The intake of the perishable fruits and vegetables (FVs) in the human diet can contribute to reduce the risk of some chronic diseases. But unfortunately, FVs loss rate is high among all the food produced annually and occurs at storage stage of post-harvest life cycle. One of the key factors contributing to this high loss rate is inability to gauge vital ambient environmental parameters in cold storage. The existing monitoring solutions about cold storage are limited to only gauge temperature, relative humidity and ignore other vital ambient environmental parameters such as luminosity and concentration of gases. This is a critical issue that needs to be addressed to overcome the loss rate of FVs. This paper presents a real-time intelligent monitoring and notification system (RT-IMNS) banked on an Internet of Things (IoT)-enabled approach for real-time monitoring of temperature, relative humidity, luminosity and concentration of gas in cold storage and notifies the personnel on exceeding of dangerous limits of these parameters. Moreover, decision support is implemented in the RT-IMNS using Artificial Neural Network (ANN) with forward propagation to classify the status of commodity into one of three classes i.e. good, unsatisfactory or alarming. The proposed prediction model outperforms Compress Sending (CS), Adaptive Naïve Bayes (ANB), Extreme Gradient Boosting (XGBoost) and Data Mining (DM) with respect to forecasting accuracy. We achieved 99% accuracy using forward propagation neural network model while existing models such as CS, ANB, XGBoost, DM achieved 95.60%, 87.50%, 93.59%, 90% accuracy respectively. Moreover, proposed approach achieved 100% precision, 100% recall, 100% F1-score for good class is achieved, for unsatisfactory class precision is 98%, recall is 99%, F1-score is 98% and for alarming class precision is 100%, recall is 98% and F1-score is 99%.

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm (KNN). Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge.

In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. The online hotel search has been increased at a very fast pace and became very time-consuming due to the presence of huge amount of online information. Recommender systems (RSs) are getting importance due to their significance in making decisions and providing detailed information about the required product or a service. To acquire the hotel recommendations while dealing with textual hotel reviews, numerical ranks, votes, ratings, and number of video views have become difficult. To generate true recommendations, we have proposed an intelligent approach which also deals with large-sized heterogeneous data to fulfill the needs of the potential customers. The collaborative filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion-based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis, and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommends hotel class based on guest type using fuzzy rules. Different experiments are performed over the real-world datasets obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated, and the results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user’s smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.

Efficient and cost effective ways of irrigation have emerged as the need of the hour due to limited sweet water resources, especially the countries that are seriously hit by a lack of sweet water reservoirs. The majority of the water is wasted due to inefficient ways of watering plants. In this paper, we propose an intelligent approach for efficient plant irrigation that has a database of daily water needs of a type of plant and decides the amount of water for a plant type on the basis of the current moisture in soil, humidity, and time of the day. This approach not only saves sweet water by efficient utilization, but also supports smart consumption of energy. Our approach employs IoT and a set of sensors to efficiently record plant data and their watering needs and the approach is implemented with a mobile phone application interface that is used to continuously monitor and control the efficient watering system. The results of this study are easy to reproduce as the sensors used are cheap and easy to access. The study discusses in this paper is experimented on small area (such as tunnel farm) but the results of the experiments show that the used approach can be generalized and can be used for large size fields for efficient irrigation. The results of the experiments also outperform the manual approach and the similar approaches for sensor based irrigation systems.