Evolutionary Computing (EC), inspired by biological evolution, is transforming the artificial intelligence (AI) world by optimizing model architectures, tuning hyperparameters, and solving complex problems. This paper explores different EC applications in two key areas of Computer Science and Engineering Education. Evolutionary methods enhance automation, improve efficiency, decrease time-intensive manual tasks, and eliminate the necessity of expert knowledge. Furthermore, evolutionary-based publication trends from 2010 to 2025 globally and then primarily in the USA and China are analyzed. This review highlights the impressive and growing impact of EC techniques in AI on solving real-world problems. Because of AI’s consistent contribution in various fields and devices, evolutionary AI offers promising approaches to tackle practical challenges.

Protein-language systems are often evaluated by whether they generate plausible biological text, but a structural question has a sharper semantics: it denotes a measurement in a 3D coordinate system. We introduce ProtStructQA, an executable benchmark for protein structural question answering in which each natural-language question is generated from a hidden typed domain-specific language (DSL) program and the answer is obtained by executing that program on an AlphaFold-predicted structure. ProtStructQA releases 382.2K questions covering confidence, distances, predicted aligned error (PAE), solvent exposure, secondary structure, topology and contacts, and held-out compositions: a 330K active benchmark over 10K proteins from four species, plus a 52.2K hard-negative robustness pool. Without fine-tuning, we evaluate Qwen3 models from 0.6B to 8B under direct prompting, chain-of-thought, grammar-constrained executable voting, executable voting with chain-of-thought, and multi-turn ReAct-style tool use, and replicate the headline finding on Gemma-3-1B and Gemma-3-12B. We find a capability-dependent denotation threshold between Qwen3-1.7B and Qwen3-4B: below it, tool-mediated ReAct dominates because models often fail to produce executable denotations; above it, chain-of-thought flips from mostly harmful to strongly beneficial and becomes the strongest strategy on most splits. Parse-failure and family-level analyses show that the threshold is a transition from unparseable language to executable structural denotation, while grammar and execution remain selectively valuable for PAE and secondary-structure queries. ProtStructQA reframes scientific QA as compilation from language to measurement and provides a diagnostic testbed for when language models can map words to executable 3D structural measurements.

The concept of artificial intelligence has been discussed in society and science for a long time, yet until recently the application of these methods in forestry has mainly focused on heuristic search and simulation for the development of management plans, and on classification methods to describe resource conditions and classes. However, over the last 20 years a number of advancements have been offered in the forestry literature that suggest highly complex resource management issues can be more closely examined using sophisticated algorithms and data processing techniques. This review involved a bibliographic search of peer-reviewed literature and a condensed summary of six main areas of forest resource management where advancements have taken place. Included in the analysis is the use of natural language processing for identifying trends in the literature and the development of a lifetime recognition index (λ) related to citation frequency over the life of the published work. Brief, salient examples are provided to illustrate the complexity of efforts. While the recent hype surrounding artificial intelligence has captured the attention of society and industry, there remain significant challenges for its seamless and smooth application to contemporary forest management issues. These challenges center on the development of timely and efficient estimates of forest composition, on the identification of forest health issues and forest disturbances, on the integration of large amounts of data from non-traditional sensors, and on the administration of forests and management of forest operations.

Previous work used machine learning (ML) to estimate past and current alfalfa yields and showed that domain adaptation (DA) with data synthesis shows promise in classifying yields as high, medium, or low. The current work uses similar techniques to forecast future alfalfa yields. A novel technique is proposed for forecasting alfalfa time series data that exploits stationarity and predicts differences in yields rather than the yields themselves. This forecasting technique generally provides more accurate forecasts than the established ARIMA family of forecasters for both univariate and multivariate time series. Furthermore, this ML-based technique is potentially easier to use than the ARIMA family of models. Also, previous work is extended by showing that DA with data synthesis also works well for predicting continuous values, not just for classification. The novel scale-invariant tabular synthesizer (SITS) is proposed, and it is competitive with or superior to other established synthesizers in producing data that trains strong models. This synthesis algorithm leads to R scores over 100% higher than an established synthesizer in this domain, while ML-based forecasters beat the ARIMA family with symmetric mean absolute percent error (sMAPE) scores as low as 12.81%. Finally, ML-based forecasting is combined with DA (ForDA) to create a novel pipeline that improves forecast accuracy with sMAPE scores as low as 9.81%. As alfalfa is crucial to the global food supply, and as climate change creates challenges with managing alfalfa, this work hopes to help address those challenges and contribute to the field of ML.

