Jackfruit (Artocarpus integer) and Cempedak (Artocarpus heterophyllus) are two different Southeast Asian fruit species from the same genus that are quite similar in their external appearance, therefore, sometimes difficult to be recognized visually by humans, especially in the form of pictures. Convolutional neural networks (CNN) and transfer learning can provide an excellent solution to recognize fruits, where the methods are known to be able to classify objects with high accuracy. In this study, several models were proposed and constructed to recognize the Jackfruit and Cempedak using a deep convolutional neural network (DCNN). We proposed our custom-made own CNN model and modify five transfer learning models on pre-trained VGG16, VGG19, Xception, ResNet50, and InceptionV3. The experiment used our own dataset and the result showed that the proposed CNN architecture was able to provide an accuracy between 89% to 93.67% compared to the other CNN transfer learning.

The factors affecting information systems and technology have become a growing topic in many disciplines. This study focuses on factors affecting the adoption of digital dental technologies and dental informatics in dental practice. There are limited studies in the literature on factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI). Understanding the factors is important for the success of the adoption of technologies. Therefore, this study aims to fill that gap. This paper reviews peer-reviewed literature to analyze factors that affect the adoption of digital dental technologies (DDT) and dental informatics (DI) and critically examines an array of technology acceptance models to unveil the underlying determinants of DDT and DI adoption. Usability and practical considerations, work efficiency factors, socioeconomic and organizational aspects, aspects of the learning curve, and system design are the most important factors influencing the adoption of digital dental technologies and dental informatics. The study results identified the conceptual framework for the factors affecting the adoption of digital dentistry.

The hospitality industry faces a persistent challenge in accurately gauging customer sentiment from online reviews, often resulting in a disparity between ratings and actual experiences. Conventional hotel rating systems struggle to capture the nuanced opinions expressed in user-generated content. To address this issue, our research presents a novel hotel rating model leveraging natural language processing (NLP) techniques. This model directly analyzes and synthesizes customer feedback from online reviews to generate dynamic and precise hotel ratings. By incorporating Word2Vec, term frequency-inverse document frequency (TF-IDF), and a three-layer bidirectional long short-term memory (Bi-LSTM) network with a lightweight self-attention mechanism, our approach adeptly identifies sentiment tendencies within diverse review data. The proposed hotel rating model outperformed baseline models across experiments, demonstrating superior adaptability, scalability, and robustness. Notably, it achieved an accuracy of 0.9123 and an area under the curve (AUC) of 0.9536 on diverse datasets, and its performance consistently improved with increased data volumes, reaching 0.9376 accuracy and 0.9612 AUC at 75% data volume. Its low variability in performance metrics across multiple runs highlights its reliability for sentiment analysis in the hospitality sector.

Objectives: The main aim of this study was to use text mining on social media to analyze information and gain insight into the health-related concerns of thalassemia patients, thalassemia carriers, and their caregivers.Methods: Posts from two Facebook groups whose members consisted of thalassemia patients, thalassemia carriers, and caregivers in Malaysia were extracted using the Data Miner tool. In this study, a new framework known as Malay-English social media text pre-processing was proposed for performing the steps of pre-processing the noisy mixed language (Malay-English language) of social media posts. Topic modeling was used to identify hidden topics within posts shared among members. Three different topic models—latent Dirichlet allocation (LDA) in GenSim, LDA in MALLET, and latent semantic analysis—were applied to the dataset with and without stemming using Python.Results: LDA in MALLET without stemming was found to be the best topic model for this dataset. Eight topics were identified within the posts shared by members. Of those eight topics, four were newly discovered by this study, and four others corresponded to the findings of previous studies that used an interview approach.Conclusions: Topic 2 (the challenges faced by thalassemia patients) was found to be the topic with the highest attention and engagement. Healthcare practitioners and other concerned parties should make an effort to build a stronger support system related to this issue for those affected by thalassemia.

Evaluating service quality presents a multicriteria decision‐making (MCDM) challenge, involving numerous factors that contribute to its complexity. Traditional approaches to assessing service quality frequently struggle to encapsulate the ambiguity and uncertainty in user experiences, particularly when confronted with intricate, multidimensional indicators of service quality. In this paper, we introduce two novel distance measures based on logarithmic function for interval‐valued Pythagorean fuzzy sets (IVPFSs). The important properties of the new measure are not only proved but also verified by numerical examples. On this basis, the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is extended to the interval‐valued Pythagorean fuzzy environment to evaluate the service quality of university libraries. The research results show that the proposed method has significant results in improving the accuracy of evaluation and coping with complex index systems. Finally, comparative analysis and sensitivity analysis are carried out. The results of these analyses further validate the effectiveness of the proposed method, which provides a new and more reliable way for service quality evaluation.

Currently, the discrete Hopfield neural network deals with challenges related to searching space and limited memory capacity. To address this issue, we propose integrating logical rules into the neural network to regulate neuron connections. This approach requires adopting a specific logic framework that ensures the network consistently reaches the lowest global energy state. In this context, a novel logic called major 1,3 satisfiability was introduced. This logic places a higher emphasis on third-order clauses compared to first-order clauses. The proposed logic is trained by the exhaustive search algorithm, aiming to minimize the cost function toward zero. To evaluate the proposed model effectiveness, we compare the model's learning and retrieval errors with those of the existing non-systematic logical structure, which primarily relies on first-order clauses. The similarity index measures the similarity benchmark neuron state with the existing and proposed model through extensive simulation studies. Certainly, the major random 1,3 satisfiability model exhibited a more extensive solution space when the ratio of third-order clauses exceeds 0.7% compared to first-order clauses. As we compared the experimental results with other state-of-the-art models, it became evident that the proposed model achieved significant results in capturing the overall neuron state. These findings emphasize the notable enhancements in the performance and capabilities of the discrete Hopfield neural network.