AI and virtual assistants are transforming higher education by using digital tools to enhance teaching and learning in ways that go beyond traditional methods. These digital tools are not merely supplementary aids but...
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This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global he...
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A new, improved computer program made specifically for banking helps in solving problems like inadequate guidance and makes it convenient for people who cannot visit banks for some reason. This helps people answer que...
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In today's dynamic world of online shopping, augmented reality (AR) integration has emerged as a game-changing innovation. It transcends the limitations of traditional online shopping by harnessing AR technologies...
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The recent advancement Tesseract OCR engine and the YOLO4 (You Only Look Once version 4) object detection framework provide an innovative approach to optical character recognition (OCR) with a focus on table extractio...
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In employee turnover research and workforce management, addressing the impacts of suboptimal employee performance is crucial for organizations of all sizes and industries. Utilizing advanced machine learning classific...
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In employee turnover research and workforce management, addressing the impacts of suboptimal employee performance is crucial for organizations of all sizes and industries. Utilizing advanced machine learning classification models to predict potential employee resignations can enhance human resource departments’ intervention strategies, effectively mitigating attrition challenges. This research investigates the performance of various machine learning algorithms in classification tasks, focusing on their accuracy in predicting outcomes from a given dataset. Five models were evaluated: Random Forest, Support Vector Machine (SVM), Decision Tree, Gradient Boosting, and a Hybrid Model that integrates multiple algorithms. The goal was to identify which model yields the highest accuracy and to understand the strengths and weaknesses of each approach. Results showed that the Hybrid Model achieved the highest accuracy at 95.0%, suggesting that combining different algorithms effectively harnesses their strengths while mitigating individual weaknesses. The SVM accurately classified the instance with 88.6%, demonstrating its capability to manage complex decision boundaries in high-dimensional spaces. Both Random Forest and Gradient Boosting attained an accuracy of 87.3%, reflecting their ensemble techniques that enhance predictive performance by reducing overfitting and optimizing error reduction. In comparison, the Decision Tree classifier exhibited the least accuracy at 80.5%, highlighting its susceptibility to overfitting and limited generalizability. The superior performance of the Hybrid Model indicates a promising direction for future research, where integrating diverse algorithms could lead to more robust predictions. Overall, this study provides valuable insights for practitioners and researchers seeking to optimize model selection and improve predictive accuracy in their domains.
In high-density crowd, a unique visual motion effect called stop-and-go wave occurs, which could evolve to trampling and compression incidents. However, few computational models have been reported for stop-and-go wave...
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Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilin...
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Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf
Federated Learning (FL) is typically deployed in a client-server architecture, which makes the Edge-Cloud architecture an ideal backbone for FL. A significant challenge in this setup arises from the diverse data ...
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Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recogni...
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