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Present days the world is surviving with respect to the software products. The development of the software product is a challenging issue and to run with the competitive world rapid development of software products are necessary. It is not enough to go with the traditional software development products like waterfall model, spiral model and all. Here we proposed the SCRUM TOOL, one of the effective techniques of Agile Methodology. Agile is an incremental and timeframe iterative approach. It supplies the software developers with a working framework for traditional software development practices like waterfall model. Although the traditional models are best suited for small products where there is no changing requirements but not suitable for the products which have rapidly changing requirements and thus here we recommend SCRUM methodology. In this approach we can develop the software product by taking the regular feedback from the customer through review meetings. So whenever the customer requests the changes we can upgrade the product with respect to those changes and can also develop the product more efficiently and rapidly.

In this paper we have proposed face recognition door lock system using raspberry pi for security purpose. Implementation of the system is for monitoring whether any unknown person is entering in to the door. We have established communication with electronic devices through face detection with the help of Pi camera Raspberry Pi platform. For software coding Python and Open CV libraries are used. In order to get accurate and clear picture of an intruder we have proposed Haar classifier method for face detection. As soon as the person enters near the door, pi camera captures the image and face detection process is done then if it matches with database images then the door is unlocked otherwise a message with the picture of a person will be sent to the registered mobile through GSM and LAN network.

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Farming is very labour intensive and needs timely action. In smart farming many activities of farming are conducted by machines which run on electricity. Electricity is one of the key elements of smart farming. The quality and cost of the agriculture produce are mostly determined by quality of the available energy and energy utilized. Though India is agriculture rich country, many rural areas are still not provided with sufficient electricity for the farming. In the current scenario of depleting natural energy resources like fossil fuels, using electricity generated from fossil fuels is expensive. Hence, there is a strong need to shift to nonconventional renewable and natural energy resources. Solar energy is one such energy available in abundance in India, however, the existing solar energy harvesting technologies which uses solar cell technology is able to convert very little portion of the available solar energy. The conversion efficiency of solar cells is found to be 16-18%. The authors in this paper present a more efficient solar energy harvesting technology which uses nanomaterial for improving conversion efficiency and machine learning technology to maximize the collection of solar radiation by continuously tracking the path of the SUN in all the seasons.

Nowadays the multimedia data easily available to most people. It is the main cause of illegal access to multimedia content, theft of the intellectual property, easily copying and manipulate the data over the internet, and spreading fake news. With the increase in the availability of the internet to a common man, it is observed that most of the multimedia data misused. Nowadays telemedicine, tele diagnosis, tele consultation, teleradiology, telematic services are necessary. Electronic Patient Record is essential to provide these services. It is necessary to secure the multimedia data by using a suitable watermarking technique. Due to the huge availability of high-speed internet, digital movie piracy was increased rapidly and it causes revenue loss and affects the employment of the people. The high demand for protecting digital videos or movies from unauthorized access. So, it is necessary to protect the intellectual property of the owner, stop unauthorized access of data, also protect and stop the spreading of fake news. In this article, we presented a survey of digital watermarking, bolding its key concepts, embedded and extracted features, state-of-the-art implementation, and research challenges. The primary goal of this article is to provide an improved understanding of the embedding-extracting of watermarking challenges of watermarking and identify important research paths in health care and multimedia applications.

Reinforced concrete (RC) structures are often subjected to extreme dynamic loading conditions, mainly caused by effects of impact loading. A countable studies have been carried out on the structural behaviour of RC slabs under static and dynamic loadings. However, it is relatively infrequent to examine the impact behaviour of RC slabs that are embedded with non-conventional reinforcement layouts. Consequently, an experimental study was performed to examine the impact behaviour of geogrid reinforced concrete slabs. A total of six RC slab specimens embedded with different combination of steel and geogrid reinforcement layers was tested under drop weight impact test. The impact response in terms of failure modes, impact energy, impact ductility index and maximum deflection produced at each impact blow were studied. The results showed that, the RC slab specimens provided with geogrid reinforcement layer at both faces of slab specimens along with the conventional reinforcement resisted the crushing of concrete by spreading the impact stress to a larger area. This configuration of reinforcement also helps to withstand for higher impact forces, thereby influencing the enrichment in impact energy and impact ductility index.

Every day, the estimated volume of data which is generated per day is 2.6 quintillion bytes. From the last two years, there is a lot of data generation and execution is taking rise due to feasible technologies and devices. To make the information accessible with ease, we need to classify the information data and predict an accurate or at least an approximate expected result which is forwarded to the end user client. To achieve the said process, the information technology industries are more concerned with machine learning and edge computing. Machine learning is a integral subset of artificial intelligence. In machine learning, the foremost step towards achieving the above task is to observe the data which is produced in large amount, later classify the data to make the system learn (train) from the old data (experience) that is stored at the server level and finally predict an estimation as a result. The obtained result is been transformed onto the devices which have made a request for a particular data. These devices are remotely located at the corner of the central data center. The process in which the execution of the information data is done at the corner of the data center is called as edge computing. In today’s world of high computation, these two technologies i.e machine learning and edge computing are creating an overwhelming significance for its usage in the business market and end user clients. Here, we try to explain few possibilities of integrating the two technologies.