Advances in computational intelligence最新文献

Developing accurate and robust prognostic prediction for classifying the risks of acute lymphoblastic leukemia (ALL) relapse is critical for patient treatment management and survival. However, the lack of clinical samples and linearity assumption remains a significant clinical challenge for achieving high accuracy for single-cell prognostics. Here, we explore the use of non-linear machine learning models with ex vivo sample perturbation as a data augmentation strategy to improve ALL relapse prediction. We hypothesize that treating each sample with ex vivo perturbation can be viewed as independent measurements, thus increasing the number of available observations for machine learning. We show that ex vivo sample stimulation combined with non-linear machine learning significantly improves the performance of ALL risk stratification from limited single-cell proteomic data.

Optimization algorithms are continuously evolving and considered as an active multidiscipline research area to design scalable solutions for complex optimization problems. Literature witnesses the constant effort by researchers to improve existing optimization algorithms or to develop a new algorithm to deal with single and multiple objective problems. This research paper presents a novel population-based, metaheuristic bio-inspired optimization algorithm. The algorithm contrived the propagation concept of antibiotic-resistant bacteria named as antibiotic-resistant bacteria propagation (ARBP) algorithm where properties of bacteria to acquire antibiotic resistance over time are used as a base concept. The optimization algorithm imitates the two prime mechanisms of horizontal gene transfer—Conjugation Gene Transfer Mechanism (CGTM) and Transformation Gene Transfer Mechanism (TGTM) to propagate antibiotic-resistant bacteria. CGTM and TGTM are used to explore the search space to handle single and multiple objective optimization problems. Conjugation mechanism is used for exploration of search space and exploitation concept is driven by transformation mechanism. The efficiency and importance of the ARBP algorithm are validated on varying classical and complex benchmark functions. An extensive comparative study is performed to detail the effectiveness of ARBP over other well-known swarm and evolutionary algorithms. This comparative analysis clearly depicts that the performance of ARBP is superior in terms of finding a better solution with high convergence as compared to other considered algorithms.

Fundus images are a powerful tool for detecting a variety of retinal disorders. Regular screening of the retina can lead to early detection of conditions like diabetic retinopathy, allowing for timely intervention and treatment. This study is focussed on developing an automated diagnostic system that can accurately detect different stages of diabetic retinopathy. Our approach involves leveraging pre-trained deep learning system to extract important features from fundus images. These features are then employed in a classification system that categorises the images into five stages of retinopathy based on ensemble algorithms. We employ ensemble algorithms like Random forest and XGBoost for classification to improve the accuracy and predictability of the forecast. This drives our focus on enhancing the interpretability and explainability of the model. We trained the model using publicly available fundus images of diabetic individuals for grading and compared the classification results obtained from ensemble techniques with those from deep learning models that used pre-trained weights and biases. The best performing ensemble showed an accuracy range of 0.63 to 0.79. Moreover, the accuracy of 0.96 in detecting the presence of retinopathy provides strong evidence of the approach’s effectiveness, contributing to its reliability, and potential for early diagnosis.

During the last decade, the Chinese housing market has seen fast expansion, and the importance of housing price forecasts has surely increased, becoming an essential problem for policymakers and investors. In this article, we explore Gaussian process regressions across different kernels and basis functions for monthly office real estate price index forecasts for ten major Chinese cities from July 2005 to April 2021 using cross-validation and Bayesian optimizations that could endow the forecast models with higher adaptability and better generalization performance. The models constructed offer precise out-of-sample forecasts from May 2019 to April 2021 with relative root mean square errors ranging from 0.0205 to 0.5300% across the ten price indices. Benchmark analysis against the autoregressive model, autoregressive-generalized autoregressive conditional heteroskedasticity model, nonlinear autoregressive neural network model, support vector regression model, and regression tree model suggests that the Gaussian process regression model leads to statistically significant higher accuracy. Our findings might be utilized independently or in conjunction with other projections to create views on office real estate price index movements and undertake further policy research.

An approach to improve workers’ productivity performance without neglecting their well-being should be investigated. To elucidate the effects of systematic micro-break on intellectual concentration performance, a controlled laboratory experiment generated 31 participants’ data when each participant was performing cognitive comparison tasks. Systematic micro-break was given for 20 s after 7.5 min of cognitive work, for a total of 25 min of work tasks. Each participant performed the task under both conditions with and without micro-break intervention in a counterbalanced design. Two quantitative evaluations were made: the answering time and concentration time ratio. A subjective symptom questionnaire and the NASA task load index were applied for analytical consideration. The average answering time indicates that the performance under the influence of micro-break tends to be more stable over time and that it mitigates performance degradation compared to the performance in a condition without micro-break. For concentration time ratio scores, no significant difference was found between conditions with micro-break and without micro-break. However, a tendency was apparent by which the concentration time ratio score was higher in a condition with micro-break, which suggests higher cognitive performance. The subjective symptoms questionnaire indicated no significant difference between conditions with and without micro-break. Weighted NASA task load index questionnaire results indicated significant difference between both conditions with lower workload scores in conditions with micro-break. Results obtained from this study suggest that the implementation of systematic micro-break can support workers’ performance stability over time. Therefore, systematic micro-break can be promoted as a promising strategy for work recovery.

