The work is devoted to the development of the radial basis functions (RBF networks) neural network new architecture – a polymorphic RBF network in which the one-dimensional radial basis functions (RBFs) in the hidden layer instead, multidimensional RBFs are used, which makes it possible to better approximate complex functions that depend on several independent variables. Moreover, in its second layer, the summing the RBF outputs one by one from each group instead, multiplication is used, which allows the polymorphic RBF network to better identify relations between independent variables. Based on the training classical RBF networks evolutionary algorithm, the polymorphic RBF network training algorithm was created, which, through the initializing weight coefficients methods use taking into account the tasks structure and preliminary values, using the mutations tournament selection, adding additional criteria to the fitness function to take into account stability and speed training a polymorphic RBF network, as well as using an evolutionary mutation strategy, allowed us to obtain the lowest errors in training and testing a polymorphic RBF network compared to known RBF network architectures. The created polymorphic RBF network practical application possibility is demonstrated experimentally using the helicopters turboshaft engines (using the example, the TV3-117 turboshaft engine) operating process parameters optimizing solving task using a multicriteria optimization algorithm. The optimal Pareto front was obtained, which made it possible to obtain the engine operation three additional modes: maximum reduction of specific fuel consumption at the total pressure in the compressor increase degree increased value by 5.0 %, specific fuel consumption minimization at the total pressure in the compressor increase degree reduced value by 1.0 %, the total pressure in the compressor increases degree optimal value with a slight increase in specific fuel consumption by 10.5 %. Future research prospects include adapting the developed methods and models into the general concept for monitoring and controlling helicopter turboshaft engines during flight operations. This concept is implemented in the neural network expert system and the on-board automatic control system.

The classification of Fourier Transform Infrared spectra images is crucial in chemometrics. This paper proposes an efficient model based on deep learning approaches for enhancement and classification of the Fourier Transform Infrared Spectra (FTIR) images. The proposed model integrates three deep learning models including ResNet101, EfficientNetB0, and Wavelet Scattering transform (WST) to extract several features from FTIR.  Then the obtained features were fused in conjunction with standard statistical feature extraction. It followed by a subsequent classification phase that employs a Convolutional Neural Network (CNN) architecture, which demonstrates high accuracy in classifying the infrared spectra images into six different classes of ligands and their metal complexes. During the training phase, the network’s weights are iteratively updated using the Adam optimization algorithm. This model addresses the challenge of small and imbalanced datasets through an image oversampling process. Using random over-sampling technique, it enhances the training process and overall classification performance. The extracted features were analyzed using t-distributed Stochastic Neighbor Embedding (t-SNE) to visualize high-dimensional data in two dimensions. The results of the proposed model show high classification accuracy of 0.91%, low error rate of 0.08%, a sensitivity of 0.89% and a precision of 0.89%, false positive rate of 0.01%, F1 score of 0.89, Matthews Correlation Coefficient of 0.87 and Kappa of 0.68.

To maximize production efficiency, product quality control is paying more attention to the quick and reliable automated quality visual inspection. Product defect detection is a critical part of the inspection process. Manual defect detection has a lot of flaws that can be overcome using a deep learning approach. In this paper we have proposed and implemented the deep learning models to detect defects in the manufactured product. Two types of classification, i.e., binary and multiclass classification, is done using CNN, AlexNet, and YOLO algorithms. For the binary classification which is just used to check whether there is a defect in the product, we have proposed three different architectures of CNN, out of which the third CNN model gave 99.44% and 97.49% for training and testing, respectively. We also tested the AlexNet model and got accuracy of 97.6%. And for the multiclass classification that is used for identification of type(s) of defects, the YOLOv8 model is proposed and implemented, which gives better results by attaining a remarkable accuracy of 98.7% for multiclass classification. We also designed and developed the Android Application, which is used on the field for defect detection in the manufacturing industry.

Building a personalized travel recommendation system is important to enhance the satisfaction and experience of travelers. Due to the lack of an efficient online-based tourist assistance system, tourists have faced several challenges in Bangladesh, such as difficulties in planning their trips and making informed decisions. To overcome the existing challenges, in this paper, a prediction model has been developed to predict the suitability of a travel destination based on the user’s preferences and some other relevant factors. Then the system offers personalized recommendations for the best local places to visit, hotels to stay in, transportation services, and travel agencies with the necessary details. This paper utilizes various machine learning classification algorithms to predict the best-suited travel destinations and local tourist spot recommendations for users based on their budget and preferences. The examined results verified that the random forest algorithm provides the best accuracy of 98 percent and is used for tourist place eligibility prediction. The user rating analysis visualized that the proposed mobile application received satisfactory remarks from more than 60 percent of reviewers regarding its effectiveness.

It's getting harder for 21st-century citizens to effectively detect sarcasm using sentiment analysis in a world full of sarcastic people and identifying sarcasm aids in understanding the unpleasant truth hidden beneath polite language. While sarcasm in text is frequently identified, very little research has been done on text sarcasm recognition in memes. This study uses a hybrid machine learning strategy to increase accuracy in identifying sarcasm text in sentiment analysis. It also compares the hybrid approach to existing approaches, like Random Forest, Logistic Regression, Naive Bayes, Stochastic Gradient Descent, and Decision Tree. The effectiveness of several methods is assessed in this study using recall, precision, and f-measure. The results showed that the suggested strategy (0.8004%) received the highest score when the prediction accuracy of several machine learning approaches was compared. The proposed hybrid approach performs much better in terms of enhancing accuracy.

Learning-to-rank is an applied domain of supervised machine learning. As feature selection has been found to be effective for improving the accuracy of learning models in general, it is intriguing to investigate this process for learning-to-rank domain. In this study, we investigate the use of a popular meta-heuristic approach called simulated annealing for this task. Under the general framework of simulated annealing, we explore various neighborhood selection strategies and temperature cooling schemes. We further introduce a new hyper-parameter called the progress parameter that can effectively be used to traverse the search space. Our algorithms are evaluated on five publicly benchmark datasets of learning-to-rank. For a better validation, we also compare the simulated annealing-based feature selection algorithm with another effective meta-heuristic algorithm, namely local beam search. Extensive experimental results show the efficacy of our proposed models.