Skin cancer is one of the dangerous cancers that threatens human life and can be seen in lesions on the patient’s skin. If this disease is not treated in time, it can threaten the patient’s life. However, with early detection of this disease, more effective treatment measures can be used and its progression can be prevented. Many machine learning and deep learning methods have been developed for early detection of skin cancer. But one of the advantages that can be provided by deep learning is its ability to learn from large data sets, which makes it more favorable for deep learning methods. The present paper proposes a method based on the combination of Swin transformer and Wavelet for classifying skin cancer images. In addition, feature optimization is performed using improved gray wolf optimizer(IGWO), Fox optimizer (FOX) and modified gorilla troops optimizer (MGTO) to identify optimal feature values. ISIC-2016 and ISIC-2017 datasets are used to evaluate the developed model. From the obtained results, it is shown that the Swin Transformer-based Wavelet +MGTO model provides the best result on the ISIC-2016 data set, achieving an accuracy of 0.9792. Furthermore, the Swin Transformer-based Wavelet +IGWO model provides the highest performance on the ISIC-2017 dataset, with an accuracy of 0.9792 and an F-measure score of 0.9780 on the ISIC-2017 data set.

The development of large language models (LLMs) has grown exponentially since the release of ChatGPT. Large language models have gained attention for their robust performance across various tasks. The ability of LLMs to understand and produce general-purpose language is achieved by training billions of parameters. These models have emerged as a transformative force in increasing natural language understanding, representing an important step toward general artificial intelligence(AI). LLMs have become powerful tools for various tasks, including natural language processing (NLP), machine translation(MT), vision applications, and question-answering(QA). The expanded reach of LLMs goes beyond the conventional linguistic bounds and includes specialized languages created in different scientific disciplines. The intensification of interest in this new subclass of scientifically oriented LLMs has led to the birth of the scientific LLMs. These scientific LLMs are gradually gaining a foothold as an exciting research area for science study. Theoretically, they share a structure in common with general LLMs. In practice, however, they differ regarding input and usage. This paper undertakes an exhaustive effort to study all the scientific LLMs, the types of structures offered, the datasets, the parameters, and the context of use. Our analysis uses a focused lens that focuses on the biological and chemical domains, which enables an in-depth examination of LLMs for textual knowledge, small molecules, macromolecules, proteins, genomic sequences, and combinations. By providing an overview of the technical advances in the field, this survey is a valuable resource for researchers navigating the complex landscape of scientific LLMs.

Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.

Purpose: This research project has a single purpose: the construction and evaluation of PKG-LLM, a knowledge graph framework whose application is primarily intended for cognitive neuroscience. It also aims to improve predictions of relationships among neurological entities and improve named entity recognition (NER) and relation extraction (RE) from large neurological datasets. Employing the GPT-4 and expert review, we aim to demonstrate how this framework may outperform traditional models by way of precision, recall, and F1 score, intending to provide key insights into possible future clinical and research applications in the field of neuroscience. Method: In the evaluation of PKG-LLM, there were two different tasks primarily: relation extraction (RE) and named entity recognition (NER). Both tasks processed data and obtained performance metrics, such as precision, recall, and F1-score, using GPT-4. Moreover, there was an integration of an expert review process comprising neurologists and domain experts reviewing those extracted relationships and entities and improving such final performance metrics. Model comparative performance was reported against StrokeKG and Heart Failure KG. On the other hand, PKG-LLM evinced itself to link prediction-in-cognition through metrics such as Mean Rank (MR), Mean Reciprocal Rank (MRR), and Precision at K (P@K). The model was evaluated against other link prediction models, including TransE, RotatE, DistMult, ComplEx, ConvE, and HolmE. Findings: PKG-LLM demonstrated competitive performance in both relation extraction and named entity recognition tasks. In its traditional form, PKG-LLM achieved a precision of 75.45\%, recall of 78.60\%, and F1-score of 76.89\% in relation extraction, which improved to 82.34\%, 85.40\%, and 83.85\% after expert review. In named entity recognition, the traditional model scored 73.42\% precision, 76.30\% recall, and 74.84\% F1-score, improving to 81.55\%, 84.60\%, and 82.99\% after expert review. For link prediction, PKG-LLM achieved an MRR of 0.396, P@1 of 0.385, and P@10 of 0.531, placing it in a competitive range compared to models like TransE, RotatE, and ConvE. Conclusion: This study showed that PKG-LLM mainly outperformed the existing models by adding expert reviews in its application in extraction and named entity recognition tasks. Further, the model's competitive edge in link prediction lends credence to its capability in knowledge graph construction and refinement in the field of cognitive neuroscience as well. PKG-LLM's superiority over existing models and its ability to generate more accurate results with clinical relevance indicates that it is a significant tool to augment neuroscience research and clinical applications. The evaluation process entailed using GPT-4 and expert review. This approach ensures that the resulting knowledge graph is scientifically compelling and practically beneficial in more advanced cognitive neuroscience research.

The effective diagnosis of acute and hard-to-heal wounds is crucial for wound care practitioners to provide effective patient care. Poor clinical outcomes are often linked to infection, peripheral vascular disease, and increasing wound depth, which collectively exacerbate these comorbidities. However, diagnostic tools based on Artificial Intelligence (AI) speed up the interpretation of medical images and improve early detection of disease. In this article, we propose a multi-modal AI model based on transfer learning (TL), which combines two state-of-the-art architectures, Xception and GMRNN, for wound classification. The multi-modal network is developed by concatenating the features extracted by a transfer learning algorithm and location features to classify the wound types of diabetic, pressure, surgical, and venous ulcers. The proposed method is comprehensively compared with deep neural networks (DNN) for medical image analysis. The experimental results demonstrate a notable wound class classifications (containing only diabetic, pressure, surgical, and venous) vary from 78.77 to 100% in various experiments. The results presented in this study showcase the exceptional accuracy of the proposed methodology in accurately classifying the most commonly occurring wound types using wound images and their corresponding locations.

Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63\% accuracy for predicting the next day and 64\%, 67\% and 82\% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72\% to 2.85\% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.