Colour differentiation is crucial in machine learning and computer vision. It is often used when identifying items and objects based on distinct colours. While common colours like blue, red, green, and yellow are easily distinguishable, some applications require recognising subtle colour variations. Such demands arise in sectors like agriculture, printing, healthcare, and packaging. This research employs prevalent unsupervised learning techniques to detect printed colours on paper, focusing on CMYK ink (saturation) levels necessary for recognition against a white background. The aim is to assess whether unsupervised clustering can identify colours within QR-Codes. One use-case for this research is usage of functional inks, ones that change colour based on environmental factors. Within QR-Codes they serve as low-cost IoT sensors. Results of this research indicate that K-means, C-means, Gaussian Mixture Model (GMM), Hierarchical clustering, and Spectral clustering perform well in recognising colour differences when CMYK saturation is 20% or higher in at least one channel. K-means stands out when saturation drops below 10%, although its accuracy diminishes significantly, especially for yellow or magenta channels. A saturation of at least 10% in one CMYK channel is needed for reliable colour detection using unsupervised learning. To handle ink densities below 5%, further research or alternative unsupervised methods may be necessary.

This study builds upon the research conducted on the personal color decision system. It employs color space logic and reduces limitations associated with capturing photos, aiming to enhance the existing personal color decision method. The objective is to obtain more reliable and objective results for personal color analysis. Our proposed approach focuses on developing a comprehensive color selection framework by leveraging personal color databases and employing decision tree methods. The findings of this research suggest that utilizing personal color analysis in image creation can assist individuals in cultivating a positive and confident image, which holds significance in interpersonal relationships and social interactions.

The concrete mixture design and mix proportioning procedure, along with its influence on the compressive strength of concrete, is a well-known problem in civil engineering that requires the execution of numerous tests. With the emergence of modern machine learning techniques, the possibility of automating this process has become a reality. However, a significant volume of data is necessary to take advantage of existing models and algorithms. Recent literature presents different datasets, each with its own unique details, for training their models. In this paper, we integrated some of these existing datasets to improve training and, consequently, the models' results. Therefore, using this new dataset, we tested various models for the prediction task. The resulting dataset comprises 2358 records with seven input variables related to the mixture design, while the output represents the compressive strength of concrete. The dataset was subjected to several pre-processing techniques, and afterward, machine learning models, such as regressions, trees, and ensembles, were used to estimate the compressive strength. Some of these methods proved satisfactory for the prediction problem, with the best models achieving a coefficient of determination (R²) above 80%. Furthermore, a website with the trained model was created, allowing professionals in the field to utilize the AI technique in their everyday problem-solving.

This study introduces a web application designed to address the challenge of ensuring correct posture and performance in weightlifting exercises, with a particular focus on fundamental bodyweight movements targeting various body parts. The problem at hand primarily concerns beginners who require guidance for accurate exercise execution. To tackle this issue, the tool leverages a live camera in conjunction with the MediaPipe and OpenCV frameworks to extract key points from the user's body. It concentrates on seven core exercise postures, using these key points to calculate numerical values and angles. Users are required to adjust their view angles to activate the tool's pose estimation functions. An algorithm, based on predefined rules that determine posture thresholds and angles between three key points, is employed to detect incorrect postures, provide real-time feedback, and track repetition counts. The completion of all required stages is necessary to count a repetition as correct. Additionally, in this study, we have expanded the algorithm to include three new exercise postures: Bent over Dumbbell Row, Seated Triceps Press, and Dumbbell Fly. We have also adapted the system to detect the lying down view, which is essential for the Dumbbell Fly posture. The results of testing this application demonstrate further development potential, particularly in enhancing the model’s framework to accommodate challenges such as high light intensity, pale skin tones, and instances when a body part is obscured by an object.

Contemporary font design is a labor-intensive process. To address this, we utilize deep learning, specifically StyleGAN2-ADA and Real-ESRGAN, for automated Thai font generation. StyleGAN2-ADA incorporates adaptive discriminator augmentation (ADA) for image synthesis. By integrating Real-ESRGAN, font quality is enhanced. Our approach produces diverse, high-resolution fonts, as demonstrated in comparative experiments. In a survey with 50 participants, StyleGAN2-ADA without augmentation proves superior in legibility and visual appeal, while StyleGAN2-ADA with augmentation excels in diversity. This research highlights the efficiency of deep learning in creating high-quality Thai fonts and has implications for automated font design advancement.