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Objective: The purpose of this research was to evaluate and select the best architecture among native convolutional neural network (CNN), MobileNetV2, ResNet50V2, and EfficientNetB0 for predicting the endpoint of the high shear wet granulation process, with accuracy as the main evaluation metric. Methods: The dataset was captured from an industrial camera using static image analysis and was manually labeled as “NOT READY” and “READY” according to the traditional endpoint method based on the mixer’s ampere point in the granulator. The dataset contained a total of 180 images, which were split between training and validation sets. Native CNN and TensorFlow Keras application programming interface (API) were utilized with MobileNetV2, EfficientNetB0, and ResNet50V2 as base feature encoders. Hyperparameters, such as final Fully Connected (FC) layer width, dropout rate, and learning rate, were optimized for binary classification using Keras hyper tuning. Results: The best was the native CNN, it was also the fastest among the three other models, taking only 20-30 ms per step for inference during runtime, though it requires 9000 ms time for training, the longest time among the models. It achieved an accuracy of 98%, and a validation accuracy of 97%. Conclusion: The system was able to determine when a wet granulation process has reached its endpoint based on live images from a camera after being trained on previously labeled data. The native CNN was the best model, offering the fastest runtime performance and the highest accuracy. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), longshort term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model's performance. [ABSTRACT FROM AUTHOR]

In Indonesia, the number of cases infected with Covid-19 peaked between mid-June and August 2021, resulting in the denial of hospital treatment to some people due to insufficient capacity. Therefore, people treat themselves by using natural ingredients. This study aims to identify plants that can treat Covid-19 with self-medication. This study took a description approach to investigate people using traditional herbs in self-medication for COVID-19 recovery. Snowball sampling is the approach used. Interviews and an online system (gform) were used to collect data. The number of samples collected allows for a more thorough analysis of the 104 respondents. The results showed that women (51.0%), people aged between 56 and 65 (52.9%), Banten residents (32.7%), secondary education (57. 7%), and people with comorbidities (86.5). %) were all interested in using traditional herbal medicine. Reduction of symptoms and results of laboratory tests were used to measure recovery. The most common comorbidities were hypertension (7-14 days to recovery), diabetes mellitus (15-21 days to recovery), and cardiovascular disease (7-14 days and 15-21 days to recovery). The Zingiberaceae family dominates traditional herbs. The most widely used traditional ingredients are Empon-Empon, which consists of red ginger, turmeric, nutmeg, aromatic ginger, bay leaves, lemongrass, galangal, cinnamon, and cloves. The average healing time is 7-14 days (32.7%). In conclusion people who utilized herbal medicines reported a speedy recovery and reduction in the severity of their symptoms. [ABSTRACT FROM AUTHOR]

The number of people around the world who have diabetes is about 422 million. Diabetes seriously affects the blood vessels in the retina, a disease called diabetic retinopathy (DR). The ophthalmologist examines signs through fundus images, such microaneurysm, exudates and neovascularisation and determines the suitable treatment for patient based on the condition. Currently, doctors require a long time and professional skills to detect DR. This study aimed to implement artificial intelligence (AI) to resolve the lack of current methods. This study implemented AI for detecting and classifying DR. AI uses deep learning, such the attention mechanism algorithm and AlexNet architecture. The attention mechanism algorithm focuses on detecting the pathological area in the fundus images, and AlexNet is used to classify DR into five levels based on the pathological area. This study also compared AlexNet architecture with and without attention mechanism. We obtained 344 fundus images from the Kaggle dataset, which contains normal, mild, moderate, severe and proliferative DR. The highest accuracy in this study is up to 91% and used the attention mechanism algorithm and AlexNet architecture. The experiment shows that our proposed method can provide results that can detect the pathological areas and effectively classify DR. [ABSTRACT FROM AUTHOR]

