Titlebook: Recent Trends in Image Processing and Pattern Recognition; 6th International Co KC Santosh,Aaisha Makkar,Ravindra Hegadi Conference proceed

mutineer

Farley, Hoenig and Yang, 2004; Chen, Lawler and Bae, 2005). There is a gap in research on multinationals from emerging economies (MEEs), since extant research has rarely examined management practices of MEEs in their subsidiaries in developed countries (Chang, Mellahi and Wilkinson, 2009). Since th

GIST

dysphagia

Building a Heroin Abuse Prediction Model: A Generalized Machine Learning Approachlgorithms are applied to different sets of features. Among the applied five algorithms, feature importance score is calculated for logistic regression, ensemble learning (gradient boosting) and random forest. The feature importance score for each feature is calculated for all applied algorithms k-ne

Deduct

Fallibility

Deep Learning Envisioned Accident Detection Systeme basis of the information inference and data mining techniques COCO (Common Objects in Context) dataset, YOLOv3 surpasses all other identification techniques considering runtime complexity, averaging an astounding 30 frames/second on a low-end system. It attains an Average Precision (AP) mark of 44

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Conscientious

Empathy-Driven Chatbots for the Arabic Language: A Transformer Based Approachnsively studied in English based chatbots, its application in other languages, such as Arabic, presents unique challenges and opportunities. Our research focuses on the development of an empathic chatbot that can understand and respond to user input in a contextually relevant and empathetic manner.

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憤憤不平

Preserving Accuracy in?Federated Learning via?Equitable Model and?Efficient Aggregationaset. We achieved smooth accuracy improvement by implementing the novel Equitable model, resulting in robust model development. As healthcare AI continues to advance, federated learning emerges as a critical tool for developing precise prediction models while preserving patient data privacy and alig

土產

Comparative Analysis of?CNN Pre-trained Model for?Stock Market Trend Predictiont contribution to the current body of literature on financial time series forecasting, providing a novel perspective on using pre-trained CNN models in the Indian Financial Sector. It carries important implications for future work and practitioners in the finance and investment sectors, offering a t