Smart agriculture has been a promising model with the intention of supervising farms by means of contemporary wireless technologies to enhance the quantity and quality of yield at the same time as minimizing the individual labor requirement. In addition the effective utilization of the Sensors as communication components that is the key one to monitor and manage soil, water, light, humidity, temperature. A Mobile Ad-hoc sensor node comprises sensors to gather real time environment from the agricultural land with the wireless communication technology and process the data before sharing information with other nodes in the network. On the other hand, the challenges have been enormously high path loss and lack of communication range under the environment when passing through soil, sand, water and other climatic conditions. As Wireless Sensor Networks (WSNs) has self-organized and adhoc wireless capability to monitor physical or environmental conditions, it can be used effectively in smart agriculture. As sensor nodes have been limited itself by means of power to be in active mode always, the design of such energy e cient Agriculture WSN is a paramount issue. Hence it has been planned to utilize the WSN as well as Ubiquitous technology for the smart agriculture with energy e ciency. With the purpose of build up a model, a Ubiquitous agriculture Mobile Sensor Network based Threshold built-in MAC Routing protocol (TBMP) has been proposed to make it t for minimal resource utilization by comparing with the existing protocols IMR and PTSR. In addition, the testing will be done to monitor changes in environmental surroundings in the agricultural land smartly in order to obtain maximum usage of Ubiquitous concept by applying existing and proposed protocols.

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Digital pathology is a technology that allows pathological information created from a digital slide to be accessed, handled, and interpreted. Using optical pathology scanners, glass slides are collected and transformed to digitized glass slides that can be viewed on your computer monitor. Relevant support for education and the practice of human anatomy is offered by digital pathology. With the recent developments in digital pathology led to computer-aided diagnosis using machine learning approaches. So, machine learning frameworks assist physicians in diagnosing critical cases such as cancer, tumors, etc and improve patient management. With an ever growing number of choices, it can be hard to pick a better machine learning method for pathological data. Big potential attempts are made in this paper to research the full context of digital pathology with the specifics of how artificial intelligence has contributed to digital pathology. This review also analyzes various machine learning frameworks by providing as much information as possible and quantifying what the tradeoffs will be. This paper ultimately provides the improvements in the frameworks available that will be required in the near future applications.

The high prevalence of urban flooding in the world is increasing rapidly with the rise in extreme weather events. Consequently, this research uses an Automatic Flood Monitoring System (ARMS) through a video surveillance camera. Initially, videos are collected from a surveillance camera and converted into video frames. After converting the video frames, the water level can be identified by using a Histogram of oriented Gradient (HoG), which is used to remove the functionality. Completing the extracted features, the frames are enhanced by using a median filter to remove the unwanted noise from the image. The next step is water level classifiers using a Convolutional Neural Network (CNN), which is utilized to classify the water level in the images. The performance analysis of the method is analyzed by various parameters. The accuracy of the proposed method is 11% higher than that of the k-Nearest Neighbors (KNN) classifiers and 5% higher than that of the ANN classifiers, and the processing time is 7% less than that of the KNN classifiers and 4% less than that of the Artificial Neural Network (ANN) classifiers.

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