In this study, we explore the potential of orthogonal polynomial functions as activation functions within the SWAG neural network architecture. By employing Chebyshev, Legendre, Hermite polynomials and and sinusoidal functions, we conduct a rigorous comparative analysis to evaluate the performance enhancements across a range of benchmark datasets. Furthermore, this paper investigates the influence of factorial coefficients on the performance of these models, providing a nuanced understanding of how these mathematical modifications affect learning dynamics and model efficacy. Our results offer insights into optimizing neural network architectures through advanced mathematical functions.

This study provides a comprehensive survey of the top-performing research papers in the field of time series prediction, offering insights into the most effective machine learning techniques, including tree-based, deep learning, and hybrid methods. It explores key factors influencing the model performance, such as the type of time series task, dataset size, and the time interval of historical data. Additionally, this study investigates potential biases in model development and weighs the trade-offs between the computational costs and performance. A detailed analysis of the most used error metrics and hyperparameter tuning methods in the reviewed papers is included. Furthermore, this study evaluates the results from prominent forecasting competitions, such as M5 and M6, to enrich the analysis. The findings of this paper highlight that tree-based methods like LightGBM 4.6.0 and deep learning methods like recurrent neural networks deliver the best performance in time series forecasting, with tree-based methods offering a significant advantage in terms of their computational efficiency. This paper concludes with practical recommendations for approaching time series forecasting tasks, offering valuable insights and actionable strategies that can enhance the accuracy and reliability of predictions derived from time series data.

Machine learning (ML) models were used to determine the moisture content (MC) for multiple grains and seeds after training on a large dataset obtained through several decades of research. The dataset consisted of attenuation, phase shift, dielectric properties, frequency, bulk density, and sample thickness collected for corn, barley, sorghum, soybeans, and wheat. In this article, a new ML-based approach for calibrating microwave sensors for rapid and nondestructive determination of MC in multiple grains and seeds is proposed. For this purpose, a single model trained on multiple grains and seeds was developed and allowed moisture determination in individual grain or seed samples. Performance of this model is investigated and compared with models trained on an individual grain or seed by using different algorithms, including artificial neural network (NN), support vector regression (SVR), ElasticNet, among other algorithms. In addition, these models were tested on new data collected for corn, wheat, and soybeans at 24 degrees C with MC ranging from 7.89% to 20.19% and frequencies between 5 and 15 GHz. The lowest mean absolute error (MAE) of MC was obtained with frequencies between 8 and 12 GHz for most models. Training when using the dielectric properties, frequency, and grain type with a single SVR-based model had the lowest error at 9 GHz for soybeans and corn. The SVR-based model showed no drawbacks and a slight improvement predicting MC using a single model when training over all grains and seeds compared with training several models over each grain individually.

Deep learning techniques have demonstrated significant capabilities across numerous applications, with deep neural networks (DNNs) showing promising results. However, training these networks efficiently, especially when determining the most suitable nonlinear activation functions, remains a significant challenge. While the ReLU activation function has been widely adopted, other hand-designed functions have been proposed. One such approach is the trainable activation functions. This paper introduces a novel neural network design, the SWAG. In this structure, instead of evolving, activation functions consistently form a polynomial basis. Each hidden layer in this architecture comprises k sub-layers that use polynomial activation functions adjusted by a factorial coefficient, followed by a Concatenate layer and a layer employing a linear activation function. Leveraging the Stone-Weierstrass approximation theorem, we demonstrate that utilizing a diverse set of polynomial activation functions allows neural networks to retain universal approximation capabilities. The SWAG algorithm’s architecture is then presented, where data normalization is emphasized, and a new optimized version of SWAG is proposed, which reduces the computational challenge of managing higher degrees of input. This optimization harnesses the Taylor series method by utilizing lower-degree terms to compute higher-degree terms efficiently. This paper thus contributes an innovative neural network architecture that optimizes polynomial activation functions, promising more efficient and robust deep learning applications.

