Smart agriculture management system using internet of things

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models.

Autonomous vehicles have been invented to increase the safety of transportation users. These vehicles can sense their environment and make decisions without any external aid to produce an optimal route to reach a destination. Even though the idea sounds futuristic and if implemented successfully, many current issues related to transportation will be solved, care needs to be taken before implementing the solution. This paper will look at the pros and cons of implementation of autonomous vehicles. The vehicles depend highly on the sensors present on the vehicles and any tampering or manipulation of the data generated and transmitted by these can have disastrous consequences, as human lives are at stake here. Various attacks against the different type of sensors on-board an autonomous vehicle are covered.

Internet of Things (IoT) refers to the practice of designing and modeling objects connected to the Internet through computer networks. In the past few years, IoT-based health care programs have provided multidimensional features and services in real time. These programs provide hospitalization for millions of people to receive regular health updates for a healthier life. Induction of IoT devices in the healthcare environment have revitalized multiple features of these applications. In this paper, a disease diagnosis system is designed based on the Internet of Things. In this system, first, the patient's courtesy signals are recorded by wearable sensors. These signals are then transmitted to a server in the network environment. This article also presents a new hybrid decision making approach for diagnosis. In this method, a feature set of patient signals is initially created. Then these features go unnoticed on the basis of a learning model. A diagnosis is then performed using a neural fuzzy model. In order to evaluate this system, a specific diagnosis of a specific disease, such as a diagnosis of a patient's normal and unnatural pulse, or the diagnosis of diabetic problems, will be simulated.

Concerns about fire risk reduction and rescue tactics have been raised in light of recent incidents involving flammable cladding systems and fast fire spread in high-rise buildings worldwide. Thus, governments, engineers, and building designers should prioritize fire safety. During a fire event, an emergency evacuation system is indispensable in large buildings, which guides evacuees to exit gates as fast as possible by dynamic and safe routes. Evacuation plans should evaluate whether paths inside the structures are appropriate for evacuations, considering the building’s electric power, electric controls, energy usage, and fire/smoke protection. On the other hand, the Internet of Things (IoT) is emerging as a catalyst for creating and optimizing the supply and consumption of intelligent services to achieve an efficient system. Smart buildings use IoT sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. This research proposes a new way for a smart building fire evacuation and control system based on the IoT to direct individuals along an evacuation route during fire incidents efficiently. This research utilizes a hybrid nature-inspired optimization approach, Emperor Penguin Colony, and Particle Swarm Optimization (EPC-PSO). The EPC algorithm is regulated by the penguins’ body heat radiation and spiral-like movement inside their colony. The behavior of emperor penguins improves the PSO algorithm for sooner convergences. The method also uses a particle idea of PSO to update the penguins’ positions. Experimental results showed that the proposed method was executed accurately and effectively by cost, energy consumption, and execution time-related challenges to ensure minimum life and resource causalities. The method has decreased the execution time and cost by 10.41% and 25% compared to other algorithms. Moreover, to achieve a sustainable system, the proposed method has decreased energy consumption by 11.90% compared to other algorithms.