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In cloud computing, providers pool their resources and make them available to customers. Next-generation computer scientists are flocking to the cutting-edge field of cloud computing for their research and exploration of uncharted territory. There are still several barriers that cloud service providers must overcome in order to provide cloud services in accordance with service level agreements. Each cloud service provider aspires to achieve maximum performance as per Service Level Agreements (SLAs), and this is especially true when it comes to the delivery of services. A cloud service level agreement (SLA) guarantees that cloud service providers will satisfy the needs of large businesses and offer their clients with a specified list of services. The authors offer a web service level agreement–inspired approach for cloud service agreements. We adopt patterns and antipatterns to symbolize the best and worst practices of OCCI (Open Cloud Computing Interface Standard), REST (Representational State Transfer), and TOSCA (Topology and Orchestration Specification for Cloud Applications) with DevOps solutions, all of which API developers should bear in mind when designing APIs. When using this method, everything pertaining to the cloud service, from creation to deployment to measurement to evaluation to management to termination, may be handled mechanically. When distributing resources to cloud apps, our system takes into account the likelihood of SLA breaches and responds by providing more resources if necessary. We say that for optimal performance, our suggested solution should be used in a private cloud computing setting. As more and more people rely on cloud computing for their day-to-day workloads, there has been a corresponding rise in the need for efficient orchestration and management strategies that foster interoperability.

This research work proposes a threat intelligence model for Internet-of-Things (IoT) sensors-based Body Area Network (BAN). It is focused primarily to be used in healthcare monitoring of vital parameters of critically ill patients and on the contrary performance measurement system for healthy sportspersons. The end-point control based applications are growing enormously with the advent of IoT based sensors and actuators being used in intelligent real-time systems. At the same time, it is expected to keep the ecosystem safe for the user while delivering the constant updates. However, the process for the monitoring health and wellness parameters of a patient, or measuring endurance and performance of a sportsperson, it remains vulnerable without a secure environment. Using the proposed model, the entire healthcare ecosystem may be designed for the personalized medication of a patient who are using sophisticated life-saving device like prosthetic heart valve or an elderly person dependent on medical-aided ambulatory devices or a sportsperson on performance measurement system.

Background: Artificial Intelligence (AI) operations have evolved over the last two decades in improvising the operations for an agriculture-based economy. The operations in this area face many challenges in maximizing their crop yield while facing the challenges like deficient soil treatment, problems from pests and crop-based diseases. Technical challenges for the requirements of real-time data which can be further transformed to big data. Its impact is fetching low yield, because of the knowledge gap between farmers and technology. These points are the key motivators of introducing an ecosystem with artificial intelligence to agriculture in this research work. IoT devices are capable to generate large amounts of data that could be transformed into information about environmental parameters like the temperature of the field, which acts as an engine to provide data. All the data shall be collected, stored and further analyzed for better decision-making. One way the business uses the data collected is by nourishing it into the AI systems, which grasp the IoT data and further use it to make predictions. Methods: This research work enables to cater the solution for supporting the farmers with IT as an enabler using data analytics on the collected information. It uses a web application that would help to monitor the soil fertility and suggest the producers to select the best crop(s) that can be grown in that geographical region. Result: The result from our crop recommender engine successfully predicted which crop(s) we could choose to grow in our gardens or farm fields. An extensive soil study along with meteorological models helped us to launch a smart agricultural system in a much smarter way. This has further helped to bridge the gap between production and quantity yield.

Brain tumor segmentation is an essential and challenging task because of the heterogeneous nature of neoplastic tissue in spatial and imaging techniques. Manual segmentation of the tumor in MRI images is prone to error and time-consuming tasks. An efficient segmentation mechanism is vital to the accurate classification and segmentation of tumorous cells. This study presents an efficient hierarchical clustering-based dense CNN approach for accurately classifying and segmenting the brain tumor cells in MRI images. The research focuses on improving the efficiency of the segmentation algorithms by considering the qualitative measures such as the dice score coefficient using quantitative parameters such as mean square error and peak signal to noise ratio. The experimental analysis states the efficacy and prominence of the proposed technique compared to other models are tabulated within the paper.

Former techniques for the identification of lion individuals (Panthera leo) relied on manual methods of recording data. Such processes have various shortcomings due to the manual nature of recording this data. This research work aims to automate the process of encoding the uniqueness within the whisker spot patterns for each lion individual by non-invasively using photographs. Towards this research work the main bottleneck was the availability of image data for individual lions. The proposed model embeds the uniqueness within the patterns for a specific individual as a unique cluster within its embedding space. This is achieved by using a triplet loss function which, due to its one-shot learning nature trains a deep inception network with less training data. Photographic images are known to have variations in lighting, pose variation, angle variation and other inconsistencies. Since the nature of these issues are nonlinear, it is preferred to create the target model using deep learning techniques. An inception network is trained to generate 128-dimensional vectors unique to each lion. This research paper elaborates on such deep machine learning techniques and other processes that are used to create this model.

According to the Google I/O 2018 key notes, in future artificial intelligence, which also includes machine learning and deep learning, will mostly evolve in healthcare domain. As there are lots of subdomains which come under the category of healthcare domain, the proposed paper concentrates on one such domain, that is breast cancer and pneumonia. Today, just classifying the diseases is not enough. The system should also be able to classify a particular patient’s disease. Thus, this paper shines the light on the importance of multi spectral classification which means the collection of several monochrome images of the same scene. It can be proved to be an important process in the healthcare areas to know if a patient is suffering from a specific disease or not. The convolutional layer followed by the pooling layer is used for the feature extraction process and for the classification process; fully connected layers followed by the regression layer are used.

