The Internet of Things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators and connectivity which enables these things to connect and exchange data. The number of IoT devices has increased 31% year-over-year to 8.4 billion in 2017 and it is estimated that there will be 30 billion devices by 2020. Water pollution is a major environmental problem in India. The largest source of water pollution in India is untreated sewage. Other sources of pollution include agricultural runoff and unregulated small scale industry that results in polluting, most of the rivers, lakes and surface water in India. In this paper, An IoT Based Smart Water Quality Monitoring System using Cloud and Deep Learningis proposedto monitor the quality of the water in water-bodies. In conventional systems, the monitoring process involves the manual collection of sample water from various regions, followed by laboratory testing and analysis. This process is ineffective, as this process is arduous and time-consuming and it does not provide real-time results. The quality of water should be monitored continuously, to ensure the safe supply of water from any water bodies and water resources. Hence, the design and development of a low-cost system for real-time monitoring of water quality using the Internet of Things (IoT) is essential. Monitoring water quality in water bodies using Internet of Things (IoT) helps in combating environmental issues and improving the health and living standards of all living things. The proposed system monitors the quality of water relentlessly with the help of IoT devices, such as, NodeMCU. The in-built Wi-Fi module is attached in NodeMCU which enables internet connectivity transfers the measured data from sensors to the Cloud. The prototype is designed in such a way that it can monitor the number of pollutants in the water. Multiple sensors are used to measure various parameters to assess the quality of water from water bodies. The results are stored in the Cloud, deep learning techniques are used to predict whether the water suitable or not.

Air pollution affects body organs and human systems in addition to the environment. Smart air pollution monitoring consists of wireless sensor nodes, server and a database to store the monitored data. Huge amounts of data are generated by gas sensors in air pollution monitoring system. Traditional methods are too complex to process and analyze the voluminous data. The heterogeneous data are converted into meaningful information by using data mining approaches for decision making. The K-Means algorithm is one of the frequently used clustering method in data mining for clustering massive data sets. In this paper, enhanced K-Means clustering algorithm is proposed to analyze the air pollution data. The correlation coefficient is calculated from the real time monitored pollutant datasets. The Air Quality Index (AQI) value is calculated from the correlation co-efficient to determine the air pollution level in a particular place. The proposed enhanced K-Means clustering algorithm is compared with Possibilistic Fuzzy C-Means (PFCM) clustering algorithm in terms of accuracy and execution time. Experimental results show that the proposed enhanced K-Means clustering algorithm gives AQI value in higher accuracy with less execution time for when compared to existing techniques.

In the last few years there has been tremendous increase in the storage and processing of data, with significant speed and storage space requirements. Medical image storage and retrieval is one such application which requires huge storage space and not easily solved by computers. The researchers who work in the area of medical image processing deal with a high degree of storage and retrieval of medical images. Digitizing medical images and computer based image retrieval replaces the use of hard copies and provides efficient remote access. The proposed grid architecture implemented in this work uses Apache Hadoop framework. Hadoop is an open source grid architecture, complies with various image formats and can be established among various hospitals to store, share and retrieve images. Various performance metrics such as reliability, accuracy, interoperability, confidentiality and security are improved as with the use of Hadoop. The user authentication and patient confidentiality is also achieved. In this work, texture based Content Based Image Retrieval (CBIR) algorithm is used for efficient image retrieval. The performance of the proposed image retrieval system using Hadoop is tested with three current operative nodes. Experimental results obtained show that the retrieval time of the proposed system, Hadoop-CBIR, is considerably less with large number of images in the database.

Background: Microarray technologies provide huge amount of information and is particularly helpful in the prediction and diagnosis of cancer. To accurately classify cancers, genes related to cancer have to be selected, as genes mined from microarrays possess too much noise. Method: In the current work, new binary modifications of the Grey Wolf Optimization (GWO) is suggested for choosing optimal features subsets for classification. In the proposed approach, GWO is modified by binarizing only the initial three optimal solutions and updation of the wolf position using stochastic crossover. Modification was also carried out using sigmoidal functions to compress the continuous updated positions. Multilayer Perceptron – Neural Network (MLP-NN) classifier are used for classifying the selected features. Results: The ALL/AML Leukaemia dataset is used for evaluating Markov Blanket filter, minimum Redundancy Maximum Relevance (mRMR), Binary GWO and Mutated Binary GWO (MBGWO) with regard to classification accuracy, sensitivity and specificity. The proposed MBGWO achieved classification accuracy of 95.45% and also has better sensitivity and specificity. Conclusion: Experiments reveal the capabilities of the proposed MBGWO to explore features space for the optimal features combination for gene selection from microarray data.