In agriculture, crop yield estimation is essential; producers, industrialists, and consumers all benefit from knowing the early yield. Manual mango counting typically involves the utilization of human labor. Experts visually examine each sample to completethe process, which is time-consuming, difficult, and lacks precision. For commercial mango production to produce high-quality fruits from the orchard to the consumer, a quick, non-destructive, and accurate variety classification is required. Because of its effectiveness in computer vision, a convolutional neural network—one of the deep learning techniques—was chosen for this investigation. For yield prediction, a total of eight popular mango cultivars were utilized. A comparison with previously trained models was used to assess the proposed model. The performance of the classifiers was evaluated using evaluation metrics such as accuracy, loss, area under the receiver operating characteristic curve score, precision, recall, F1-score, sensitivity, specificity, positive predictive value, negative predictive value, and Cohen’s Kappa performance measure. In terms of performance evaluation criteria, it was found that the proposed approach outperformed the pre-trained models. The suggested model achieved 98.85% accuracy in the test set, which had 800 images. This outcome demonstrates the tangible applicability of the proposed methodology for mango crop estimation.

In Indonesia, natural fibers are extensively utilized as essential raw materials for various human needs. These natural fibers find significant application in handicrafts, particularly in small and medium enterprises (SME) within the hand-icraft industry. Agel, a prominent natural fiber, is obtained from drying gebang leaves and plays a vital role in the local economy of the Kulon Progo region in Yogyakarta, Indonesia. Currently, the production process of raw materials to become fiber relies on conventional methods, primarily sun-drying, which often gives rise to numerous challenges such as temperature fluctuations and weather dependencies. Moreover, the physical posture adopted by workers during the drying process is ergonomically unfavorable, as they must bend over repeatedly to turn the agel fibers being dried under the sun. After recognizing these issues, it becomes imperative to develop a sustainable dryer machine equipped with advanced technology that enhances productivity while prioritizing employee ergonomics. This study employs the research and development (R&D) method, which encompasses analysis, design, development, implementation, and evaluation stages. The primary objective of this research is to design, fabricate, and test a dryer machine utilizing a sustainability automation system integrated with Internet of Things (IoT). The outcome of this research is a dryer machine that can effectively dry agel leaves within a significantly reduced timeframe of 2-4 hours, with a maximum capacity of 10 kg per cycle. This achievement surpasses the conventional method, which typically takes 5-6 days to produce 10 kg of dried agel fibers.

Deep learning methods with convolutional neural network (CNN) models have increasingly been applied to facial expression recognition. However, due to the recent pandemic, many individuals wear masks for work or health reasons, obstructing the complete visibility of their faces. This can impact social interactions, particularly in areas involving facial expression cues like the mouth. This study explores the application of CNNs in identifying facial expressions obscured by masks, focusing on the VGG16 and MobileNet architectures. Additionally, the research investigates the effects of data augmentation, including geometric and brightness augmentation, on the accuracy of facial expression classification. The findings indicate that the VGG16 architecture with cross-validation (VGG16-FLCV) outperforms MobileNet-FLCV in recognizing and classifying masked facial expressions. Data augmentation, particularly brightness augmentation, significantly enhances CNN model performance. For the VGG16-FLCV architecture, the brightness range (1.00, 1.25) yields the best accuracy, with a training accuracy of 81.73% and a validation accuracy of 70.71%. The most optimal brightness ranges for VGG16-FLCV are in the dark category (0.25, 0.50), (0.50, 0.75), and (0.75, 1.00), as well asthe bright category (1.00, 1.25). Meanwhile, MobileNet-FLCV with brightness ranges (0.25, 0.50), (0.50, 0.75), (0.75, 1.00), (1.00, 1.25), and (1.25, 1.50) can be used as alternative brightness ranges without significant accuracy degradation. These findings provide valuable insights for improving the accuracy of masked facial expression recognition by applying appropriate data augmentation techniques.

Globally, one of the major concerns in women’s health issues is gynecological disorders such as cancer, which needs to be observed at its early stage. With traditional approaches, it is quite difficult to detect such disorders at its early stages. Therefore, more advanced tools need to be integrated. This paper focuses the advancements of artificial intelligence (AI) and machine learning (ML), exploring their potential in the early detection and diagnosis of these disorders. This paper presents a systematic meta-analysis of AI/ML approaches employed in the diagnosis of gynecological disorders using medical imaging modalities such as magnetic resonance imaging (MRI), ultrasound, etc. The flow for systematic meta-analysis is based on designing the research objective, selection andsearching approach with inclusion and exclusion strategy; quality assessment is performed then; and finally, discussion of interpretations is also presented. This paper investigates how ML algorithms can extract characteristics from MRI images and how to use ML to extract and recognize the features from medical images such as MRI, ultrasound, computed tomography (CT) scans, etc. for early detection of gynecological tumors and provision of more personalized risk assessment. However, it is observed that there is a significant impact of advancement of AI/ML on medical technology in the future. Therefore, this paper presents a significant contribution for future medical applications and innovations.

