Data Classification Research Articles

Classification of Skin Diseases using Decision Tree Algorithm on an Imbalanced Dataset

Skin infections caused by pathogens such as bacteria and fungi are common and can lead to serious health complications if not properly managed. Accurate classification of these infections is crucial for effective treatment and management. This study focuses on classifying two skin diseases, Chickenpox and Shingles, using a Decision Tree algorithm applied to an imbalanced dataset sourced from Kaggle. The dataset, which is imbalanced by nature, was split into training (80%) and testing (20%) subsets. Pre-processing involved segmentation using Thresholding to isolate regions of interest and feature extraction using Hu Moments to capture shape characteristics of the lesions. The dataset was scaled to ensure that all features had a mean of 0 and variance of 1. The classifier's performance was evaluated using 5-fold cross-validation, yielding a mean accuracy of 66.06%, with precision, recall, and F1-scores indicating moderate performance. The study highlights the challenges posed by imbalanced datasets and the limitations of the Decision Tree algorithm in this context. The results underscore the importance of proper pre-processing and feature extraction but also suggest the need for more advanced classification techniques and data balancing methods. This research contributes to the field by providing a detailed methodology and comprehensive evaluation metrics, offering insights into the application of machine learning for medical image classification. Future work should focus on improving classifier performance through data augmentation, advanced feature extraction, and exploring other machine learning models better suited for imbalanced datasets.

An Intelligent Kick Detection Model for Large-Hole Ultra-Deep Wells in the Sichuan Basin

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

Taxonomy and phylogeny of rodents parasitic fleas in southeastern China with description of a new subspecies of Ctenophthalmus breviprojiciens.

Accurate differentiation and identification of flea species are essential for both basic and applied research on fleas, as well as for the diagnosis of flea-borne diseases. However, distinguishing between flea species can be challenging, especially among those with minimal morphological differences. Therefore, some scholars have suggested the necessity of comprehensive revisions to the classification of fleas, incorporating morphological, molecular, and phylogenetic data. In this study, we focused on classifying the rodents' parasitic fleas in southeastern China and provided molecular and phylogenetic data. We also described a new subspecies Ctenophthalmus breviprojiciens fujiansis n. ssp. A total of 392 fleas were collected from 8 species of rodents in 10 counties. Morphologically, they belonged to 10 species, 9 genera and 5 families. Barcode identification based on COI gene and phylogenetic analysis based on five genetic markers (18S rDNA, 28S rDNA, EF-1a, COI, COII) revealed that the molecular and morphological identification of Xenopsylla cheopis, Aviostivalius klossi bispiniformis, Leptopsylla segnis, Monopsyllus anisus and Ctenocephalides felis felis were consistent. The taxonomic status of Neopsylla specialis minpiensis and Peromyscopsylla himalaica sinica as subspecies is questionable due to significant intraspecific genetic distance, and further morphological and molecular data are required to determine if they should be elevated to species level. The molecular identification of C. breviprojiciens n. ssp., N. dispar fukienensis, and Nosopsyllus nicanus could not be completed at this time due to a lack of sequences for related species in existing GenBank databases. Additionally, phylogenetic relationships of 31 species from 9 genera and 5 families of Siphonaptera were inferred based on five molecular markers (18S rDNA, 28S rDNA, EF-1a, COI and COII) using Maximum Likelihood analyses. The analyses revealed that various taxa of Siphonaptera are monophyletic at the subspecies, species, and genus levels. However, at the family level, Leptopsyllidae, Ceratophyllidae, Pulicidae, and Pygiopsyllidae are all monophyletic, while Ctenophthalmidae is paraphyletic. we support the view of some authors that revising the catchall group Ctenophthalmidae and elevating each of its constituent subfamilies to family status.

Conformal novelty detection for multiple metabolic networks.

Graphical representations are useful to model complex data in general and biological interactions in particular. Our main motivation is the comparison of metabolic networks in the wider context of developing noninvasive accurate diagnostic tools. However, comparison and classification of graphs is still extremely challenging, although a number of highly efficient methods such as graph neural networks were developed in the recent decade. Important aspects are still lacking in graph classification: interpretability and guarantees on classification quality, i.e., control of the risk level or false discovery rate control. In our contribution, we introduce a statistically sound approach to control the false discovery rate in a classification task for graphs in a semi-supervised setting. Our procedure identifies novelties in a dataset, where a graph is considered to be a novelty when its topology is significantly different from those in the reference class. It is noteworthy that the procedure is a conformal prediction approach, which does not make any distributional assumptions on the data and that can be seen as a wrapper around traditional machine learning models, so that it takes full advantage of existing methods. The performance of the proposed method is assessed on several standard benchmarks. It is also adapted and applied to the difficult task of classifying metabolic networks, where each graph is a representation of all metabolic reactions of a bacterium and to real task from a cancer data repository. Our approach efficiently controls - in highly complex data - the false discovery rate, while maximizing the true discovery rate to get the most reasonable predictive performance. This contribution is focused on confident classification of complex data, what can be further used to explore complex human pathologies and their mechanisms.

