Developing ISO 9001:2000 System: Applied Study on the Faculty of Computers and Information Technology

Applying ISO 9001:2000 in organization by using manual way faced many problems through the implementation process and beyond such as heavy loads of paperwork for management and documentations, unnecessary testing and documentation procedures and the need for a good communications system. This paper develops a web based-system by analyzing the manual system of applying ISO 9001:2000 in Faculty of Computers and Information Technology in King Abdulaziz University. The developed software facilitate applying ISO 9001:2000 in the organization by supporting all the basic processes, and it avoids the difficulties encountered in the manual way and provides more control in the documentation processes

SAFEPA: An Expandable Multi-Pose Facial Expressions Pain Assessment Method

Accurately assessing the intensity of pain from facial expressions captured in videos is crucial for effective pain management and critical for a wide range of healthcare applications. However, in uncontrolled environments, detecting facial expressions from full left and right profiles remains a significant challenge, and even the most advanced models for recognizing pain levels based on facial expressions can suffer from declining performance. In this study, we present a novel model designed to overcome the challenges posed by full left and right profiles—Sparse Autoencoders for Facial Expressions-based Pain Assessment (SAFEPA). Our model utilizes Sparse Autoencoders (SAE) to reconstruct the upper part of the face from the input image, and feeds both the original image and the reconstructed upper face into two pre-trained concurrent and coupled Convolutional Neural Networks (CNNs). This approach gives more weight to the upper part of the face, resulting in superior recognition performance. Moreover, SAFEPA’s design leverages CNNs’ strengths while also accommodating variations in head poses, thus eliminating the need for face detection and upper-face extraction preprocessing steps needed in other models. SAFEPA achieves high accuracy in recognizing four levels of pain on the widely used UNBC-McMaster shoulder pain expression archive dataset. SAFEPA is extended for facial expression recognition, where we show it to outperform state-of-the-art models in recognizing seven facial expressions viewed from five different angles, including the challenging full left and right profiles, on the Karolinska Directed Emotional Faces (KDEF) dataset. Furthermore, the SAFEPA system is capable of processing BioVid Heat Pain datasets with an average processing time of 17.82 s per video (5 s in length), while maintaining a competitive accuracy compared to other state-of-the-art pain detection systems. This experiment demonstrates its applicability in real-life scenarios for monitoring systems. With SAFEPA, we have opened new possibilities for accurate pain assessment, even in challenging situations with varying head poses

Facial Expressions Based Automatic Pain Assessment System

Pain assessment is used to improve patients’ treatment outcomes. Human observers may be influenced by personal factors, such as inexperience and medical organizations are facing a shortage of experts. In this study, we developed a facial expressions-based automatic pain assessment system (FEAPAS) to notify medical staff when a patient suffers pain by activating an alarm and recording the incident and pain level with the date and time. The model consists of two identical concurrent subsystems, each of which takes one of the two inputs of the model, i.e., “full face” and “the upper half of the same face”. The subsystems extract the relevant input features via two pre-trained convolutional neural networks (CNNs), using either VGG16, InceptionV3, ResNet50, or ResNeXt50, while freezing all convolutional blocks and replacing the classifier layer with a shallow CNN. The concatenated outputs in this stage is then sent to the model’s classifier. This approach mimics the human observer method and gives more importance to the upper part of the face, which is similar to the Prkachin and Soloman pain intensity (PSPI). Additionally, we further optimized our models by applying four optimizers (SGD/ADAM/RMSprop/RAdam) to each model and testing them on the UNBC-McMaster shoulder pain expression archive dataset to find the optimal combination, InceptionV3-SGD. The optimal model showed an accuracy of 99.10% on 10-fold cross-validation, thus outperforming the state-of-the-art model on the UNBC-McMaster database. It also scored 90.56% on unseen subject data. To speed up the system response time and reduce unnecessary alarms associated with temporary facial expressions, a select but effective subset of frames was inspected and classified. Two frame-selection criteria were reported. Classifying only two frames at the middle of 30-frame sequence was optimal, with an average reaction time of at most 6.49 s and the ability to avoid unnecessary alarms

Facial Expressions Based Automatic Pain Assessment System

Pain assessment is used to improve patients’ treatment outcomes. Human observers may be influenced by personal factors, such as inexperience and medical organizations are facing a shortage of experts. In this study, we developed a facial expressions-based automatic pain assessment system (FEAPAS) to notify medical staff when a patient suffers pain by activating an alarm and recording the incident and pain level with the date and time. The model consists of two identical concurrent subsystems, each of which takes one of the two inputs of the model, i.e., “full face” and “the upper half of the same face”. The subsystems extract the relevant input features via two pre-trained convolutional neural networks (CNNs), using either VGG16, InceptionV3, ResNet50, or ResNeXt50, while freezing all convolutional blocks and replacing the classifier layer with a shallow CNN. The concatenated outputs in this stage is then sent to the model’s classifier. This approach mimics the human observer method and gives more importance to the upper part of the face, which is similar to the Prkachin and Soloman pain intensity (PSPI). Additionally, we further optimized our models by applying four optimizers (SGD/ADAM/RMSprop/RAdam) to each model and testing them on the UNBC-McMaster shoulder pain expression archive dataset to find the optimal combination, InceptionV3-SGD. The optimal model showed an accuracy of 99.10% on 10-fold cross-validation, thus outperforming the state-of-the-art model on the UNBC-McMaster database. It also scored 90.56% on unseen subject data. To speed up the system response time and reduce unnecessary alarms associated with temporary facial expressions, a select but effective subset of frames was inspected and classified. Two frame-selection criteria were reported. Classifying only two frames at the middle of 30-frame sequence was optimal, with an average reaction time of at most 6.49 s and the ability to avoid unnecessary alarms

45S5 Bioglass paste is capable of protecting the enamel surrounding orthodontic brackets against erosive challenge

OBJECTIVES: This study aimed at evaluating the effect of using a 45S5 bioglass paste and a topical fluoride as protective agents against acidic erosion (resembling acidic beverage softdrinks intake) for enamel surrounding orthodontic brackets. MATERIALS AND METHODS: Sample of 21 freshly extracted sound incisor and premolar teeth was randomly divided into three equal groups: a bioglass group (Bioglass) (NovaMin, 5-mm average particle, NovaMin Technology), a Fluoride group (Fluoride) (Gelato APF Gel, Keystone Industries), and a control group (Control). Orthodontic brackets were bonded to the utilized teeth usingMIP (Moisture Insensitive Primer) and Transbond PLUS color change adhesive. All specimens were challenged by 1% citric acid for 18 min. The top enamel surfaces next to the orthodontic brackets were examined by SEM-EDS. Wilcoxon Signed-Rank test was used to compare the area covered by the 45S5 bioglass paste before/after erosion P < 0.05. RESULTS: 45S5 bioglass paste application resulted in the formation of an interaction layer that significantly resisted erosion challenge P < 0.05. The fluoride and control specimens showed signs of erosion of the enamel next to the orthodontic brackets (P < 0.05). CONCLUSION: 45S5 bioglass paste can efficiently protect the enamel surfaces next to orthodontic brackets for acidic erosion challenges