Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

Dental radiographs are essential in diagnosing the pathology of the jaw. However, similar radiographic appearance of jaw lesions causes difficulties in differentiating\ud cyst from tumor. Therefore, we conducted a development of computer-aided classification system for cyst and tumor lesions in dental panoramic images. The proposed system consists of feature extraction based on texture using the first-order\ud statistics texture (FO), Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). In this work, there were thirty three features which were\ud classified using Support Vector Machine (SVM) based classification. The result shows that differentiation of cyst from tumor lesions can achieve accuracy up to 87.18% and\ud Area Under the Receiver Operating Characteristic (AUC) curve up to 0.9444. When using the number of features used as predictors, the highest accuracy obtained were 84.62% using FO, 61.54% using GLCM, 76.92% using GLRLM, 84.62% using the combination of FO and GLCM, 87.18% using the combination of FO and GLRLM, 75.56% using the combination of GLCM and GLRLM, and 87.18% using the\ud combination of FO, GLCM and GLRLM. The highest AUC value was 0.9361 using FO, using GLCM was 0.8667, using GLRLM was 0.8722, using the combination of FO and GLCM was 0.9278, using the combination of FO and GLRLM was 0.9444, using the combination of GLCM and GLRLM was 0.8417, and using the combination of FO, GLCM and GLRLM was 0.9278. Based on the AUC value, the level of accuracy of this prediction can be categorized as ???Excellent???

Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech pattern

MULTISPECTRAL DORSAL HAND VEIN RECOGNITION BASED ON LOCAL LINE BINARY PATTERN

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%

Multispectral Dorsal Hand Vein Recognition Based On Local Line Binary Pattern

Nowadays, dorsal hand vein recognition is one of the most recent multispectral biometrics technologies used for the person identification/authentication. Looking into another biometrics system, dorsal hand vein biometrics system has been popular because of the privilege: false duplicity, hygienic, static, and convenient. The most challenging phase in a biometric system is feature extraction phase. In this research, feature extraction method called Local Line Binary Pattern (LLBP) has been explored and implemented. We have used this method to our 300 dorsal hand vein images obtained from 50 persons using a low-cost infrared webcam. In recognition step, the adaptation fuzzy k-NN classifier is to evaluate the efficiency of the proposed approach is feasible and effective for dorsal hand vein recognition. The experimental result showed that LLBP method is reliable for feature extraction on dorsal hand vein recognition with a recognition accuracy up to 98%

Classifying Cyst and Tumor Lesion Using Support Vector Machine Based on Dental Panoramic Images Texture Features

Dental radiographs are essential in diagnosing the pathology of the jaw. However, similar radiographic appearance of jaw lesions causes difficulties in differentiating cyst from tumor. Therefore, we conducted a development of computer-aided classification system for cyst and tumor lesions in dental panoramic images. The proposed system consists of feature extraction based on texture using the first-order statistics texture (FO), Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). In this work, there were thirty three features which were classified using Support Vector Machine (SVM) based classification. The result shows that differentiation of cyst from tumor lesions can achieve accuracy up to 87.18% and Area Under the Receiver Operating Characteristic (AUC) curve up to 0.9444. When using the number of features used as predictors, the highest accuracy obtained were 8462% using FO, 61.54% using GLCM, 76.92% using GLRLM, 84.62% using the combination of FO and GLCM, 87.18% using the combination of FO and GLRLM, 75.56% using the combination of GLCM and GLRLM, and 87.18% using the combination of FO, GLCM and GLRLM. The highest AUC value was 0.9361 using FO, using GLCM was 0.8667, using GLRLM was 0.8722, using the combination of FO and GLCM was 0.9278, using the combination of FO and GLRLM was 0.9444, using the combination of GLCM and GLRLM was 0.8417, and using the combination of FO, GLCM and GLRLM was 0.9278. Based on the AUC value, the level of accuracy of this prediction can be categorized as ‘Excellent’

Technical Data Analysis for Movement Prediction of Euro to USD Using Genetic Algorithm-Neural Network

in the foreign currency exchange (FOREX), a technical data analysis system for predicting currency movements is needed to help traders in decision making. Thus, this study proposes a system of technical data analysis to movement prediction of Euro to USD using Genetic Algorithm-Neural Network (GANN). To generate predicted value, genetic algorithm searching for the best value of Feed Forward Neural Network trained with the Neural Network method that produced a net to predict. The Validation of predicted results with GANN method based on the degree of accuracy as follows. RMSE values of open is 0.00043; The RMSE values of high is 0.00068; The RMSE value of low is 0.00075; and RMSE values of close is 0.00070

CONSISTENCY AND STRUCTURE ANALYSIS OF SCHOLARLY PAPERS USING BASED ON NATURAL LANGUAGE PROCESSING

<p><strong>Abstract</strong></p><p>This research presents a comprehensive similarity analysis of the consistency of authors in crafting papers and providing simple conclusions or meanings in a journal. Machine learning technique are employed to assess the similarity and interpretation of these sentences. The study attempts to mine text data, making it more structured and easily understood, introducing an approach to identifying relevant author consistency in an extensive collection by utilizing text analysis and the understanding of the meanings of new words using Natural Language Processing. In the interim, the weight analysis was conducted through the validation of TF-IDF and Cosine Similarity. By conducting an in-depth analysis across the corpus dataset consisting of 60 to 150 journal documents, this research utilizes classification patterns, preprocessing patterns, similarity calculations, and interpretation of results. This study is able to provide information about how consistent researchers are in writing assembled journals. The results underscore the effectiveness of NLP in processing natural language, enhanced by the incorporation of TF-IDF and Cosine Similarity, which refine the representation of relevance in journal content.</p&gt

Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext

Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small

A real time non-invasive cholesterol monitoring system

Hypercholesterolemia causes cardiovascular disease which is a disorder of the heart and blood vessels, one of the most significant causes of death in the world, and this needs to be anticipated by monitoring blood cholesterol levels regularly. The current method of monitoring blood cholesterol levels is using invasive technique by collecting blood samples. A simple device is needed to measure blood cholesterol levels that can be done without collecting blood samples. This study aims to develop a non- invasive technique for monitoring blood cholesterol levels using sensors utilizing infrared light absorption in body tissues, in order to simplify measuring blood cholesterol levels regularly for patients with hypercholesterolemia. The application of a non-invasive technique focused on developing a total blood cholesterol monitoring device using an infrared sensor with IR LED - 940nm wavelength as a transmitter. A photodiode was used as a detector with the wavelength range of 400-1100 nm and a microcontroller as the minimum system for controlling the value of the output voltage in the form of digital data and then converted onto total blood cholesterol. The measurement results using non-invasive technique was compared to the results using invasive technique