In Indonesia, dengue has become one of the hyperendemic diseases. Dengue consists of three clinical phases—febrile phase, critical phase, and recovery phase. Many patients have died in the critical phase due to the lack of proper and timely treatment. Therefore, we developed models that can predict the severity level of dengue based on the laboratory test results of the corresponding patients using Artificial Neural Network (ANN) and Discriminant Analysis (DA). In developing the models, we used a very small dataset. It is shown that ANN models developed using logistic and hyperbolic tangent activation function with 70% training data yielded the highest accuracy (90.91%), sensitivity (91.11%), and specificity (95.51%). This is the proposed model in this research. The proposed model will be able to help physicians in predicting the severity level of dengue patients before entering the critical phase. Furthermore, it will ease physicians in treating dengue patients early, so fatal cases or deaths can be avoided. [ABSTRACT FROM AUTHOR]

This paper investigates a deep learning method in image classification for the detection of colorectal cancer with ResNet architecture. The exceptional performance of a deep learning classification incites scholars to implement them in medical images. In this study, we trained ResNet-18 and ResNet-50 on colon glands images. The models trained to distinguish colorectal cancer into benign and malignant. We assessed our prototypes on three varieties of testing data (20%, 25%, and 40% of whole datasets). The empirical outcomes confirm that the application of ResNet-50 provides the most reliable performance for accuracy, sensitivity, and specificity value than ResNet-18 in three kinds of testing data. Upon three test assortments, we perceive the best performance value on 20% and 25% test sets with a classification accuracy of above 80%, the sensitivity of above 87%, and the specificity of above 83%. In this research, a deep learning method demonstrates the profoundly reliable and reproducible outcomes for biomedical image analysis. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase-IV (DPP-IV) inhibitor is one of the drug targets for the treatment of diabetes. Some classes of those drugs have dangerous side effects so is critical to develop safer drugs. By using rotation forest methods and in silico, it will be more efficient than conventional methods that require a lot more costs and are more time-consuming. One of in silico methods used in drug design is ligand-based virtual screening (LBVS). The interlocking structure capabilities are identified by the LBVS Process. The fingerprint is one of the structural interpretations. Molecular fingerprints are used as a criterion for LBVS in computational drug discovery. A circular fingerprint is found to improve LBVS performance. In this paper, we used the representation of ECFP and FCFP as a method to extract features, after which we used a Rotation Forest classifier to predict active and inactive compounds. The experiment result shows Rotation Forest has good prediction based on the different circular fingerprint and can successfully better classify with results of MCC being 85% and accuracy 92%. [ABSTRACT FROM AUTHOR]

Several of the DPP-4 inhibitors in the treatment of type 2 diabetes (T2DM) still have unsafe side effects in long-term use. It is necessary to develop a new DPP-4 inhibitor to minimize these unsafe side effects of the drug. QSAR is a model that can be used for the development of DPP-4 inhibitor drugs. The selection of a subset of DPP-4 inhibitor molecules by applying the clustering method can be made to improve the accuracy of the QSAR model. This study aims to select the corresponding DPP-4 inhibitor molecules by using the MiniBatchKMeans algorithm with Levenshtein distance and based on the log P criteria of 'Lipinski's Rule of 5' for QSAR modeling. The research began with the collection of DPP-4 inhibitor molecule data from the ChEMBL database site (https://www.ebi.ac.uk/chembl/) in CSV format. A representation of the molecular structure of the data is obtained from their SMILES features. Before running the clustering process, data in the form of SMILES is extracted into molecular fingerprints using several fingerprint generators, namely MACCS, ECFP, and FCFP. Clustering was carried out on five fingerprint datasets, including ECFP (with 4 and 6 diameters), FCFP (with 4 and 6 diameters), and MACCS (167 structural keys). The clustering process begins by determining the optimal number of clusters evaluated by applying the Davies-Bouldin index, the Silhouette coefficient, and the Calinski Harabasz score. Based on the clustering process, 1540 clusters were obtained from the minimum DBI cluster evaluation values of 0.545311, maximum SCO of 0.302842, and maximum CHS of 331.3942 from the MACCS fingerprint dataset. Based on log P criteria from 'Lipinski's Rule of 5', 1532 molecules were obtained for the molecular selection process that have log P values between -0.205 to 4.95. [ABSTRACT FROM AUTHOR]

