Internet of things (IoT) and data-driven techniques are creating greater opportunities for smart dairy farming. The demand for milk is continuously increasing due to increasing population of the world. The consumption of the dairy products is more in developed countries as compared to developing countries. To meet this increased demand for milk products, better technological techniques for improving milk yield are required. It is expected that the use of IoT and different AI techniques can assist a farmer to overcome different traditional farming challenges and increase the milk production. In this research, the authors address different challenges that a dairy farmer has to face in daily life. Brief introduction of smart dairy farming (SDF) is presented with respect to the innovation in production and the processes of smart dairy farming. This review focuses on different aspects of smart dairy farming, and finally a state-of-the-art framework that can assist the farmers to increase the milk yield by using different latest technologies has been proposed. These technological methods can decrease the factors negatively affecting milk production and increase those positively affecting production with minimal resources.

Internet of things technology consists of physical objects that are accessible via the Internet, for example, devices, vehicles, and buildings. Internet of things technology is used to connect these physical objects by utilizing the existing infrastructure of networks. A unique identifier is assigned to identify the objects in IoT environments. Internet of things technology is used to make productive decisions on the sensed data after converting it into information. IoT technology is being used in various life disciplines, such as smart health services delivery, smart traffic management, border management, and governmental control. There is no single standard for IoT technology; thus, interoperability between IoT devices that use different protocols and standards is required. This research was carried out to provide and develop a specialized framework for an IoT‐based smart health system by focusing particularly on interoperability problems. Based on different technology standards and communication protocols, the specific requirements of the IoT system were analyzed and served as a basis for the design of the framework. The protocols and standards within the framework utilize existing web technologies, communication protocols, and hardware design. This approach ensures that the specific expectations of the proposed model can be fulfilled with confidence. The experiments showed that interoperability between different IoT devices, standards, and protocols in a smart health system could be achieved using a specialized gateway device and that different web technologies could be used simultaneously in constrained and Internet environments.

Conventional approaches to query expansion (QE) rely on the integration of an unstructured corpus and probabilistic rules for the extraction of candidate expansion terms. These methods do not consider search query semantics, thereby resulting in ineffective retrieval of information. Semantic approaches for QE overcome this limitation, whereby a search query is expanded with meaningful terms that accord with user information needs. This article surveys recent approaches to semantic QE that employ different models and strategies and leverages various knowledge structures. We organize these approaches into a taxonomy that includes linguistic methods, ontology-based methods and mixed-mode methods. We also discuss the strengths and limitations of each type of semantic QE method. In addition, we evaluate various semantic QE approaches in terms of knowledge structure utilization, corpus collection, baseline model adaptation and retrieval performance. Finally, future directions in exploiting personalized social information and multiple ontologies for semantic QE are suggested.

Researchers in the software industry have focused on risk management systems for a long time. Software risk management is a software engineering practice that contains risk identification, risk estimation, mitigation and monitoring. It delivers a disciplined environment for efficient decision-making to assess the problems in software development. Measuring risks in a large-scale system is comparatively difficult because of its complex nature. Large-scale systems are challenging since many risks can arise during system development. Risk factors in large-scale systems are relatively different from small systems, especially with respect to the independent components. This study describes a difference between large-scale and small-scale systems along with an exhaustive list of risk factors. The tools from the literature are further divided into sub categories according to those that are best suited. We present a detailed comparative analysis for different software-related risk management models with some commonly identified features and further categorize them into classes based on the severity of their risks.