Business and healthcare application are tuned to automatically detect and react events generated from local are remote sources. Event detection refers to an action taken to an activity. The association rule mining techniques are used to detect activities from data sets. Events are divided into 2 types' external event and internal event. External events are generated under the remote machines and deliver data across distributed systems. Internal events are delivered and derived by the system itself. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used in the event detection process. The new event discovery process is designed to support uncertain data environment. Uncertain derivation of events is performed on uncertain data values. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. Selectability filters events that are irrelevant to derivation by some rules. Selectability algorithm is applied to extract new event derivation. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. A sampling algorithm is used for efficient approximation of new event derivation. Medical decision support system is designed with event detection model. The system adopts the new rule mapping mechanism for the disease analysis. The rule base construction and maintenance operations are handled by the system. Rule probability estimation is carried out using the Apriori algorithm. The rule derivation process is optimized for domain specific model.

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The design and development of a low power wearable physiological parameter monitoring system have been developing and reporting in this paper. The system can be used to monitor physiological parameters, such as ECG signals, temperature and heartbeat. The system consists of an electronic device which is worn on the wrist and finger, by an at-risk person. Using several sensors to measure different vital signs, the person is wirelessly monitored within his own home. An epic sensor has been used to detect ECG signals. The device is battery powered for use outdoors. The device can be easily adapted to monitor athletes and infants. The low cost of the device will help to lower the cost of home monitoring of patients recovering from illness. A prototype of the device has been fabricated and extensively tested with very good results.

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A single feed quad band compact rectangular microstrip antenna for Wi-max applications has been designed and developed. This antenna is incorporated by one c-shaped slot, one L-shaped slot & two I-shaped slot structure along the length on the patch. Four resonating frequencies are obtained at 2.5 GHz with return loss -25.01 dB, 3.5 GHz with return loss -17.9 dB, 4.9 GHz with return loss -25.74 dB and 5.8 GHz with return loss -28.32 dB. The size of the antenna has been reduced by 77.3% when compared to a conventional microstrip patch without slot. An extensive analysis of the return loss, radiation pattern and efficiency of the proposed antenna has been given in this paper. The characteristics of the designed structure is simulated by High frequency structure simulator (HFSS) software and implemented using RT DUROID dielectric substrate which has ϵr = 2.2 and h = 1.575mm. The proposed antenna leads to multi frequency operation makes it suitable for Wireless Communication applications.

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In this paper, we present an overview of existing frequent item set mining algorithms. All these algorithms are described more or less on their own. Frequent item set mining is a very popular and computationally expensive task. We also explain the fundamentals of frequent item set mining. We describe today's approaches for frequent item set mining. From the broad variety of efficient algorithms that have been developed we will compare the most important ones. We will systematize the algorithms and analyse their performance based on both their run time performance and theoretical considerations. Their strengths and weaknesses are also investigated. It turns out that the behaviour of the algorithms is much more similar as to be expected.

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