this paper presents a review of "How AI, cognitive science and DM are combined to develop intelligent agents", and how the paradigm first shifted from AI to Data mining and then towards combination of data mining and artificial intelligence. The paper will also provide a state-of-the-art account of the cognitive architectures. It also gives a detailed comparative study of all the architectures discussed in the paper. All the survey of data mining and cognitive architecture is done w.r.t Multi agent systems. Therefore, paper will also provide a bird eye view of MAS! ABMS.

Weather data have two classes' synoptic data and climatic data. Real time data (Synoptic data) is used in applications like forecast modelling and aviation; climatic data is recorded over certain period of time. Formerly weather was forecasted manually by observing sky conditions and current weather conditions with manual calculations. Weather conditions are chaotic so there prediction must be precise and accurate. More sophisticated techniques have been developed which are far more efficient and accurate than manual calculations. Data mining is among one of such technologies. And it has a broader scope of applicability. One of such domains is Meteorology where data mining can enhance the productivity of its analysts extensively by transforming their huge, unmanageable data into valuable information knowledge. Meteorological changes of a region can cause economic and ecological damage and can harm human lives. Accurate prediction is therefore a key factor for controlling such occurrences. Various weather events e.g. Temperature, Humidity, Wind direction, Wind Speed, and Rain etc. are being predicted using various data mining techniques .The prime objective of this paper is to review research in data mining techniques applied in the field of weather prediction. A comparative study of various data mining techniques in weather forecasting is followed by a discussion on conventional preprocessing, challenges associated and issues of model evaluation and building methods. Therefore, this paper provides a roadmap to researchers for knowledge.

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World Elderly population is rising day by day, which increases demand for healthcare and number of caregivers. Ambient assisted livings is an emerging field(AAL) aimed at making Elderly and physically challenged people's life selfsufficient, safe and independent. Due to progresses in technology our surroundings are being automated. These may include homes, hospitals, factories and transportation. Ambient Assisted Environments for elder people monitor their daily activities to detect any abnormal or abrupt behavior. Any anomaly detected can be then sent to the concerned person who can be physician or family member of the elderly. As Ambient is a diverse field having a lot of technologies and application areas. Activity monitoring can be specified to a certain specific activity e.g. Fall detection and monitoring Vital Signs. This paper specifically focuses on smart home projects of Ambient Assisted Livings for elderly people monitoring overall daily activities. Out of two Ambient Technologies aspects providing support for indoor and outdoor activities of aged people this paper is focused on indoor smart environments. The prime objective of this paper is to analyze different researches being done in Ambient Assisted Livings (AAL) used for home automation or building smart homes. A comparative study of various AAL environments is followed by a discussion on issues linked with AAL. Along with the analysis of issues and challenges associated with these, a roadmap is also provided for the researchers for knowledge acquisition bout AAL systems.