Exploring novel features and decision rules to identify cardiovascular autonomic neuropathy using a hybrid of wrapper-filter based feature selection
- Authors: Huda, Shamsul , Jelinek, Herbert , Ray, Biplob , Stranieri, Andrew , Yearwood, John
- Date: 2010
- Type: Text , Conference paper
- Relation: Paper presented at the 2010 6th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, ISSNIP 2010 p. 297-302
- Full Text:
- Reviewed:
- Description: Cardiovascular autonomic neuropathy (CAN) is one of the important causes of mortality among diabetes patients. Statistics shows that more than 22% of people with type 2 diabetes mellitus suffer from CAN and which in turn leads to cardiovascular disease (heart attack, stroke). Therefore early detection of CAN could reduce the mortality. Traditional method for detection of CAN uses Ewing's algorithm where five noninvasive cardiovascular tests are used. Often for clinician, it is difficult to collect data from for the Ewing Battery patients due to onerous test conditions. In this paper, we propose a hybrid of wrapper-filter approach to find novel features from patients' ECG records and then generate decision rules for the new features for easier detection of CAN. In the proposed feature selection, a hybrid of filter (Maximum Relevance, MR) and wrapper (Artificial Neural Net Input Gain Measurement Approximation ANNIGMA) approaches (MR-ANNIGMA) would be used. The combined heuristics in the hybrid MRANNIGMA takes the advantages of the complementary properties of the both filter and wrapper heuristics and can find significant features. The selected features set are used to generate a new set of rules for detection of CAN. Experiments on real patient records shows that proposed method finds a smaller set of features for detection of CAN than traditional method which are clinically significant and could lead to an easier way to diagnose CAN. © 2010 IEEE.
Smart RFID reader protocol for malware detection
- Authors: Ray, Biplob , Huda, Shamsul , Chowdhury, Morshed
- Date: 2011
- Type: Text , Conference proceedings
- Full Text: false
- Description: Radio frequency identification (RFID) is a remote identification technique promises to revolutionize the way a specific object use to identify in our industry. However, large scale implementation of RFID sought for protection, against Malware threat, information privacy and un-traceability, for low cost RFID tag. In this paper, we propose a framework to provide privacy for tag data and to provide protection for RFID system from malware. In the proposed framework, malware infected tag is detected by analysing individual component of the RFID tag. It uses sanitization technique for analysing individual component. Here authentication based shared unique parameters is used as a method to protect privacy. This authentication protocol will be capable of handling forward and backward security and identifying rogue reader better than existing protocols. Using this framework, the RFID system will be protected from malware and the privacy of the tag will be ensured as well. © 2011 IEEE.
Enhanced RFID mutual authentication scheme based on shared secret information
- Authors: Ray, Biplob , Chowdhury, Morshed
- Date: 2010
- Type: Text , Conference proceedings
- Full Text: false
- Description: RFID is a revolutionary remote technology which has many useful implications. Large scale implementation of RFID is seeking 100% information privacy and untraceability, for users and organizations, which is suitable for low cost RFID tag (Class1). To protect users and organization we are proposing an enhanced RFID mutual authentication scheme. In this protocol we use authentication based on shared unique parameters as a method to protect privacy. This protocol will be capable of handling forward and backward security, rouge reader better than existing protocols. In our new scheme we involved RFID reader's hardware ID in addition to other shared secret information which uses hash to protect users and industries privacy. Moreover, we used LAMED as our PRNG (Pseudorandom Number Generator) which is faster and take less computational power
- Description: E1
A new data driven long-term solar yield analysis model of photovoltaic power plants
- Authors: Ray, Biplob , Shah, Rakibuzzaman , Islam, Md Rabiul , Islam, Syed
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 136223-136233
- Full Text:
- Reviewed:
- Description: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs). © 2013 IEEE.