Object detection serves as a crucial element in computer vision, increasingly relying on deep learning techniques. Among various methods, the YOLO series has gained recognition as an effective solution. This research enhances object detection by merging YOLOv7 with MobileNetv3, known for its efficiency and feature extraction. The integrated model was tested using the COCO dataset, which contains over 164,000 images across 80 categories, achieving a mAP score of 0.61. Additionally, confusion matrix analysis confirmed its accuracy, especially in detecting common objects such as 'person' and 'car' with minimal misclassifications. The results demonstrate the potential of the proposed model to address the complexities of real-world scenarios, highlighting its applicability in various scientific and industrial domains.

This paper presents a new classification technique to optimize the prediction of telemarketing target calls for selling bank long-term deposits. A Portuguese retail bank addressed, from 2008 until 2013, data on its clients, products and social-economic attributes including the effects of the financial crisis. An original set of 150 features has been explored and 21 features are retained for the proposed approach. This paper introduces a new technique that implicitly fosters most significant features and predicts the classes of clients according to the types of these features. Moreover, experiments showed that either these features are normalized or not, the proposed technique proved stable and accurate. To evaluate the obtained results, this paper compares them to those of most known machine learning models: Naive Bayes (NB), Decision Trees (DT), Artificial Neural Network (ANN) and Support Vector Machines (SVM). Thus, the proposed approach yielded the best performance in terms of f-measure, and it allowed reaching 60.12% of the subscribers.