Nowadays, increasing efficiency in the service sector by objectively monitoring and tracking business workflows has become a critical requirement for the sustainability of businesses. Traditional monitoring methods are insufficient for providing sustainable performance analysis due to their time-consuming nature and reliance on subjective human judgment. The aim of this study is to develop a system that automatically detects and classifies employee behavior using computer vision and deep learning techniques. As part of the study, data was collected using a camera placed in a real bakery environment. Five basic classes were labeled on the created data set: cleaning, product interaction, computer use, phone use, and money interaction. The current YOLOv11 (You Only Look Once) architecture, which offers high speed and accuracy for object detection and classification, was used. According to the experimental results obtained from training the model, the system demonstrated high performance, achieving 0.9552 Precision, 0.9324 Recall, 0.9437 F1-Score, and 0.9644 mAP@50 values. These results demonstrate that the proposed system can detect employee behaviors in real-time with a high accuracy rate, allowing it to be used as an effective tool in workplace productivity enhancement and performance evaluation processes.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with a critical need for early and accurate diagnosis. In this research, various machine learning (ML) and deep learning (DL) techniques are employed for identifying reliable biomarkers for early-stage AD detection. This research paper presents a comprehensive approach utilizing a myriad of artificial intelligence (AI) and machine learning (ML), i.e., self-attention mechanisms with Convolutional Neural Networks (CNN) model has been proposed for predicting the onset of AD. The study has compared these models including Gaussian Naive Bayes (GNB), Decision Trees (DT), Random Forest (RF), XGBoost, Voting Classifier (VC) where the proposed Feature based CNN (F-CNN) network produces the highest classification accuracy, sensitivity, recall, AUC and F1 scores by utilizing datasets such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). The proposed approach holds promise for early detection and intervention in AD, potentially enabling clinicians to intervene before the onset of clinical symptoms. The findings of this research could significantly contribute to improving patient outcomes and advancing our understanding of Alzheimer's disease pathology.

Convolutional neural networks (CNN) have been successfully used in the classification of medical images. However, CNNs extract the features of the image, neglecting the spatial information between the objects since they evaluate each object separately and this makes it difficult to analyse images reliably. CapsNets partially overcomes these limitations by encapsulating the objects in the image and their feature parameters without losing the spatial information between objects. However, this advantage of CapsNet can become a disadvantage due to the fact that CapsNet stores a lot of information, which can lead to an increase in the number of parameters and the complexity of the network. In this study, a new method is proposed in which residual block and CBAM (Convolutional Block Attention Module) modules are used hybridly with CapsNet in order to classify medical images with high accuracy and reliability and, at the same time, to overcome this disadvantage of CapsNet by reducing the number of parameters. In this study, more features of the image are extracted by using the residual block instead of the convolutional layer of CapsNet. The attention mechanism is used to increase the effectiveness of important features and reduce the effect of unnecessary features. In addition, the residual block and CBAM module reduced the number of parameters in CapsNet by approximately 2 million. Brain tumour images and OCT (Optical Coherence Tomography) images were classified with the proposed model. The results of the study show that the proposed model achieves significant results when compared to other CapsNet-based studies in the literature.