The traditional process of smoking fish, which is widely used in coastal regions, poses significant challenges due to its labor-intensive nature, constant supervision, and difficulty maintaining stable temperatures. These issues often result in inefficiencies, inconsistent product quality, and potential safety hazards. Given the importance of the smoked fish industry in sustaining local economies in the coastal area of Indonesia, there is a critical need for more advanced, reliable, and efficient methods of fish smoking. This study addresses these challenges by developing an Internet of Things (IoT)-integrated monitoring and control system for the smoked fish machine. This study was conducted to develop a monitoring and control system for machines which includes turning on/off machine components, temperature monitoring, and blower revolutions per minute (RPM) control. The results of the study showed that the implementation of IoT can activate machine components such as a blower, a servo motor, and a light. Moreover, IoT can monitor the machine temperature from a smartphone in real-time by integrating a temperature sensor. The temperature difference between the sensor and the analog thermometer was found to be 0.1 -0.5oC, proving that the temperature on the IoT system is not very different from the analog thermometer. Furthermore, the blower RPM control results showed that the system could maintain the temperature in the optimal range (75 -90oC) for smoking the fish, with a maximum deviation of 1°C, and the blower RPM can be adjusted through the control in the IoT system. In general, the use of IoT can simplify machine operation for users.

This study examines the impact of intellectual capital enabled knowledge capability management on innovation ambidexterity and the moderating role of intangible resources advantage through the lens of open innovation. The sample consists of 105 companies in the Thai food industry. To enhance understanding of how companies can achieve success in innovation, an interesting result of the study is that it helps companies explore new knowledge and leverage existing or new knowledge to innovate more continuously. Based on the presented findings, intangible resources advantage plays a positive moderating role in this relationship: knowledge capability management and intangible resources advantage work together to foster innovation ambidexterity.

Atherosclerotic disorders, such as peripheral artery disease (PAD), have a significant negative impact on patient outcomes. Inadequate treatment and poor detection rates can result in cardiovascular complications and limb loss. There is great promise for improving the detection and treatment of PAD and other medical disorders through machine learning (ML) and artificial intelligence (AI) techniques. This paper highlights the use of field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs) to implement the fundamental ideas of AI and ML, specifically in the treatment of PAD. It emphasizes how these technologies can enhance drug selection, improve patient care, and refine disease phenotyping. This paper also describes how the integration of AI and ML with FPGA and ASIC technology can provide accurate and effective solutions to complex medical challenges, representing a significant breakthrough in medical analytics.

Machine and deep learning methods have gained significant prominence in the healthcare industry, particularly for the prediction of cardiac diseases. The increasing prevalence of heart-related diseases underscores the necessity for proactive and accurate healthcare interventions. Machine learning, a data-driven approach, can play a crucial role in recognizing and addressing cardiovascular risks. To achieve this, researchers have utilized a range of classification techniques, such as Support Vector Machines, Random Forests, and Naive Bayes, to unravel the intricate aspects of heart disease prediction. Additionally, ensemble learning techniques, especially Stacking Technique, is employed to further enhance predictive accuracy. However, the ensemble approach has certain limitations. Therefore, confusion matrices are utilized for thorough evaluation and validation, offering better classifier performance. As research advances, prediction models aim to achieve higher accuracy and generalizability. Insights from confusion matrices can help researchers to make more robust and dependable predictions.Future research will investigate the deep learning models to detect subtle patterns in electrocardiogram data, with the aim of enabling earlier identification of cardiovascular conditions. Additionally, the integration of wearable sensor technologies holds promise for continuous risk monitoring and the development of personalized healthcare interventions. These technological advancements possess the potential to fundamentally transform the field of cardiac care, facilitating earlier disease diagnosis and substantially enhancing patient prognosis. In conclusion,the convergence of machine learning and deep learning models heralds a novel era for precision medicine, where data-driven insights empower stakeholders to tackle formidable challenges with unparalleled effectiveness.

Malignant growth of the gastrointestinal (GI) tract is among the leading causes of death worldwide. Research indicates that almost 40% of people worldwide suffer from long-term digestive issues. According to a study published in the United European Gastroenterology Journal, digestive disorders have increased since 2000. Digestive disorders continue to be a major cause of death, even with a slight decline. The World Health Organization’s Mortality Database reported huge death rates every year due to GI diseases. From that report, the need to accurately detect GI tract malignant in low-cost and error-prone labor must be developed. This work introduces MNET Gastrointestinal Disease Detection (MNETGIDD), which is a complete identification model for multi-gastrointestinal disease discovery from clinical images. MNETGIDD model uses the Gastrolab dataset with endoscopic images, acting as pipelines that are pre-processed and segmented to identify the affected areas. This proposed approach aims to enhance image quality and facilitate accurate segmentation and classification through a pipeline process, initially preprocessing with techniques such as text removal, illumination enhancement, and fuzzy histogram equalization. During segmentation, Otsu segmentation based on Krill-Herd optimization was used to identify the affected area. The MNETGIDD model incorporates the MobileNetV2 architecture, designed for a lightweight classification model working under resource-constrained environments. According to the tests, the MNETGIDD model exhibits high sensitivity and specificity, often outperforming human experts. In terms of accuracy, the model achieved 96.349%, a precision of 96.25 %, and a recall of 97.08%. This deep learning system has the potential to revolutionize gastrointestinal disease diagnostics and screening by automating key steps and improving patient outcomes.