This research aims to improve the performance of convolutional neural network (CNN) in medical image segmentation that will detect specific parts of the body's anatomical structures. Medical images have drawbacks, such as the image's variability, quality, and complexity. We developed image preprocessing scenarios using Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and the hybrid approaches (HE-CLAHE and CLAHE-HE). We propose CNN with image enhancement for image segmentation and evaluate its performance on Lung CT-Scan and Chest X-ray datasets, which totaled 267 and 3616 images, respectively, and had ground truth. The experimental results indicate that the optimal cumulative distribution function (CDF) value of HE is 0 to 39, and the clip limit of CLAHE is 0.01. CNN produces the best segmentation with the addition of the CLAHE-HE approach. This method can increase the accuracy by 1.23 percentage points (training) and 3.22 percentage points (testing) for Lung CT-Scan images. Meanwhile, for Chest X-ray images, the training and testing accuracy increased by 1.58 and 0.96 percentage points. In addition, the proposed medical image segmentation approach using the CNN method with CLAHE-HE obtained the values of comparative coefficients DSC (dice similarity coefficient), and SSIM (structural similarity index measurement) of only about 0.92 and 0.97, respectively.

This study aims to classify the maturity level of oil palm using the K-Nearest Neighbors (KNN) method based on the extraction of RGB and L*a*b color features. This classification determines the optimal oil production based on the color of the palm. However, this process is often carried out by humans, so sorting is not optimal because it is done manually, and it takes a long time. Thus, we propose automatic detection using machine learning based approach. In this study, the image processing methods are used to classify the level of oil palm maturity. The process starts with taking 150 photos with a smartphone. Each of them is then grouped into 3 classes, namely ripe (50), intermediate (50), and unripe (50). The oil palm image is pre-processed using the segmentation method (removing background), resizing, and cropping. Then, color features using RGB and L*a*b are extracted. Each of these extracted color features becomes input in the training and testing process of the KNN algorithm and is validated using k-fold cross-validation, with the results of accuracy being 95.4% and 97.4%. The combination of KNN and L*a*b color extraction approaches has better accuracy than KNN and RGB in terms of model accuracy (by 2–4.8 percentage points).

The paper presents the Bairon system that supports the automatic generation of poetry. The proposed system allows generating a poem in the literary style of the selected writer using the user's input as the first line, and translating the given text into Shakespearean English. To accomplish that, GPT-2 and T5 language models were fine-tuned. We also propose easy to understand metrics to evaluate the quality of the generated poems and their similarity to the corresponding poet's original work, and to present the results. Additionally, the Poetry Turing Test with human participants was conducted to get another measure of quality of the generated poetry.

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results.

In the paper requirements for artificial intelligence algorithms designed for modern strategy computer games are analyzed. The selected techniques used to fulfill those requirements are described in detail. Then the exemplary game, which was designed and implemented using the selected algorithms for path-finding, decision-making, tactical and strategic reasoning, and team coordination is presented. Finally, the results of experiments conducted with the use of the created game are analyzed. The results prove the efficiency of selected techniques in creating a strategically challenging game.

Moves made by chess players during the match are certainly some kind of sequence. In this work, we tried to answer the question if such data could be interpreted as sequential. To achieve that, a model build with LSTM layers only was designed. Results performed by the model are justifying that thesis. A novel model architecture was also proposed and trained on multiple data types—chess moves and chess game metadata. Its purpose was to perform as high classification accuracy as possible—we managed to achieve it with a result close to 69%. Moreover, we compared a couple of chessboard representation methods, more precisely a bitmap input and algebraic input, to check which one is more relevant for the neural networks training process. Contrary to what one might suppose, better scores were reached for bitmap input, which from the theoretical point of view carries out less information than algebraic input.

Evolutionary algorithms are (meta-)heuristic techniques used in the case of search, optimization, and adaptation problems, which cannot be solved with the use of traditional methods. Sexual selection mechanism helps to maintain the population diversity in evolutionary algorithms. In this paper the agent-based realization of multi-objective evolutionary algorithm with sexual selection mechanism is presented. The system is evaluated with the use of Zitzler’s test problems and compared to “classical” multi-objective evolutionary algorithms.

Co-evolutionary techniques for evolutionary algorithms are aimed at overcoming their limited adaptive capabilities and allow for the application of such algorithms to problems for which it is difficult or even impossible to formulate explicit fitness function. Sexual selection resulting from sexual conflict and co-evolution of female mate choice and male displayed trait is considered to be one of the ecological interactions responsible for maintaining population diversity. In this paper the idea of co-evolutionary multi-agent system with sexual selection mechanism for multi-objective optimization is introduced. In presented system the Pareto frontier is located by the population of agents as a result of co-evolutionary interactions between sexes. Also, results from runs of presented system against test functions and comparison to classical multi-objective evolutionary algorithms are presented.