Optimizing Facial Expression Recognition through Effective Preprocessing Techniques

Analyzing human facial expressions using machine vision systems is indeed a challenging yet fascinating problem in the field of computer vision and artificial intelligence. Facial expressions are a primary means through which humans convey emotions, making their automated recognition valuable for various applications including man-computer interaction, affective computing, and psychological research. Pre-processing techniques are applied to every image with the aim of standardizing the images. Frequently used techniques include scaling, blurring, rotating, altering the contour of the image, changing the color to grayscale and normalization. Followed by feature extraction and then the traditional classifiers are applied to infer facial expressions. Increasing the performance of the system is difficult in the typical machine learning approach because feature extraction and classification phases are separate. But in Deep Neural Networks (DNN), the two phases are combined into a single phase. Therefore, the Convolutional Neural Network (CNN) models give better accuracy in Facial Expression Recognition than the traditional classifiers. But still the performance of CNN is hampered by noisy and deviated images in the dataset. This work utilized the preprocessing methods such as resizing, gray-scale conversion and normalization. Also, this research work is motivated by these drawbacks to study the use of image pre-processing techniques to enhance the performance of deep learning methods to implement facial expression recognition. Also, this research aims to recognize emotions using deep learning and show the influences of data pre-processing for further processing of images. The accuracy of each pre-processing methods is compared, then combination between them is analysed and the appropriate preprocessing techniques are identified and implemented to see the variability of accuracies in predicting facial expressions. .

Improvement in Automated Diagnosis of Soft Tissues Tumors Using Machine Learning

Soft Tissue Tumors(STT)are a form of sarcoma found in tissues that connect,support,and surround body structures.Because of their shallow frequency in the body and their great diversity,they appear to be heterogeneous when observed through Magnetic Resonance Imaging(MRI).They are easily confused with other diseases such as fibroadenoma mammae,lymphadenopathy,and struma nodosa,and these diagnostic errors have a considerable detrimental effect on the medical treatment process of patients.Researchers have proposed several machine learning models to classify tumors,but none have adequately addressed this misdiagnosis problem.Also,similar studies that have proposed models for evaluation of such tumors mostly do not consider the heterogeneity and the size of the data.Therefore,we propose a machine learning-based approach which combines a new technique of preprocessing the data for features transformation,resampling techniques to eliminate the bias and the deviation of instability and performing classifier tests based on the Support Vector Machine(SVM)and Decision Tree(DT)algorithms.The tests carried out on dataset collected in Nur Hidayah Hospital of Yogyakarta in Indonesia show a great improvement compared to previous studies.These results confirm that machine learning methods could provide efficient and effective tools to reinforce the automatic decision-making processes of STT diagnostics.