Enhancing Apple Fruit Quality Detection with Augmented YOLOv3 Deep Learning Algorithm

Busireddy Seshakagari Haranadha Reddy, R Venkatramana, L Jayasree — Mon, 10 Mar 2025 00:00:00 +0000

Precise apple detection is essential in the food manufacturing industry to provide quality control in production lines for differentiating between fresh and damaged apples. Various apple detection difficulties are found even before harvest in today's environment. However, post-harvest evaluation is still crucial for identifying apple species and assessing quality to expedite food processing procedures. This study presents a sophisticated detection model for multi-class apple recognition to distinguish between regular, damaged, and red delicious apples. The proposed model enhances Augment-YOLOv3 by integrating background removal through GrabCut, thereby improving object localization. Additionally, extra spatial pyramid pooling and a Swish activation function are incorporated to optimize feature retention during training. The YOLOv3 framework is refined using the Darknet53backbone with feature pyramid network-based spatial pooling, ensuring superior feature extraction before object detection. The final classification layer precisely distinguishes between apple categories. Experimental evaluations reveal that the Augment-YOLOv3 model achieves a mean average precision (mAP) of 98.20%, outperforming conventional YOLOv3 and YOLOv4models. The study leverages a newly curated Kaggle dataset, utilizing Google Colab with an NVIDIA Tesla K-80 GPU for inference, ensuring precise object localization and robust multi-object detection performance.

Dynamic Financial Sentiment Analysis and Market Forecasting through Large Language Models

Mon, 31 Mar 2025 00:00:00 +0000

Sentiment analysis is essential for determining public opinion, customer feedback, and decision-making in different disciplines. While traditional sentiment analysis investigates general sentiment classification, aspect-based sentiment analysis with the finer aspect of sentiment identification delves into specialized sentiments directed toward specific product or service elements. In finance, sentiment analysis provides excellent value in market-related conditions, including trend forecasting, stock price forecasting, and investment decisions. However, in current-day research, financial sentiment analysis fails in two respects: the ability to analyze vast and dynamic unstructured financial discourse and, second, to track the domain-specific connotations. In this paper, we tackle these problems by utilizing three advanced models for financial sentiment classification: FinBERT, GPT-4, and T5. While evaluation metrics considered precision, recall, and F1-score, the results show that GPT-4 proved the best by achieving 93.5% precision, 92.8% recall, and an F1-score of 93.1%. This indicates the incredible ability of GPT-4 in generalization between different financial contexts. FinBERT comes next in prediction since it holds up best in structured financial texts, achieving an F1-score of 90.8%. T5, while showing strong generative capacity, was inhibited in its recall and generalization. This points out each model's principal strength and weakness, suggesting that GPT-4 is preferably suited for real-time tracking of financial sentiment, FinBERT for more structured financial analysis, and T5 for generating financial sentiment and explainable AI-type applications. This work advances the field by furnishing selections for ideal model choices based on application necessities in financial sentiment analysis.

International Journal of Human Computations & Intelligence

Enhancing Apple Fruit Quality Detection with Augmented YOLOv3 Deep Learning Algorithm

Dynamic Financial Sentiment Analysis and Market Forecasting through Large Language Models