Body temperature is a critical indicator of the health and productivity of egg-laying chickens and other domesticated animals. Recent advancements in thermography allow for precise surface temperature measurement without physical contact with animals, reducing animal stress from human handling. Gold standard temperature analysis via thermography requires manual selection of limited points for an object of interest, which could be time-consuming and inadequate for representing the comprehensive thermal profile of a chicken's body. The objective of this study was to leverage and optimize a zero-shot artificial intelligence technology for the automatic segmentation of individual cage-free laying hens within thermal images, providing insights into their overall thermal conditions. A zero-shot image segmentation model (Segment Anything, "SAM") was modified by replacing manual selections of target points with automatic selection of the initial point using pre-processing techniques (e.g., thresholding) in each thermal image. The model was also incorporated with post-processing techniques integrated with a machine learning classifier to improve segmentation accuracy. Three versions of modified SAM models (i.e., SAM, FastSAM, and MobileSAM), two common instance segmentation algorithms (i.e., YOLOv8 and Mask R-CNN), and two foundation segmentation models (i.e., U-2-Net and ISNet) were comparatively evaluated to determine the optimal one for bird segmentation from thermal images. A total of 1,917 thermal images were collected from cage-free laying hens (Hy-Line W-36) at 77-80 weeks of age. The image dataset exhibited considerable variations such as feathers, bird movement, body gestures, and the specific conditions of cage-free facilities. The experimental results demonstrate that the modified SAM did not only surpass the six zero-shot models-YOLOv8, Mask R-CNN, FastSAM, MobileSAM, U(2)Net, and ISNet-but also outperformed other modified SAM-based models (Modified FastSAM and Modified MobileSAM) in terms of hen detection performance, achieving a success rate of 84.4 %, and in segmentation performance, with an intersection over union of 85.5 %, recall of 91.0 %, and an F1 score of 92.3 %. The optimal model, modified SAM, was pipelined to extract statistics including the averages (degrees C) of mean (27.03, 27.04, 28.53, 26.68), median (26.27, 26.84, 28.28, 26.78), 25th percentile (25.33, 25.61, 27.26, 25.53), and 75th percentile (28.04, 27.95, 29.22, 27.55) of surface body temperature of individual laying hens in thermal images for each week. More statistics of hen body surface temperature can be extracted based on the segmentation results. The developed pipeline is a useful tool for automatically evaluating the thermal conditions of individual birds.

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods.

Deep learning (DL) along with never-ending advancements in computational processing and cloud technologies have bestowed us powerful analyzing tools and techniques in the past decade and enabled us to use and apply them in various fields of study. Health informatics is not an exception, and conversely, is the discipline that generates the most amount of data in today's era and can benefit from DL the most. Extracting features and finding complex patterns from a huge amount of raw data and transforming them into knowledge is a challenging task. Besides, various DL architectures have been proposed by researchers throughout the years to tackle different problems. In this paper, we provide a review of DL models and their broad application in bioinformatics and healthcare categorized by their architecture. In addition, we also go over some of the key challenges that still exist and can show up while conducting DL research.

Interindividual distances and orientations of laying hens provide quantitative measures to calculate and optimize space allocations for bird flocks. However, these metrics were often measured manually and have not been examined for different stocking densities of laying hens. The objectives of this study were to 1) integrate and develop several deep learning techniques to detect interindividual distances and orientations of laying hens; and 2) examine the 2 metrics under 8 stocking densities via the developed techniques. Laying hens (Jingfen breed, a popular hen breed in China) at 35 wk of age were raised in experimental compartments at 8 different stocking densities of 3,840, 2,880, 2,304, 1,920, 1,646, 1,440, 1,280, and 1,152 cm2•bird-1 (3-10 hens per compartment, respectively), and cameras on the top of the compartments recorded videos for further analysis. The designed deep learning image classifier achieved over 99% accuracy to classify bird's perching status and excluded frames with bird perching to ensure that all birds analyzed were on the same horizontal plane, reducing calculation errors. The YOLOv5m oriented object detection model achieved over 90% precision, recall, and F1 score in detecting birds in compartments and can output bird centroid coordinates and angles, from which interindividual distances and orientations were calculated based on pairs of birds. Laying hens maintained smaller minimum interindividual distances in higher stocking densities. They were in an intersecting relationship with conspecifics for over 90% of the time. The developed integrative deep learning techniques and behavior metrics provide animal-based measurement of space requirement for laying hens.

