The optimization of fiber-reinforced high-strength concrete (FR-HSC) remains challenging due to complex nonlinear interactions among binder composition, fiber content, and mix proportions. This study proposes a hybrid optimization-driven AI framework integrating machine learning (ML), deep learning (DL), and hybrid optimization for accurate strength prediction and efficient mix design. A dataset of 392 samples, comprising 32 experimental and 360 literature-based data points, was developed covering a wide range of material compositions. Experimental results showed that the hybrid mix with 1.0% steel fiber and 0.45% polypropylene fiber achieved the highest 28-day compressive strength of 111.96 MPa, demonstrating the synergistic effect of fiber hybridization. Multiple ML models (SVR, RF, XGB-RR) and DL models (ANN, DCN, CNN-LSTM) were trained and optimized using a hybrid Genetic Algorithm-Bayesian Optimization (GA-BO) approach. Among all models, XGB-RR achieved the best performance with R2 = 0.998 and RMSE = 1.397 MPa in training, and R2 = 0.901 in testing, indicating strong predictive accuracy and generalization. SHAP analysis identified solution-to-binder ratio, steel fiber content, cement content, and superplasticizer dosage as the most influential parameters governing strength. An interactive GUI was also developed for real-time prediction and optimization. The proposed framework demonstrates high accuracy, interpretability, and practical applicability for sustainable FR-HSC mix design.
The research work introduces the development of a Retrieval-Augmented Generation (RAG)-based chatbot using the LLaMA-3 model. Unlike traditional chatbots, which are limited by predefined responses, the RAG-based framework improves the chatbot's ability to retrieve and integrate information from user-provided documents and from a knowledge base, ensuring that responses are accurate, contextually appropriate, and informative. The chatbot has the ability to understand the intricate queries and produce the response after 4 seconds depending on the prompt by utilizing the advanced natural language capabilities of the LLaMA-3. Privacy is achieved by ensuring that all data processing is done in-house so that the data of the user does not leave the system and does not need internet access. This does not only increase security but it also enables those organizations that have stringent data compliance requirements to utilize the system without concerns. Besides strong response generation, chatbot could be specialized in specific domain needs like providing legal advice, academic research, healthcare consultations and customer service automation. The system is multi-turn, contextual and provides module components that can be easily integrated with other existing enterprise systems. The proposed work indicates how the combination of advanced language models and effective document search tools could lead to the development of very productive, smart, and personal chatbots that can be successfully implemented in various real life situations.
Rice cultivation, a staple for over 70
Sensor technology progressions have paved the way for the rapid expansion of the Internet of Things (IoT) applications to construct behavioral and physiological monitoring systems, like an IoT-based student healthcare monitoring system. The status of student health observation is necessary because the number of students who survive loneliness is increasing in large geographical areas. This research article presents an approach named optimized attention enhanced temporal graph convolutional network-based cloud resource allocation supported Internet of Things for students' health monitoring system (HMS-AETGCN-NGOA-IoT). The proposed HMS-AETGCN-NGOA-IoT is implemented using MATLAB. To detect students' health status, performance metrics like precision, accuracy, F1-score, Recall (Sensitivity), Specificity, Error rate, Computation time, and ROC are considered. The HMS-AETGCN-NGOA-IoT approach achieves 19.11%, 24.12%, and 28.13% higher specificity; 24.93%, 23.04%, and 9.51% lower computation time; 15.2%, 25.45%, and 13.91% higher ROC values; and 8.45%, 20.98%, and 27.55% higher accuracy compared with the existing Health Monitoring System based on Message Passing Neural Network for Internet of Things(HMS-MPNN-IoT), Health Monitoring System based on Support Vector Machine for Internet of Things(HMS-SVM-IoT) and Health Monitoring System based on Deep Neural Network for Internet of Things(HMS-DNN-IoT) methods, respectively.
