected in pGrid nodes, (iv) authenticated and secured transfer of files. Benchmarks used to test this framework are: (i) file size vs. download time, (ii) simultaneous connections, (iii) band-width utilization, (iv) security, (v) scalability, and (vi) robustness.
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