Deep learning has been the answer to many machine learning problems during the past two decades. However, it comes with two significant constraints: dependency on extensive labeled data and training costs. Transfer learning in deep learning, known as Deep Transfer Learning (DTL), attempts to reduce such reliance and costs by reusing obtained knowledge from a source data/task in training on a target data/task. Most applied DTL techniques are network/model-based approaches. These methods reduce the dependency of deep learning models on extensive training data and drastically decrease training costs. Moreover, the training cost reduction makes DTL viable on edge devices with limited resources. Like any new advancement, DTL methods have their own limitations, and a successful transfer depends on specific adjustments and strategies for different scenarios. This paper reviews the concept, definition, and taxonomy of deep transfer learning and well-known methods. It investigates the DTL approaches by reviewing applied DTL techniques in the past five years and a couple of experimental analyses of DTLs to discover the best practice for using DTL in different scenarios. Moreover, the limitations of DTLs (catastrophic forgetting dilemma and overly biased pre-trained models) are discussed, along with possible solutions and research trends.

This paper proposes a model to predict the levels (e.g., Bachelor, Master, etc.) of postsecondary degree awards that have been ambiguously expressed in the student tracking reports of the National Student Clearinghouse (NSC). The model will be the hybrid of two modules. The first module interprets the relevant abbreviatory elements embedded in NSC reports by referring to a comprehensive database that we have made of nearly 950 abbreviations for degree titles used by American postsecondary educators. The second module is a combination of feature classification and text mining modeled with CNN-BiLSTM, which is preceded by several steps of heavy pre-processing. The model proposed in this paper was trained with four multi-label datasets of different grades of resolution and returned 97.83\% accuracy with the most sophisticated dataset. Such a thorough classification of degree levels will provide insights into the modeling patterns of student success and mobility. To date, such a classification strategy has not been attempted except using manual methods and simple text parsing logic.

Evolutionary algorithms, rooted in Darwin's theorem, can be considered as a base for implementing deep/machine learning models. This approach can noticeably increase the accuracy in most cases as shown in this paper. This experiment aims to evaluate the performance of two evolutionary algorithms, an evolutionary neural network (ENN) and an evolutionary CNN-based algorithm with mutation and crossover (ECNNB), on the Fashion-MNIST, MNIST, and EMNIST_Digits datasets. The performance of the ENN algorithm is examined for 10, 50, and 100 generations, with 50 generations being used due to computational limitations. The results show that the accuracy of the model improves as the number of generations increases. However, the ECNNB model consistently outperforms the ENN model on all three datasets, with an average accuracy of 92.58% on Fashion-MNIST, 99.32% on MNIST, and 99.50% on EMNIST_Digits, compared to 88.54 %, 98.05 %, and 98.95 %, respectively, for the ENN model. The performance of both models is compared with other state-of-the-art models in the literature. These results highlight the significance of well-designed models in achieving high accuracy in machine learning tasks.

