The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large language models (LLMs). SKG-LLM extracts and organizes complex relationships from the biomedical literature, using it to increase the accuracy and depth of KG in stroke research. In the proposed method, GPT-4 was used for data pre-processing, and the extraction of embeddings was also done by GPT-4 in the whole KG construction process. The performance of the proposed model was tested with two evaluation criteria: Precision and Recall. For further validation of the proposed model, GPT-4 was used. Compared with Wikidata and WN18RR, the proposed KG-LLM approach performs better, especially in precision and recall. By including GPT-4 in the preprocessing process, the SKG-LLM model achieved a precision score of 0.906 and a recall score of 0.923. Expert reviews further improved the results and increased precision to 0.923 and recall to 0.918. The knowledge graph constructed by SKG-LLM contains 2692 nodes and 5012 edges, which are 13 distinct types of nodes and 24 types of edges.

Prostate cancer (Pca) continues to be a leading cause of cancer-related mortality in men, and the limitations in precision of traditional diagnostic methods such as the Digital Rectal Exam (DRE), Prostate-Specific Antigen (PSA) testing, and biopsies underscore the critical importance of accurate staging detection in enhancing treatment outcomes and improving patient prognosis. This study leverages machine learning and deep learning approaches, along with feature selection and extraction methods, to enhance PCa pathological staging predictions using RNA sequencing data from The Cancer Genome Atlas (TCGA). Gene expression profiles from 486 tumors were analyzed using advanced algorithms, including Random Forest (RF), Logistic Regression (LR), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The performance of the study is measured with respect to the F1-score, as well as precision and recall, all of which are calculated as weighted averages. The results reveal that the highest test F1-score, approximately 83%, was achieved by the Random Forest algorithm, followed by Logistic Regression at 80%, while both Extreme Gradient Boosting (XGB) and Support Vector Machine (SVM) scored around 79%. Furthermore, deep learning models with data augmentation achieved an accuracy of 71. 23%, while PCA-based dimensionality reduction reached an accuracy of 69.86%. This research highlights the potential of AI-driven approaches in clinical oncology, paving the way for more reliable diagnostic tools that can ultimately improve patient outcomes.

Mobile network operators constantly optimize their networks to ensure superior service quality and coverage. This optimization is crucial for maintaining an optimal user experience and requires extensive data collection and analysis. One of the primary methods for gathering this data is through drive tests, where technical teams use specialized equipment to collect signal information across various regions. However, drive tests are both costly and time-consuming, and they face challenges such as traffic conditions, environmental factors, and limited access to certain areas. These constraints make it difficult to replicate drive tests under similar conditions. In this study, we propose a method that enables operators to predict received signal strength at specific locations using data from other drive test points. By reducing the need for widespread drive tests, this approach allows operators to save time and resources while still obtaining the necessary data to optimize their networks and mitigate the challenges associated with traditional drive tests.

One fundamental task for NLP is to determine the similarity between two texts and evaluate the extent of their likeness. The previous methods for the Persian language have low accuracy and are unable to comprehend the structure and meaning of texts effectively. Additionally, these methods primarily focus on formal texts, but in real-world applications of text processing, there is a need for robust methods that can handle colloquial texts. This requires algorithms that consider the structure and significance of words based on context, rather than just the frequency of words. The lack of a proper dataset for this task in the Persian language makes it important to develop such algorithms and construct a dataset for Persian text. This paper introduces a new transformer-based model to measure semantic similarity between Persian informal short texts from social networks. In addition, a Persian dataset named FarSSiM has been constructed for this purpose, using real data from social networks and manually annotated and verified by a linguistic expert team. The proposed model involves training a large language model using the BERT architecture from scratch. This model, called FarSSiBERT, is pre-trained on approximately 104 million Persian informal short texts from social networks, making it one of a kind in the Persian language. Moreover, a novel specialized informal language tokenizer is provided that not only performs tokenization on formal texts well but also accurately identifies tokens that other Persian tokenizers are unable to recognize. It has been demonstrated that our proposed model outperforms ParsBERT, laBSE, and multilingual BERT in the Pearson and Spearman's coefficient criteria. Additionally, the pre-trained large language model has great potential for use in other NLP tasks on colloquial text and as a tokenizer for less-known informal words.

