Deep Learning-Driven Chatbots for Crop Health Monitoring and Agricultural Decision Support | Chapter 1 | Mathematics and Computer Science: Research Updates Vol. 9

 

Numerous problems in agriculture, including unpredictable crop yields, disease susceptibility, and the consequences of weather variability, put nutrition and farmer livelihoods at risk. In order to increase agricultural yields, detect diseases early, and provide valuable insights on the Crop Yield Prediction Dataset and Plant Village Dataset, this research provides an AI-powered solution to these issues by integrating deep learning, sophisticated machine learning algorithms, and instantaneous data analysis. The system employs a sophisticated methodology that forecasts temperature, humidity, and conditions for the next five days using the PyOWM API; detects crop diseases using data augmentation and deep learning models such as CNN (accuracy 99.14%), DenseNet-201 (accuracy 99.04%), and Visual Geometry Group-VGG19 (accuracy 97%); and predicts crop yield using models such as Multi-Layer Perceptron-MLP (R2 Score: 0.8242), MLP + Regressor, and Random Forest Regressor achieves the highest R2 Score (0.1789). An AI chatbot that provides farmers with recommendations, disease control methods, and personalised suggestions is part of the technology's real-time help. In order to provide an AI-driven system for weather forecasting, disease detection, yield prediction, and real-time assistance via a chatbot, this project integrates models with high accuracy rates. The user-friendly Streamlit UI is available in Telugu, Hindi, and English, and SQLite handles the secure login and registration procedure.

 

 

Author(s) Details

 

Anantha Kranthi Suravarapu
Department of CSE (Artificial Intelligence & Machine Learning), Ramachandra College of Engineering, Eluru, Andhra Pradesh, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v9/6825

Bank Term Deposit Subscription Recommendation Using Cross-validated Neural Network | Chapter 1 | New Innovations in Economics, Business and Management Vol. 2

 Term deposits can help to accelerate the financial field by increasing profit for both the bank and the customer. The bank's promotion efforts, as well as the customer's background information, often affect term deposit subscription. If the bank can identify clients' subscription tendencies early on, it can change its fundamental strategy to attract additional customers. The current work has focused on identifying term likelihood prediction from the customer's perspective in this setting. Machine learning-based algorithms have been used to predict term deposit investment opportunities in advance for this study. Neural network, a common machine learning-based method, is proposed as the predictive model in this study, along with stratified 10-fold cross-validation methodology. Other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP) are also implemented and compared to assess the efficiency of this model. With an accuracy of 88.32 percent and an MSE of 0.1168, this comparison analysis found that the suggested model outperforms existing baseline models in terms of prediction results.

Author(S) Details

Shawni Dutta
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

Payal Bose
Lincoln University College, Kota Bharu, Kelantan, Malaysia.

Samir Kumar Bandyopadhyay
Department of Computer Science, The Bhawanipur Education Society College, Kolkata, India.

View Book:- https://stm.bookpi.org/NIEBM-V2/article/view/4737

Study on Modeling with Multilayer Perceptron for Detection of Fuel Adulteration Using Python Programming | Chapter 10 | Challenges and Advances in Chemical Science Vol. 6

 Adulteration of fuel is the illegal or unpermitted introduction of an unknown substance into motor spirit, resulting in a product that does not meet the needs and specifications. Normally, cheaper boiling point range hydrocarbons with similar composition are added as additives, causing the quality of the base fuels to be altered and degraded. The trading community uses this approach to make quick unlawful profits. This is due to the fact that tailpipe exhaust from automobiles pollutes the environment and poses a health risk to humans. Fuel pipes leaking exhaust due to illegally added ethanol and methanol to increase octane levels. There must be a proper method for detecting contaminants, both at the laboratory level and at the legislative level. The Artificial Neural Networks technique for analysing fuel adulteration is more precise than any other method currently in use. The gasoline and hydrocarbon fractions are detected in-situ with the help of the Internet of Things, which can be controlled via a remote and data collected via smattering. This information will aid in the detection of contaminants in gasoline and diesel pollutants emitted into the atmosphere via tailpipe emissions. In this paper, we use a cutting-edge computational technique known as Multilayer Perceptron (MLP) to identify impurities in fuels. As a result, global warming and hazardous diseases will be reduced. The multilayer perceptron (MLP) is a type of feed forward artificial neural network that is one of the most efficient techniques for detecting fuel adulterants. For data training, MLP employs the back propagation approach. It has three layers: the input layer, the concealed layer, and the output layer. For the detection and estimate of 3D objects from a single 2D perspective view It is a multilayer perceptron that is employed.

Author(S) Details

U. Vimal Babu
Vignan Foundation for Scientific and Technological Research University, Vadlamudi, Guntur, AP, India.

M. Ramakrishan
Vignan Foundation for Scientific and Technological Research University, Vadlamudi, Guntur, AP, India.

M. Nagamani
School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India

View Book:- https://stm.bookpi.org/CACS-V6/article/view/4356