ABSTRACT Accurate lesion segmentation in medical image analysis is critical for diagnosis and treatment planning. However, traditional U‐shaped architectures often struggle with large variation in lesion size and blurred boundaries. To address these challenges, a model called SFIF‐Net has been proposed in this study. In particular, SFIF‐Net strengthens feature interaction through four key components: a Hierarchical Feature Aggregation (HFA) module to enable cross‐layer feature fusion guidance; a Layer‐wise Feature Aggregation (LWFA) module in skip connections for dynamic multiscale fusion; an Interactive Feature Fusion (IFF) module equipped with a Spectral Feature Migration (SFM) component in the decoder to restore fine boundaries via spatial‐frequency fusion and a Multiscale Feature Enhancement (MFE) module applied across stages to improve multilevel feature learning. Experiments have been conducted on four public datasets, which are ISIC2018, BUSI, GlaS and CVC‐ClinicDB, and four metrics (Dice, mIoU, HD95 and Specificity) are used for evaluating the model performance. Experimental results show that SFIF‐Net outperforms other popular models. It achieves the highest average Dice score across all four datasets, outperforming the second‐best models by 1.02%, 1.22%, 0.06% and 0.06%, respectively. The source code is available at https://github.com/shen123shen/SFIF‐Net‐main .

Air quality (AQ) is crucial in the industry for financial as well as health and safety reasons. Thus, it is imperative to monitor and control workspace AQ. This paper proposed the integration of a smart AQ sensor system with self-powered autonomous airflow controllers (AACs) in a local exhaust ventilation (LEV) system. The AQ sensor system is integrated with artificial intelligence (AI) to improve sensing performance and reliability. The airflow energy harvester of the AAC could provide a local power source to meet the energy demand for sensing and AI. Sensor readings can be used to set appropriate airflow velocities for effective pollutant removal with optimized energy consumption of the LEV system. High volatile organic compounds (VOCs) and particulate matter (PM) levels after a long period of operation could be a sign of a faulty system. This improves the efficiency, resilience, and reliability of the LEV system as well as worker health and safety.

Brain-Computer Interface (BCI) make it possible to identify the physiological changes that are unnoticeable to the normal eye. BCI helps improve the diagnostic capacity to identify the drivers’ emotional states. Previous studies on the emotional states of drivers mostly concentrated on the drivers’ attentiveness, fatigue, and aggressive driving style. When driving a vehicle, drivers with negative emotions may make errors of judgment. One potential preventive measure is to examine the emotions that exist when operating a car. This paper discusses the architecture of the classification methods for the EEG signal when the drivers encounter several situations in the simulated environment. The classification focuses only on KNN and Fuzzy classifiers. The classifier will classify the emotions into five classes: fear, nervous, relax, surprise, and focus.

This paper presents a method to analyze table utilization and customer movements in fast food courts through video surveillance data using methods from machine learning. The system that has been proposed YOLOv8 for object detection, DeepSORT for object tracking and a custom status order algorithm to classify the table activities. A real-world implementation on existing infrastructure in SA such as the Tapah Southbound RSA (Rest and Service Area) Food Court were undertaken to address challenges posed by non-optimal camera angles, occlusion issues etc. Based on people (people) and objects (bowls, plates, bottles or cups), the system classifies table statuses to eat, drink, eat_drink sit-down empty. To improve the classification accuracy and to address this unstable detection issue, a status order hierarchy was introduce. This paper summarizes the system performance of occupancy rates, activity durations and customer patterns analysis while highlighting its limitations by potential embedding in started a business intelligence for food court operations. Consistent with our confirmation, we find high table utilization rates and clear behavioural patterns that can provide insights for service optimization or layout improvement. It also points to avenues for further improvement in data collection methods that cater to improved detection of nuanced activity under challenging visual settings.

Grapes, ranking among the top fruits globally, undergo essential grading processes to ensure quality and market readiness. However, conventional grading methods rely heavily on subjective human expertise, leading to inconsistencies and inefficiencies. To address this, we present a novel grape grading system integrating computer vision, artificial intelligence (AI), and IoT technologies. Our system utilizes deep neural networks (DNNs) based prediction model to accurately grade the grapes, and a sensing station equipped with cameras and weight sensors to capture images and weight data of grape bunches. Our research focuses on Shine Muscat grapes, a prominent variety in Japan. The system implements a multimodal grading approach that combines image and weight information. Our system offers portability, affordability, and operational versatility that prioritizes the safety of the grape bunch. Experimentation with different DNN architectures and input data configurations reveals the superiority of ResNet-18 for grading classification using multiple images from different angles, achieving $85.71 \%$ accuracy. On the other hand, when the weight information is included with the images, ResNet-50 performs the best with $82.86 \%$ accuracy. Additionally, we analyze mispredictions across grade classes, highlighting the model’s challenges in discerning subtle differences between closely ranked grades.

