Meta Learning-Based Dynamic Ensemble Model for Crop Selection

Agricultural sector is working for optimal crop yield toward securing a sustainable food supply for the world. Fast growth in precision agriculture helps farmers to increase their yields by extending the era of machine-learning techniques. However, in organic and inorganic farming, predicting yield is an open issue that dominantly depends on the presence of soil nutrients. The lack of knowledge about the richness of land nutrients deals with the crop selection problem. Therefore, the proposed work extended the idea of a dynamic ensemble model for imbalanced multi-class nutrient data. In this work, an attempt is being made to include a novel customized voting strategy for deciding the final class output from the ensemble model. As an initial step, a well-known ranking technique, VIKOR, is applied over land nutrients to extract the most informative land samples. The rationale is to reduce the complexity of the ensemble model by determining only informative land samples for further classification. Furthermore, the meta-learning approach of dynamic ensemble selection accounts for multi-criterion-based competent classifier selection as meta-classifiers. These meta-classifiers decide on ensemble formation with the customized voting strategy to classify the right crop for the test land. To investigate nutrient richness, real-time soil and water nutrient data are collected from the soil testing laboratory, which covers different spatial data. Our experiments on six popular DES algorithms over nutrient data reveal the proposed algorithm’s outperformance in specificity, sensitivity, BCA, Multi-Area under Curve, and precision. Moreover, the lesser computational time of the proposed work indicates the model’s efficiency toward suitable crop selection

Statistical modeling of an integrated boiler for coal fired thermal power plant

The coal fired thermal power plants plays major role in the power production in the world as they are available in abundance. Many of the existing power plants are based on the subcritical technology which can produce power with the efficiency of around 33%. But the newer plants are built on either supercritical or ultra-supercritical technology whose efficiency can be up to 50%. Main objective of the work is to enhance the efficiency of the existing subcritical power plants to compensate for the increasing demand. For achieving the objective, the statistical modeling of the boiler units such as economizer, drum and the superheater are initially carried out. The effectiveness of the developed models is tested using analysis methods like R2 analysis and ANOVA (Analysis of Variance). The dependability of the process variable (temperature) on different manipulated variables is analyzed in the paper. Validations of the model are provided with their error analysis. Response surface methodology (RSM) supported by DOE (design of experiments) are implemented to optimize the operating parameters. Individual models along with the integrated model are used to study and design the predictive control of the coal-fired thermal power plant

Dynamic Physical Activity Recommendation Delivered through a Mobile Fitness App: A Deep Learning Approach

Regular physical activity has a positive impact on our physical and mental health. Adhering to a fixed physical activity regimen is essential for good health and mental wellbeing. Today, fitness trackers and smartphone applications are used to promote physical activity. These applications use step counts recorded by accelerometers to estimate physical activity. In this research, we performed a two-level clustering on a dataset based on individuals’ physical and physiological features, as well as past daily activity patterns. The proposed model exploits the user data with partial or complete features. To include the user with partial features, we trained the proposed model with the data of users who possess exclusive features. Additionally, we classified the users into several clusters to produce more accurate results for the users. This enables the proposed system to provide data-driven and personalized activity planning recommendations every day. A personalized physical activity plan is generated on the basis of hourly patterns for users according to their adherence and past recommended activity plans. Customization of activity plans can be achieved according to the user’s historical activity habits and current activity objective, as well as the likelihood of sticking to the plan. The proposed physical activity recommendation system was evaluated in real time, and the results demonstrated the improved performance over existing baselines