When an intense brightness for a small amount of time is seen in the sun, then we can say that a solar flare emerged. As solar flares are made up of high energy photons and particles, thus causing the production of high electric fields and currents and therefore results in the disruption in space-borne or ground-based technological system. It also becomes a challenging task to extract its important features for prediction. Convolutional Neural Networks have gain a significant amount of popularity in the classification and localization tasks. This paper has given stress on the classification of the solar flares emerged on different years by stacking different convolutional layers followed by max pooling layers. From the reference of Alexnet, the pooling layer employed in this paper is the overlapping pooling. Also two different activation functions that are ELU and CReLU have been used to investigate how many number of convolutional layers with a particular activation function provides with the best results on this dataset as the size of the dataset in this domain is always small. The proposed investigation can be further used in a novel solar prediction systems.

This paper is proposing a recommender engine for security of real time monitoring systems based on the IoT based architectures. Depending upon the dynamic requirement for the most scalable technology of IoT this paper is proposing a fuzzy logic based recommender system. This system shall adapt the circumstances and acquire the required factors to resolve the security issues.

Data security is a process of enhancing the data privacy measures to filebase, database, websites while preventing unauthorized access to the datasets and data-streams. Encryption is the key data security technique that prevents the access of digital data from unauthorized person or hackers. This research work proposes a novel approach using data transformation and encapsulation techniques namely Hadamard Transform along with DNA Cryptography and Amino Acid for enhancing data security.

The workplace of the future promises to be quite different from present days. The technological strides of recent years including digitalization, block-chains, artificial intelligence and other forms of technology would herald a new VUCA world. This would usher in new job opportunities on one hand while wiping out thousands of present day jobs. India aspires to be among top three economic superpowers by 2030 with a USD 5 trillioneconomy by 2025. However, the future workforce seems woefully unprepared for being industry-ready to meet the challenges of Industry 4.0. Recent reports suggest that half of the 310 million school graduates that would form the next generation workforce will lack required job skills to lead the Fourth Industrial Revolution. Hence, it is imperative for the Indian education system to make a shift in teaching-learning process at secondary school level to mitigate the impending challenge. Defining the new models of education thus becomes important to dole-out firms that are struggling to stay competitive. The current paper attempts to present an integrated model of general and vocational education at secondary level to dramatically transform the skill landscape. It would facilitate designing new models of education that reimagines the existing eco-system to embed the new-age skills among young learners. This seminal work attempts to foster a comprehensive and unified approach to close the skill-gap and aid multiple socio-economic outcomes to bridge the staggering income divide and inequality among society.

The hasty development of information and communication technologies (ICTs) has overwhelmingly altered many aspects of life and societies all around the globe. High-quality information and communication technology (ICT) infrastructure is essential for developing countries to match the pace of economic growth. This can be achieved only with sophisticated infrastructure. Enormous competitive pressure on businesses across the world has made Information and Communication Technologies (ICTs) one of the main drivers of economic growth. This paper studies a select set of economic factors and their respective impacts on ICT development in five leading emerging economies known as BRICS (Brazil, Russia, India, China, and South Africa). The authors' findings demonstrate that in addition to deregulation, existing conditions (socioeconomic factors) must also be considered. They conclude by avowing that policy makers can more easily realize economic development via ICTs if they consider these conditions while cultivating their technology strategies.

The global market for chewing gum is approximately 13 lac tonnes per year reprcsenting almost a US 19 billion dollar industry, meriting heavy media and print promotion by the gum manufacturing industry. Chewing gum has almost as long a history as the mankind itself. Chewing gums have been marketed for a variety of benefits such as an adjunctive, oral hyglene aid, for smoking cessation and as a preventive option for various dental problems Iike plaque accumulation, gingivitis, caries etc. The medicinal uses of chewing gum have also been realised in the last thirty years. The ease of administration and control of dosage makes it a potent drug delivery system. Consequently, chewing gums have seen changes in their components over a period of time with many active ingredients being added to exploit this potential. Effects of various therapeutic substances in chewing gums have been studied over the last few decades. This review aims to compare the benefits and drawbacks of chewing gum to provide an objective insight from a neutral perspective.

India is stated to be a superpower of the future. The undeniable fact is that she has a formidable chunk of her population at the poorest socio-economic group - the bottom of pyramid (BOP). One of the key growth areas is the healthcare industry. It is one of the few industries yet to be completely disrupted by technology, though its foundation is shifting from within. We talk of making access to good healthcare a fundamental right, yet millions in India struggle for it every day. In an environment of optimism, there is a strong need that leading industry players as healthcare providers and pharmaceutical firms rethink their business models and infuse technology in a big way to reinvent their approach to reach out to serve BOP healthcare consumers. This calls for conceptualising sustainable healthcare models that encompass initiatives of software automation for simplified clinic management. It would thereby help healthcare service providers offer a superior patient experience in the clinic and also facilitate offline access to all their electronic healthcare records. India's healthcare needs and costs are mounting and healthcare start-ups might help target more innovative investment in healthcare and ensure the adoption of 'inclusive business models'.

Image watermarking implants an ownership parameter in a digital image or video. This paper works on authentication of the image file. Robust image watermarking technique is used to model the Human Visual System (HVS) using Fuzzy Logic. The proposed Fuzzy Logic System is trained using inference rules considering three sensitivities of HVS as Brightness, Contrast and Edge Sensitivity. The Fuzzy network uses the image captured in realtime and computes block wise for producing a single output weighting factor used to embed unique identification numbers generated from the confidential data as watermark, which is for authorization and verification of the original image. The robustness of the watermark embedded is checked by Stirmark image processing attacks. Recovered watermark's computed value using SIM (X, r) parameter for the image verified it as good watermark recovery process.