The task of aspect-level sentiment analysis is intricately designed to determine the sentiment polarity directed towards a specific target within a sentence. With the increasing availability of online reviews and the growing importance of healthcare decisions, analyzing drug reviews has become a critical task. Traditional sentiment analysis, which categorizes a whole review as positive, negative, or neutral, provides limited insights for consumers and healthcare professionals. Aspect-based sentiment analysis (ABSA) aims to overcome these limitations by identifying and evaluating the sentiment associated with specific aspects or attributes of drugs mentioned in the reviews. Various fields, including business, politics, and medicine, have been explored in the context of sentiment analysis. Automation of online user reviews allows pharmaceutical companies to assess large amounts of user feedback. This helps extract pharmacological efficacy and side effect insights. The data collected could improve pharmacovigilance. Reviewing user comments can provide valuable data that can be used to improve drug safety and efficacy monitoring procedures. This improves pharmacovigilance processes, improving pharmaceutical outcomes understanding and corporate decision-making. Therefore, we propose a pre-trained RoBERTa with a Bi-LSTM model to categorise drug reviews from online sources and pre-process the text data. Ant Colony Optimization can be used in feature selection for ABSA, helping to identify the most relevant aspects and sentiments. Further, RoBERTa is fine-tuned to perform ABSA on the dataset, enabling the system to categorize aspects and determine the associated sentiment. The outcomes reveal that the suggested framework has achieved higher accuracy (96.78%) and F1 score (98.29%) on druglib.com, and 95.02% on the drugs.com dataset, than several prior state-of-the-art methods.

Large blocks of data must be analyzed and explored by utilizing the data mining procedures in order to uncover significant patterns and trends. Medical databases are one area where the data mining procedures can be utilized. Many people all over the world are struggling with their health and medical diagnoses. Massive amounts of data are produced by hospital information systems (HIS), yet it might be difficult to extract knowledge from diagnosis case data. By just giving the symptoms they are experiencing, patients can quickly learn about the sickness they are experiencing and the medication that can assist, treat it using the approaches utilized in this paper. In this paper, we give drug recommendations relied on ratings and conditions to customers. Four distinct prototypes are utilized to predict the diseases. The Vader tool and sentiment analysis relied on NLP are utilized to analyze the reviews. And finally, probabilistic and weighted average methodologies are utilized to recommend the medications. Each model and strategy utilized in this paper is described in detail. The experimental findings presented in this work can be utilized in future studies and for a variety of different medicinal applications.

Sentiment analysis is a sub-domain in opinion mining that extracts sentiments from the users’ opinions from text messages. Opinions from E-commerce websites, blogs, online social media, etc., and these opinions are in the form of text, suggestions, and comments. This paper describes the new sentiment analysis model to predict sentiments effectively that can be used to improve product quality and sales. The proposed approach is an integrated model combining several techniques, such as the pre-trained model BERT-large-cased (BLC) for training the dataset. BLC model contains 24-layer, 1024-hidden, 16-heads, 340M parameters. Optimization algorithms can fine-tune pre-trained models, such as BERT, for sentiment analysis tasks. Fine-tuning involves training the pre-trained model on a specific sentiment analysis task to improve performance. Stochastic Gradient Descent (SGD) is the optimized algorithm that helps to analyze the sentiments effectively from the given datasets. The next step is the combination of pre-processing techniques such as Tokenization, Stop Word Removal, etc. The next step focused on Bag-of-Words (BoW) and word embedding techniques like Word2Vec used to extract the features from the datasets. The deep sentiment analysis (DSA) based classification is designed to classify the sentiments based on aspect and priority model to achieve better results. The proposed model combines Aspect and Priority-based Sentiment analysis with a Decision-based Recurrent Neural Network (D-RNN). The experiments are conducted using Twitter, Restaurant, and Laptop datasets available publicly on Kaggle—the proposed model’s performance is analyzed using a confusion matrix. The proposed approach addresses various challenges in analyzing the sentiment analysis. Python programming language with several libraries such as Keras, Pandas, and others extracts the sentiments from given datasets. The comparison between the existing and proposed models shows the effectiveness of the sentiment outputs.