Precision agriculture involves the application of advanced technologies to improve agricultural productivity, efficiency, and profitability while minimizing waste and environmental impact. Deep learning approaches enable automated decision-making for many visual tasks. However, in the agricultural domain, variability in growth stages and environmental conditions, such as weather and lighting, presents significant challenges to developing deep learning-based techniques that generalize across different conditions. The resource-intensive nature of creating extensive annotated datasets that capture these variabilities further hinders the widespread adoption of these approaches. To tackle these issues, we introduce a semi-self-supervised domain adaptation technique based on deep convolutional neural networks with a probabilistic diffusion process, requiring minimal manual data annotation. Using only three manually annotated images and a selection of video clips from wheat fields, we generated a large-scale computationally annotated dataset of image-mask pairs and a large dataset of unannotated images extracted from video frames. We developed a two-branch convolutional encoder-decoder model architecture that uses both synthesized image-mask pairs and unannotated images, enabling effective adaptation to real images. The proposed model achieved a Dice score of 80.7\% on an internal test dataset and a Dice score of 64.8\% on an external test set, composed of images from five countries and spanning 18 domains, indicating its potential to develop generalizable solutions that could encourage the wider adoption of advanced technologies in agriculture.

Soccer Simulation 2D (SS2D) is a simulation of a real soccer game in two dimensions. In soccer, passing behavior is an essential action for keeping the ball in possession of our team and creating goal opportunities. Similarly, for SS2D, predicting the passing behaviors of both opponents and our teammates helps manage resources and score more goals. Therefore, in this research, we have tried to address the modeling of passing behavior of soccer 2D players using Deep Neural Networks (DNN) and Random Forest (RF). We propose an embedded data extraction module that can record the decision-making of agents in an online format. Afterward, we apply four data sorting techniques for training data preparation. After, we evaluate the trained models' performance playing against 6 top teams of RoboCup 2019 that have distinctive playing strategies. Finally, we examine the importance of different feature groups on the prediction of a passing strategy. All results in each step of this work prove our suggested methodology's effectiveness and improve the performance of the pass prediction in Soccer Simulation 2D games ranging from 5\% (e.g., playing against the same team) to 10\% (e.g., playing against Robocup top teams).

There are many complex and rich morphological subtleties in the Arabic language, which are very useful when analyzing traditional Arabic texts, especially in the historical and religious contexts, and help in understanding the meaning of the texts. Vocabulary separation means separating the word into different parts such as root and affix. In the morphological datasets, the variety of labels and the number of data samples helps to evaluate the morphological methods. In this paper, we present a benchmark data set for evaluating the methods of separating Arabic words which include about 223,690 words from the book of Sharia alIslam, which have been labeled by experts. In terms of the volume and variety of words, this dataset is superior to other existing data sets, and as far as we know, there are no Arabic Hadith Domain texts. To evaluate the dataset, we applied different methods such as Farasa, Camel, Madamira, and ALP to the dataset and we reported the annotation quality through four evaluation methods.

Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for decomposing the total energy consumption profile into individual appliance load profiles within the household. It has multiple applications in demand-side management, energy consumption monitoring, and analysis. Various methods, including machine learning and deep learning, have been used to implement and improve NILM algorithms. This paper reviews some recent NILM methods based on deep learning and introduces the most accurate methods for residential loads. It summarizes public databases for NILM evaluation and compares methods using standard performance metrics.

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a transformer-based attention mechanism and temporal pooling for Non-Intrusive Load Monitoring (NILM) of smart buildings. The paper aims to improve the accuracy of NILM by using a deep learning-based method. The proposed method uses a Seq2Seq model with a transformer-based attention mechanism to capture the long-term dependencies of NILM data. Additionally, temporal pooling is used to improve the model's accuracy by capturing both the steady-state and transient behavior of appliances. The paper evaluates the proposed method on a publicly available dataset and compares the results with other state-of-the-art NILM techniques. The results demonstrate that the proposed method outperforms the existing methods in terms of both accuracy and computational efficiency.

Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a robust model by re-weighting training examples based on a secondary biased model. Here, the underlying assumption is that the biased model resorts to shortcut features. Hence, those training examples that are correctly predicted by the biased model are flagged as being biased and are down-weighted during the training of the main model. However, assessing the importance of an instance merely based on the predictions of the biased model may be too naive. It is possible that the prediction of the main model can be derived from another decision-making process that is distinct from the behavior of the biased model. To circumvent this, we introduce a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance. With experiments conducted on natural language inference and fact verification benchmarks, we show that our method improves OOD results while maintaining in-distribution (ID) performance.