Asif Salekin

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The Microsoft Kinect sensor has brought a new era of Natural User Interface (NUI) based gaming and the associated SDK has provided access to its powerful sensors, which can be utilized in many ways, especially in research purposes. We have already seen its use in robotics, developing assistive technologies, and augmented reality, aside from gaming.(More)
Recently, there has been an increased use of wireless sensor networks and embedded systems in the medical sector. Healthcare providers are now attempting to use these devices to monitor patients in a more accurate and automated way. This would permit healthcare providers to have up-todate patient information without physical interaction, allowing for more(More)
DAVE is a comprehensive set of event detection techniques to monitor and detect 5 important verbal agitations: asking for help, verbal sexual advances, questions, cursing, and talking with repetitive sentences. The novelty of DAVE includes combining acoustic signal processing with three different text mining paradigms to detect verbal events (asking for(More)
In this paper we have proposed a novel method to detect the defects in woven fabric based on the abrupt changes in the intensity of fabric image due to the defects and have constructed a classification model to properly identify the defects. We have also improved an existing method based on histogram processing for the classifier. In classification model we(More)
In this paper, we have considered a real life scenario where data is available in blocks over the period of time. We have developed a dynamic cluster based ensemble of classifiers for the problem. We have applied clustering algorithm on the block of data available at that time and have trained a neural network for each of the clusters. The performance of(More)
Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the(More)
Diaries are used to record aspects of lives --- activities, events, experiences, feelings, thoughts, and physiological measures. Smart diaries can reduce the user's burden by automatically registering some of these aspects. Existing systems have two weaknesses: (a) they are not extensible, and (b) their design is not theory-driven. We introduce LifeMaps, a(More)
Recognizing various meaningful patterns from stock market time series data is getting tremendous attention among researcher during the recent years. Much work has been devoted to pattern discovery from stock market time series data using template based approaches and rule based approaches but not much has attempted to combine the power of any of these(More)
For last few years many research have been taken place to recognize various meaningful patterns from time series data. These researches are based on recognizing basic time series patterns. Most of these works used template based, rule based and neural network based techniques to recognize basic patterns. But in time series there exist many composite(More)
Many elderly who are suffering from dementia are also suffering from agitation. While most assisted living facilities and home health care situations rely upon caregivers to monitor and record agitation of their patients, the accuracy is limited because the caregiver must be present during the agitation and must record the event properly. Accurate 24-7 data(More)