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Several interconnected brain circuits such as cerebellum, cerebral cortex, thalamus and basal ganglia process motor information in many species including mammals. Interconnection between basal ganglia and cerebellum through thalamus and cortex may influence the pathways involved in basal ganglia processing. Malfunctions in the neural circuitry of basal ganglia influenced by modifications in the dopaminergic...
Big-data bioinformatics workflows are usually complex and data-intensive. They need to analyze large volumes of data using one or more analysis tools either from existing datasets or that from the new intermediate datasets generated during the workflow execution. Traditionally, the workflows are executed by moving the data to analysis tools. With the advent of big-data in bioinformatics workflows,...
Local Field Potentials arising (LFP) from neural circuits are crucial to understand neural ensemble activity and can act as a link between molecular, cellular and circuit neuroscience. Additionally, mathematical estimations of LFPs allow the study of circuit functions and dysfunctions. In this study, we used mathematical reconstructions of LFP in rat cerebellum Crus IIa using spiking neuronal models...
Medical imaging has been identified as one of the fastest growing of all health care sectors. Despite the overwhelming benefits of computed tomography (CT) in diagnosis, there is concern over radiation induced cancer. Retaining diagnostically acceptable medical image quality with minimum radiation dose is the current need in the healthcare system. In this paper we have optimized WH-HYPR low dose image...
Deep learning has received considerable attention in recent years. In this paper, we compare the long short-term memory, a deep learning technique, with other learning techniques such as the back propagation algorithm and the more recently proposed online sequential learning algorithm in the context of time-series prediction. The effectiveness of these learning algorithms is compared using a variety...
Recently, multi-label classification has gained prime importance among the classification problems. The applications of classification problems has increased so rapidly that the need for efficient and accurate classifiers has become a vital requirement in the area of data mining. Multi-label classification problem is distinguished from the single label classification because of the capability to handle...
In traditional e-assessments as well as in personalized learning, question bank calibration plays an important role in ensuring high-quality assessment outcomes. We propose an unsupervised learning approach that uses performance metrics derived from test-taker responses for precise calibration of question banks. We show partitioning test-takers into three groups using their scores is an effective...
Designing optimal controllers continues to be challenging as systems are becoming complex and are inherently nonlinear. The principal advantage of reinforcement learning (RL) is its ability to learn from the interaction with the environment and provide an optimal control strategy. In this paper, RL is explored in the context of control of the benchmark cart-pole dynamical system with no prior knowledge...
Diabetic retinopathy has become one of the most severe complications associated with diabetic retinopathy. Early detection can go a long way to prevent total blindness in the patient. Diabetic retinopathy is characterized by lesions of which exudates and hemorrhages appear prominently. We utilize a region based CNN approach to automatically mark the exudates and hemorrhages in a fundus image. The...
In the Linked Data context, identity link is one of the most important semantic links that can be established between the datasets. It specifies that different identifiers refer to the same real world object and therefore must be linked. The process of detecting these identical instances across different data repositories is referred as instance matching. This is used to connect existing data sources...
Support Vector Machines (SVM) are considered as one of the most commonly used pattern recognition techniques for various applications. In this paper, a novel attempt is made to design and implement SVM classifier using Reconfigurable architecture on a Xilinx Virtex-5 FPGA. The performance of proposed reconfigurable system is compared with its conventional non-reconfigurable architecture and the results...
Cardiac Arrhythmia is a disease dealing with improper beating of heart. The improper condition may be fast beating or slow beating associated with heart. This paper proposes a detection or prediction scheme in the type of cardiac arrhythmia disease. It uses a clustering approach and regression methodology. The clustering approach used is DBSCAN and for regression, multiclass logistic regression is...
Stock price prediction is a challenging problem as the market is quite unpredictable. We propose a method for price prediction using Dynamic Mode Decomposition assuming stock market as a dynamic system. DMD is an equation free, data-driven, spatio-temporal algorithm which decomposes a system to modes that have predetermined temporal behaviour associated with them. These modes help us determine how...
Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies...
Coreference resolution plays a significant role in natural language processing systems. It is the method of figuring out all the noun phrases that refer back to the identical real world entity. Several researches have been done in noun phrase coreference resolution by using certain machine learning techniques. Our paper proposes a machine learning approach using support vector machines (SVM) towards...
Cancer classification is routinely done using gene expression data. With microarray technology, monitoring thousands of genes is an easy task. The reliable and precise classification of different tumour types is very important in cancer classification and drug discovery which is useful in providing better treatment. Microarray gene expression data analysis is extensively used for human cancer diagnosis...
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