Advances in computational intelligence最新文献

This study aims to automate the reporting process of house inspections, which enables prospective buyers to make informed decisions. Currently, the inspection report generated by an inspector involves inserting all defect images into a spreadsheet software and manually captioning each image with identified defects. To the best of our knowledge, there are no previous works or datasets that have automated this process. Therefore, this paper proposes a new image captioning dataset for house defect inspection, which is benchmarked with three deep learning-based models. Our models are based on the encoder–decoder architecture where three image encoders (i.e., VGG16, MobileNet, and InceptionV3) and one GRU-based decoder with an additive attention mechanism of Bahdanau are experimented. The experimental results indicate that, despite similar training losses in all models, VGG16 takes the least time to train a model, while MobileNet achieves the highest BLEU-1 to BLEU-4 scores of 0.866, 0.850, 0.823, and 0.728, respectively. However, InceptionV3 is suggested as the optimal model, since it outperforms the others in terms of accurate attention plots and its BLEU scores are comparable to the best scores obtained by MobileNet.

Deep learning has played an important role in many real-life applications, especially in image classification. It is often found that some domain data are highly skewed, i.e., most of the data belongs to a handful of majority classes, and the minority classes only contain small amounts of information. It is important to acknowledge that skewed class distribution poses a significant challenge to machine learning algorithms. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis in case of imbalanced dataset is performed by considering deep learning based well known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is investigated. Moreover, to determine the global scientific research on the image classification of imbalanced mushroom dataset, a bibliometric analysis is conducted from 1991 to 2022. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research.

Text generation from charts is a task that involves automatically generating natural language text descriptions of data presented in chart form. This is a useful capability for tasks such as summarizing data for presentation or providing alternative representations of data for accessibility. In this work, we propose a hybrid deep network approach for text generation from table images in an academic format. The input to the model is a table image, which is first processed using Tesseract OCR (optical character recognition) to extract the data. The data are then passed through a Transformer (i.e., T5, K2T) model to generate the final text output. We evaluate the performance of our model on a dataset of academic papers. Results show that our network is able to generate high-quality text descriptions of charts. Specifically, the average BLEU scores are 0.072355 for T5 and 0.037907 for K2T. Our results demonstrate the effectiveness of the hybrid deep network approach for text generation from table images in an academic format.

Supervised learning will be a bottleneck for developing plant disease identification since it relies on learning from massive amounts of carefully labeled images, which is costly and time-consuming. On the contrary, self-supervised learning has succeeded in various image classification tasks; however, it has not been applied broadly in the plant disease analysis process. This work, therefore, studies the effectiveness of self-supervised learning using contrastive pre-training with SimCLR for plant disease image classification. We investigated unsupervised pre-training scenarios on unlabeled plant images across multiple architectures, including supervised fine-tuning on labeled samples. In addition, we explored the label efficiency of the self-supervised approach, acquired by fine-tuning the models on various fractions of labeled images. Our results demonstrated that the performance of self-supervised learning on plant disease became comparable to that of the supervised training approach.

In this paper, we have considered a non-linear mathematical model to study the chaotic situation, arising due to slow process of recruitment, leading to an increase in unemployment. We observed the effects on recruitment process due to delay and without delay. We have also studied the stability of equilibrium points with numerical examples to compare with analytical and theoretical results.

Threat surface area for the Internet of Things is calculated as the sum of security vulnerabilities or the weakness and gaps in protection efforts for the device, operating systems, associated software applications, and the local infrastructure. This aggregates all the known and unknown threats that can potentially expose the device, logs, data, and hosted applications. By reducing the exposed elements of the device surface, the device vulnerabilities can decrease the exposed threat surface area. This research presents a new framework first to map the devices in the ecosystem, measure the potential threat surface area from the exposure indicators for each layer and then determine the threat vectors for device compromise to calculate the maturity and severity levels. The authors propose new metrics to reassess and re-calculate the maturity and severity levels. Based on the new metrics, newly exposed threat surface elements provide a new security perspective beneficial for stakeholders involved in design, implementation, and security ecosystem of smart devices.

Local citation recommendation is a list of references that researchers need to cite based on a given context, so it could help researchers produce high-quality academic writing quickly and efficiently. However, existing citation recommendation methods only consider contextual content or author information, ignore the critical influence of historical citation information on citations, and learn the paper embedding at a coarse-grained level, resulting in lower-quality recommendations. To solve the above problems, we propose a novel two-stage citation recommendation model with multiple information fusion (MICR). The first stage is to enhance the target paper’s representation learning of the MICR model. To achieve the above goal, three encoders, which contain context information encoder, historical citation encoder, and author information encoder, are designed to learn rich representations of the target paper. The second stage is to select appropriate recommendation strategies for the target paper and candidate papers to achieve the goal of efficient citation recommendation. Experiments on two public citation datasets show that our model outperforms several competitive baseline methods on citation recommendation.

In the real number set, generalized intuitionistic fuzzy numbers (GIFNs) are an impressive number of fuzzy sets (FSs). GIFNs are very proficient in managing the decision-making problem data. Our aim of this paper is to develop a new ranking method for solving a multi-attribute decision-making (MADM) problem with GIFN data. Here, we have defined the possibility mean and standard deviation of GIFNs. Then, we have formulated the magnitude of membership and non-membership function of GIFNs. In the proposed MADM problem, the attribute values are expressed as GIFNs, which is a very workable environment for decision-making problems. Finally, a numerical example is analyzed to demonstrate the flexibility, applicability and universality of the proposed ranking method and MADM problem.

In this study, a decision support system (DSS) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was developed using MATLAB to select the best dental implant alternative. The first step involved conducting interviews with experts to identify the criteria for TOPSIS. In the second step, a database was structured for each dental implant brand distributed in the market for the last five years. In the third step, MATLAB code and Graphical User Interfaces (GUI) were created to execute TOPSIS. The user can also display the other four options with a graph on the GUI, including the ranking scores (C_i^*) for each option. The DSS was applied in two case studies. The MATLAB-based DSS tool has a compact, user-friendly interface, making it easy to adopt in implant selection decisions. The proposed DSS can be widely used in different applications in dental implant selection tasks.

Speech mood analysis is a challenging task with unclear optimal feature selection. The nature of the dataset, whether it is from an infant or adult, is crucial to consider. In this study, the characteristics of speech were investigated using Mel-frequency cepstral coefficients (MFCC) to analyze audio files. The CREMA-D dataset, which includes six different mood states (normal, angry, happy, sad, scared, and irritated), was employed to identify mood states from speech files. A mood classification system was proposed that integrates Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) models to increase the number of labeled data in small datasets and improve classification accuracy.

A semi-supervised model was proposed in this study to improve the accuracy of speech mood classification systems. The approach was tested on a classification model that used SVM and LSTM, and it was found that the semi-supervised model outperforms both SVM and LSTM models, achieving a validation accuracy of 89.72%. This result surpasses the accuracy achieved by SVM and LSTM models alone. Moreover, the semi-supervised method was observed to accelerate the training process of the model. These outcomes illustrate the efficacy of the proposed model and its potential to enhance speech mood analysis techniques.