Breast cancer (BC) is closely linked with the maximum mortality rate for cancer detection across the globe and has become a predominant public health issue. Earlier detection might increase the possibility of survival and successful treatment. However, it is a time-consuming and very challenging task that depends on the diagnostician’s experience. For patients and their prognosis, it is essential that BC cancer can be automatically detected by the analysis of histopathological images. Conventional feature extraction method extracts some lower-level features of images, and preceding knowledge is essential for selecting suitable features that could be heavily impacted by human beings. The deep learning (DL) technique extracts higher-level abstract features from an image automatically. Therefore, this study develops a new Ensemble Learning with Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification (ELSOSA-BCC) technique on Histopathological Images. In the ELSOSA-BCC technique, the noise is removed using Gabor filtering (GF). In addition, the ELSOSA-BCC technique employs the EfficientNet-B0 model for feature extraction and optimal hyperparameter tuning using the SOS algorithm. Finally, the ensemble learning-based classification process is performed by three classifiers namely deep stacked autoencoder (DSAE), kernel extreme learning machine (KELM), and bidirectional long short-term memory (BiLSTM). In this study, ELSOSA-BCC simulation values are tested on a medical dataset. ELSOSA-BCC has been shown to perform better than other models in the experimental results.

INTRODUCTION: Dermatological disorders, particularly human skin diseases, have become more common in recent decades. Environmental factors, socioeconomic problems, a lack of a balanced diet, and other variables have all contributed to an increase in skin diseases in recent years. Skin diseases can cause psychological suffering in addition to physical injury, especially in people with scarred or disfigured faces. OBJECTIVES: The use of artificial intelligence or computer-based technologies in the detection of face skin disorders has advanced dramatically over time. Even for highly experienced doctors and dermatologists, identifying skin disorders can be tricky since many skin diseases have a visual affinity with the surrounding skin and lesions. METHODS: Today, the majority of skincare specialists rely on time-consuming, traditional methods to identify disorders. Even though several research have demonstrated promising results on the picture classification job, few studies compare well-known deep learning models with various metrics for categorizing human skin disorders. RESULTS: This study examines and contrasts various skin illnesses in terms of cosmetics and common skin concerns. Our dataset includes over 25000 of the eight most common skin disorders. Convolutional neural networks have shown imaging performance that is comparable to or greater than that of humans. We used 11 different network algorithms to identify the illnesses in the sample and compared the results. CONCLUSION: To adjust the format of incoming photographs, we do certain image pre-processing and image scaling for each model. ResNet152 beat other deep learning methods in terms of recall, accuracy, and precision on a test dataset of 1930 images.

The landscape of medical imaging, particularly in brain tumor analysis and survival prediction, necessitates advancements due to the inherent complexities and life-threatening nature of brain tumors. Existing methodologies often struggle with precision and efficiency, predominantly due to limitations in handling the diverse and intricate nature of medical image datasets. This research presents a novel approach that aims to improve the accuracy of survival prediction in patients with brain tumours, leveraging a Generative Adversarial Network (GAN) integrated with Neural Architectural Search (NAS) operations. The model employs the Adaptive Computation Time (ACT) Transformer, a method crucial for dynamically adjusting the number of transformer layers based on the complexity of the input sets. This feature is particularly beneficial in medical imaging for adapting to the varying complexities of brain tumor data samples. The integration of Squeeze-and-Excitation Networks (SENet) enables the model to recalibrate features channel-wise, significantly enhancing sensitivity to pivotal features in MRI images. Furthermore, the application of Google’s AutoML Vision Edge offers efficient neural architecture and hyperparameter optimization, specifically tuned for image data samples. Efficient Neural Architecture Search (ENAS) is utilized to discover high-performance models with lower computational demands, a critical aspect for medical image analysis where resource constraints are common for different use cases. The model also incorporates customized loss functions, particularly Weighted Cross-Entropy Loss, addressing the class imbalance in medical datasets by emphasizing rarer tumor types. Spatial Dropout and Batch Normalization are integrated as regularization techniques to improve generalization and reduce overfitting risks. The model’s efficacy was validated on the Br35H, Kaggle Brain Tumor Dataset, and IEEE Data Port Dataset Databases, exhibiting a notable improvement over existing methods: 5.9% better precision, 6.5% higher accuracy, and 4.9% higher recall in brain tumor analysis. In survival analysis, the model demonstrated 8.5% better precision and 8.3% higher accuracy, among other improvements. These enhancements underscore the model’s capability in providing more accurate, efficient, and reliable predictions for brain tumor patients, potentially revolutionizing the approach to brain tumor diagnosis and survival prognostication in clinical settings.