Dengue is one of the endemic diseases in Indonesia. Dengue is being suffered by many people, regardless of their gender and age. Therefore, research about dengue based on dengue patients' data was conducted. There was a lot of information written in that data regarding the corresponding patients and the dengue they had suffered, such as gender, age, how long the patients were hospitalized, the symptoms they experienced, and laboratory characteristics results. Diagnosis of each of the corresponding patients based on their symptoms and laboratory characteristics results were also written in that data. The diagnoses were classified into three different clinical degrees according to the severity level, which is DF as the mild level, DHF grade 1 as the intermediate level, and DHF grade 2 as the severe level. In this research, data of the patients on the third day of being hospitalized was analyzed, because, on the third day, dengue is entering a critical phase. The objectives of this research were: to evaluate the performance of the models that were used to predict the correct class within the given dataset developed using Artificial Neural Network (ANN) classifier and Random Forest (RF) classifier separately, and to find a classifier that yielded the best performance. The results obtained from this research will be used in the development of a Machine Learning model that can predict the clinical degree of dengue in the critical phase, if the laboratory characteristics results are known, using a classifier that yielded the best performance. [ABSTRACT FROM AUTHOR]

Topic Detection or topic modeling is a process of finding topics in a collection of textual data. Detecting topic for a very large document collection hardly done manually. Therefore, we need an automatic method, one of which is a clustering-based method such as fuzzy cmeans (FCM). The standard initialization method of FCM is a random initialization which usually produces different topics for each execution. In this paper, we examine a nonrandom initialization method called nonnegative double singular value decomposition (NNDSVD). Besides the advantage of non-randomness, our simulations show that the NNDSVD method gives better accuracies in term of topic recall than both random method and another existing singular value decomposition-based method for the problem of sensing trending topic on Twitter. [ABSTRACT FROM AUTHOR]

The invention of graphical processing units (GPUs) has significantly improved the speed of long processes used in molecular dynamics (MD) to search for drug candidates to treat diseases, such as malaria. Previous work using a single GTX GPU showed considerable improvement compared to GPUs run in a cluster environment. In the current work, AMBER and dual GTX 780 and 970 GPUs were used to run an MD simulation on the Plasmodium falciparum enoyl-acyl carrier protein reductase enzyme; the results showed that performance was improved, particularly for molecules with a large number of atoms using single GPU. [ABSTRACT FROM AUTHOR]

Fundus image is an image that captures the back of the eye (retina), which plays an important role in the detection of a disease, including diabetic retinopathy (DR). It is the most common complication in diabetics that remains an important cause of visual impairment, especially in the young and economically active age group. In patients with DR, early diagnosis can effectively help prevent the risk of vision loss. DR screening was performed by an ophthalmologist by analysing the lesions on the fundus image. However, the increasing prevalence of DR is not proportional to the availability of ophthalmologists who can read fundus images. It can lead to delayed prevention and management of DR. Therefore, there is a need for an automated diagnostic system as it can help ophthalmologists increase the efficiency of the diagnostic process. This paper provides a deep learning approach with the concatenate model for fundus image classification with three classes: no DR, non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR). The model architecture used is DenseNet121 and Inception-ResNetV2. The feature extraction results from the two models are combined and classified using the multilayer perceptron (MLP) method. The method that we propose gives an improvement compared to a single model with the results of accuracy, and average precision and recall of 91% and 90% for the F1-score, respectively. This experiment demonstrates that our proposed deep-learning approach is effective for the automatic DR classification using fundus photo data. [ABSTRACT FROM AUTHOR]

In the modern era, the implementation of chatbot can be used in various fields of science. This research will focus on the application of sentence classification using the News Aggregator Dataset that is used to test the model against the categories determined to create the chatbot program. The results of the chatbot program trial by multimodal implementation applied four models (GRU, Bi-GRU, 1D CNN, 1D CNN Transpose) with six variations of parameters to produce the best results from the entire trial. The best test results from this research for the chatbot program using the 1D CNN Transpose model are the best models with detailed characteristics in this research, which produces an accuracy value of 0.9919. The test results on both types of chatbot are expected to produce sentence prediction results and precise and accurate detection results. The stages in making the program are explained in detail; therefore, it is hoped that program users can understand not only how to use the program by entering an input and receiving program output results that are explained in more detail in each sub-topic of this study. [ABSTRACT FROM AUTHOR]

Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However, with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data. [ABSTRACT FROM PUBLISHER]