The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield. With the recent advances in deep learning, many supervised learning approaches have been implemented to perform phenotypic trait measurement from images for various crops, but few studies have been conducted to count cotton bolls from field images. Supervised learning models require a vast number of annotated images for training, which has become a bottleneck for machine learning model development. The goal of this study is to develop both fully supervised and weakly supervised deep learning models to segment and count cotton bolls from proximal imagery. A total of 290 RGB images of cotton plants from both potted (indoor and outdoor) and in-field settings were taken by consumer-grade cameras and the raw images were divided into 4350 image tiles for further model training and testing. Two supervised models (Mask R-CNN and S-Count) and two weakly supervised approaches (WS-Count and CountSeg) were compared in terms of boll count accuracy and annotation costs. The results revealed that the weakly supervised counting approaches performed well with RMSE values of 1.826 and 1.284 for WS-Count and CountSeg, respectively, whereas the fully supervised models achieve RMSE values of 1.181 and 1.175 for S-Count and Mask R-CNN, respectively, when the number of bolls in an image patch is less than 10. In terms of data annotation costs, the weakly supervised approaches were at least 10 times more cost efficient than the supervised approach for boll counting. In the future, the deep learning models developed in this study can be extended to other plant organs, such as main stalks, nodes, and primary and secondary branches. Both the supervised and weakly supervised deep learning models for boll counting with low-cost RGB images can be used by cotton breeders, physiologists, and growers alike to improve crop breeding and yield estimation.

The alfalfa crop is globally important as livestock feed, so highly efficient planting and harvesting could benefit many industries, especially as the global climate changes and traditional methods become less accurate. Recent work using machine learning (ML) to predict yields for alfalfa and other crops has shown promise. Previous efforts used remote sensing, weather, planting, and soil data to train machine learning models for yield prediction. However, while remote sensing works well, the models require large amounts of data and cannot make predictions until the harvesting season begins. Using weather and planting data from alfalfa variety trials in Kentucky and Georgia, our previous work compared feature selection techniques to find the best technique and best feature set. In this work, we trained a variety of machine learning models, using cross validation for hyperparameter optimization, to predict biomass yields, and we showed better accuracy than similar work that employed more complex techniques. Our best individual model was a random forest with a mean absolute error of 0.081 tons/acre and R{$^2$} of 0.941. Next, we expanded this dataset to include Wisconsin and Mississippi, and we repeated our experiments, obtaining a higher best R{$^2$} of 0.982 with a regression tree. We then isolated our testing datasets by state to explore this problem's eligibility for domain adaptation (DA), as we trained on multiple source states and tested on one target state. This Trivial DA (TDA) approach leaves plenty of room for improvement through exploring more complex DA techniques in forthcoming work.

Over the past few years, deep neural networks have been widely employed for representation learning and achieved remarkable success in many computer vision tasks, such as visual sentiment analysis and emotion recognition. However, identifying image sentiments similar to what humans do is challenging due to the complexity of raw images and the intangible nature of human visual perception. Besides, training a deep learning model from scratch for a complex task such as group emotion recognition is very time-consuming and requires a large amount of labeled data representing the total population which is practically infeasible. In addition, creating annotated data for each specific task is costly and sometimes impossible. So instead, we can use the knowledge extracted by a model trained on related existing labeled datasets. To address the above challenges, we propose an end-to-end group emotion recognition framework that integrates human perception learned from human eye fixation data to efficiently and effectively extract the sentiment of input images and classify them as positive, negative, or neutral. The proposed architecture aims to leverage the information in eye fixation data to learn how humans perceive and interpret the visual world in a free-viewing task. Our framework can be utilized in any free-viewing task, potentially reducing the need for a large amount of labeled data and speeding up the training phase. In the following sections, The following sections outline the initial steps and describe different parts of our architecture.

The number of web APIs is growing rapidly. API adoption is increasing across all industries with executives prioritizing investments in the API economy. Each API provider offers API documentation which includes complex descriptions. In order to collect and understand the applications and operations of diverse APIs, software engineers read lengthy and complicated API documentations. Understanding the variety of API documentations is a labor intensive and error-prone process. In this paper, we introduce a data-centric web crawler service to collect, analyze, and construct a large corpus of API documentations. The generated API Corpus can be used in machine programming (i.e., code generation, code search). The proposed API web-crawler intelligently harvests more than 2.8M API documentation pages where it uses a machine-learning-based approach with an accuracy of 91.32% to select only web API pages (REST). We also conducted an extensive and end-to-end real-world evaluation, where the proposed API web-crawler not only collects a sheer number of API pages, but also successfully validates 1,222 APIs out of 1,521 target APIs with a success rate of 80.34%.