This investigation strongly suggests the possibilities of candidal infections in patients even in the absence of predisposing factors such as HIV infection or immune compromised conditions. Hence, patients with symptoms of odynophagia and dysphagia shall be considered for possible esophageal candidiasis.

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Congestion management is one of the most important issues for secure and reliable system operations. One of the most practiced techniques for congestion management is rescheduling the real power output of generators in the system. In this paper Particle Swarm Optimization (PSO) is used to determine the optimal generation levels to alleviate transmission congestion. Numerical results on IEEE 30 Bus test system is presented and the experimental outcomes demonstrate that PSO is one among the demanding optimization methods which are certainly capable of obtaining higher quality solutions for the proposed problem.

Agriculture 4.0, as the future of farming technology, comprises numerous key enabling technologies towards sustainable agriculture. The use of state-of-the-art technologies, such as the Internet of Things, transform traditional cultivation practices, like irrigation, to modern solutions of precision agriculture. To achieve effective water resource usage and automated irrigation in precision agriculture, recent technologies like machine learning (ML) can be employed. With this motivation, this paper design an IoT and ML enabled smart irrigation system (IoTML-SIS) for precision agriculture. The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation. The proposed IoTML-SIS model involves different IoT based sensors for soil moisture, humidity, temperature sensor, and light. Besides, the sensed data are transmitted to the cloud server for processing and decision making. Moreover, artificial algae algorithm (AAA) with least squares-support vector machine (LS-SVM) model is employed for the classification process to determine the need for irrigation. Furthermore, the AAA is applied to optimally tune the parameters involved in the LS-SVM model, and thereby the classification efficiency is significantly increased. The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

Mosquito index study of three ecologically different ecozones of the Thiruvananthapuram district, Kerala showed sharp difference on the proportionate distribution of Aedes aegypti and Aedes albopictus. Human dengue viremia (HDV) was very high in those ecozones where A.aegypti density was high and HDV was low where A.albopictus was high. In a coastal zone of Thiruvananthapuram city, A. aegypti was the most abundant vector and in a hilly, arid suburban zone, A.albopictus was the abundant vector. In the urban zone both species of mosquitoes showed equal distribution. Study on the circulating serotypes in the serum of HDV by Single step single tube Multiplex PCR showed all the four serotypes viz DENV1, DENV2, DENV3 and DENV4 in patients of Thiruvananthapuram city, which indicated the possibility of Dengue Shock Syndrome, unless there is efficient vector management. Among the four dengue serotypes, Type 1 was the most abundant virus. Abundance of microhabitats in Thiruvananthapuram city, which support A. aegypti may be the reason for high prevalence of dengue fever in the urban zone.

Cloud computing provides physical and logical computation resource on demand for the set of service. Cloud environment reduce the infrastructure cost and easy to use without any extra burden. Cloud storage an access raised the several security issues like data privacy, access control, authentication, virtual machine security, web security etc., In one side hackers, breaches, cloud security issues and threats get expanded. But in another side many technologies are keep increased to secure cloud data. Technology may be cryptographic technique, anonymization technique, machine learning technique etc., In this paper we analyse cloud computing basics, models, machine learning technique and some security solution through machine learning technique such as support vector machine (SVM), K-Nearest Neighbour (KNN), Decision tree and Naïve Bayes classifier technique.

Energy technologies and their efficient use plays a vital role in socio-economic development of any country. In the recent years, the restructuring of electricity market evolves some major improvements in the technologies of energy production and thus, it has paved the way for increasing the applications of Distributed Generation (DG) with renewable energy sources. In this research, the optimal placement and sizing of multiple DGs are achieved by a novel indicator United Bus and Line Voltage Firmness Factor (UBL_VFF). The objectives of this work are the minimization of system losses and maximization of voltage stability and they are achieved by identifying the weakest voltage bus due to the weakest link in the system. Particle Swarm Optimization (PSO) is used for solving this optimization problem. The effectiveness of this proposed approach is tested in 33 and 69 bus radial distribution test systems. The results of this proposed method is compared with the results reported in the contemporary literature. The results have proved to be robust in terms of reduction in system losses and maximization of bus and line voltage stability.