Individuals facing challenging and threatening situations significantly strain their minds and bodies. As a result, they may experience emotional, physical, or psychological stress. However, people perceive stress differently depending on how long or how severe their exposition to traumatic events is. Prolonged stress can cause significant and often inexplicable damage to both the body and mind. Because of that, early detection of human stress levels has become a prominent means of early diagnosis. Therefore, the major goal of this study is to investigate how human stress detection using Electroencephalography (EEG) which has been applied to EEG channel selection using Mutual Information (MI) with Minimum Redundancy Maximum Relevance (mRMR) (Mi$m R M R$) and the ensemble learning algorithm mainly using the method of bagging, boosting and stacking works in classifying different stress levels. On the other hand, the features were extracted using time-domain, frequency domain and timefrequency domain analysis containing relevant features related to stress data. Based on the experiment results, the ensemble learning method bagging performs better compared to the boosting method and can classify the data with the highest accuracy, highest F1 score, and highest precision, of 88 percent and highest precision of 94 percent. Hence, this shows the capability of the ensemble learning algorithms to classify stress EEG data that has been selected using (Mi-mRMR) algorithms as a means of early stress detection and diagnosis model.

This study presents an advanced automatic thrips counting and classification system, leveraging a novel SuperResolution (SR) technique, named KSVD_DR to enhance image analysis accuracy. We developed and validated detection models using a diverse dataset that included high-resolution scanned images and smartphone-captured images of blue and yellow traps, both with and without plastic wrap. This approach ensured robust performance across various real-world agricultural settings. The application of SR improved the detection accuracy from $66.5 \%$ to $\mathbf{8 9. 7} \%$, as measured by the mean Average Precision at $\mathbf{5 0 \%}$ Intersection over Union (mAP50). The overall testing accuracy achieved was $81.2 \%$, with specific accuracies of $80.3 \%$ for images with plastic wrap and $83.4 \%$ for those without confirming the system’s effectiveness in both laboratory and field conditions. Additionally, SR processing enhanced thrips classification accuracy from $58.5 \%$ to $\mathbf{6 5. 3 \%}$ across six distinct thrips classes, demonstrating its potential to refine species-specific identification. Future developments will focus on expanding outdoor data collection to validate and enhance system performance under varying environmental conditions and to improve detection accuracy at higher confidence levels. The study also aims to refine the classification model by incorporating more diverse data inputs and exploring advanced machine learning techniques, enhancing the ability to differentiate between thrips species effectively.

Messenger ribonucleic acid (mRNA) vaccines, despite their rapid degradation, play a critical role in pandemic response due to their high efficacy and swift production capabilities. Accurate prediction of mRNA vaccine degradation rates is vital for determining their shelf life and maintaining efficacy. This study presents a tetra-nitrogenous-base label encoding approach (4-ntb-lbA) integrated with a novel hyperparameter optimization (HPO) technique, named the Hyperparameter Optimization Penalizer (HOPeR), aimed at enhancing prediction precision. The state-of-the-art hybrid Dense-BiGRU-BiLSTM-BiLSTM (Hybrid_LGSS) model underwent rigorous testing to assess both the model’s performance and the proposed methodologies, broadening its interdisciplinary applications. Results indicate that the 4 -ntblbA method, which leverages bioinformatic data, substantially improves prediction reliability and accuracy while reducing error rates (Set_I: training loss $={0. 0 9 0 4}$, validation loss $=$ 0.0938; Set_II: training loss $=0.0141$, validation loss $=0.0145$), assessed with mean column-wise root mean square error (MCRMSE). The HOPeR approach demonstrated effectiveness across various models and HPO algorithms, including Particle Swarm Optimization (PSO), Bayesian Optimization with Gaussian Process (BOGP), and the RIME optimization algorithm (RIME), by minimizing the risk of suboptimal hyperparameter configurations while promoting fast convergence through penalization strategies. These findings validate the proposed approaches’ flexibility, applicability, and robustness across different algorithms. Additionally, beyond advancing mRNA vaccine degradation prediction, this research introduces a versatile framework for HPO with broad applicability. By fostering interdisciplinary integration and innovation, this paper significantly enhances the precision and efficiency of predictive models in bioinformatics, machine learning (ML), and beyond, thereby paving the way for future advancements across various scientific and engineering disciplines.

BACKGROUND: Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen. RESULTS: This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy. CONCLUSIONS: The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.