Nonlinear Deep Learning Framework for Precise Sentiment Classification in Textual Data

The study presents the Nonlinear Deep Learning Framework for Precise Sentiment Classification (NPSC), focusing on improving sentiment analysis in text data. The model addresses the challenge of capturing complex and hidden sentiment patterns using a combination of BERT embeddings, Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), and an attention mechanism. This setup helps in identifying subtle relationships within text, enabling more precise sentiment predictions. NPSC tackles common issues faced by existing models that struggle with detailed text analysis. Using the IMDB movie review dataset, the approach relies on BERT to convert words into dense vectors, BiLSTM for capturing context from both directions, and CNN to extract key local features. An attention layer highlights important words, refining the sentiment detection process. The integration of these components in a nonlinear fusion layer allows for enhanced feature interaction, leading to accurate classification through a final softmax layer. The findings show that NPSC performs better across metrics like accuracy, precision, recall, and F1-score. Ablation studies reveal the impact of each component, demonstrating their role in enhancing the model’s effectiveness. Error analysis shows how NPSC manages mixed sentiments and challenging text patterns. The study highlights NPSC’s potential as a reliable method for sentiment analysis, contributing to better handling of complex textual data.

Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm.

Prediction model and technical and tactical decision analysis of women's badminton singles based on machine learning.

In the Paris Olympic cycle, South Korean women's athlete An Se-young rose to the top of the 2023 BWF Olympic points with a win rate of 89.5%. With An Se-young as the subject, this paper aims to carry out technical and tactical analysis of women's badminton singles and formulate a prediction model based on machine learning. Firstly, An's technical and tactical statistics are analyzed and presented in a proposed "three-stage" data classification method. Secondly, we improve our "three-stage" machine learning dataset using video analysis of 10 matches (21 point games) where An Se-young faced off against four other players ranked in the top five of the World Badminton Federation (BWF) in week 44 of 2023. Finally, we establish a prediction model for the scoring and losing of points in the women's badminton singles based on the 'Decision tree', 'Random forest', 'XGBoost', 'Support vector' and 'K-proximity' algorithms, and analyze the effectiveness of this model. The results show that the improved data classification is reasonable and can be used to predict the final score of a match. When the support vector machine uses the RBF function kernel, the accuracy reaches its highest at 87.5%, and the consistency of this prediction model is strong. An's playstyle is sustained and unified; she does not seek continuous pressure, but rather exploits and maximizes her aggression following any mistake made by her opponents, immediately utilizing assault methods such as kills or dives, often resulting in the conversion of points during the subsequent 2-3 strikes.

Efficient Data Recognition and Classification in IoT Ecosystem using Optimized K-Means Algorithm and Hybrid Deep Learning Model

Efficient Data Recognition and Classification in IoT Ecosystem using Optimized K-Means Algorithm and Hybrid Deep Learning Model

Algorithm for intelligent procesing of text information

The purpose of research. The goal of the research is to develop an intellectual processing algorithm for classifying text information. As the amount of information grows every day, it is necessary to quickly and efficiently separate significant from unimportant content. Therefore, the development of an intellectual processing algorithm for classifying text information is an urgent task.Methods. A method is proposed for classifying text information presented in one or more natural languages. It is based on 5 key stages: entering a task, accumulating a queue of tasks, processing the task, generating the result of processing the task, outputting the result. The input task is presented in the form of an http request, the body of which contains a file object. If the intensity of the input stream is greater than the processing speed, then an accumulation of tasks occurs. After selecting the active task (using the FIFO principle), it is processed. As a result of the transformations, the received data is decoded into a string using UTF-8 encoding. Processing refers to the process of categorization, when a search for patterns occurs in a line. Upon completion of rubrication, the result for the selected task is generated. From the accumulated result, a response to the original http request is formed, the body of which contains a list of found categories.Results. A method and algorithm for processing text data has been developed to determine the topics that are present in the input data set. The algorithm, implemented in software, allows you to work with text data in various languages.Conclusion. The software development of the text data classification algorithm was carried out in the C++ programming language using the Qt libraries version 5.11. This implementation showed a throughput of 1-5 MB per second (on a homogeneous input text data set). The algorithm allows you to correctly process damaged file formats.

Machine learning-based methods for piecewise digital predistortion in mmW 5G NR systems

AbstractPiecewise linearization techniques require dividing the signal into multiple pieces each linearized individually. Machine learning (ML) is one of the useful tools to perform the automatic division of these pieces. Complexity reduction in the classification of piecewise digital predistortion is possible through carefully constructing features from both the signal statistics and the power amplifier (PA) characteristics. Our paper introduces two low-complex classical ML-based methods that facilitate the classification of baseband input data into distinct segments. These methods effectively linearize PA behavior by employing tailored Volterra models corresponding to each segment. Moreover, we perform an in-depth analysis of the proposed schemes to further optimize their classification and regression complexities. The two proposed low-complexity approaches are validated by laboratory experiments and show up to 4 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz. Similarly, the EVM improvement is up to 2 dB over the vector-switched general memory polynomial scheme. With only one indirect learning architecture iteration, the two proposed schemes obey the 5G new radio standard up to 6.5 dB and 7 dB output backoff, respectively.

Learning to predict perceptual visibility of rendering deterioration in computer games.

Contemporary computer gaming affords players the agency to manually tailor rendering settings, a capability crucial for optimizing computational demands following their hardware performance. Specifically, adjustments to texture resolution, shadow map intricacies, and anti-aliasing complexities facilitate seamless animation generation, particularly on systems equipped with budget-friendly graphical units. Nonetheless, the intricacy and extensive interdependencies among these rendering parameters render selecting an optimal configuration a multifaceted challenge. Our approach involves training a proprietary convolutional neural network (CNN) to streamline this intricate process. This CNN quantitatively compares reference images rendered at peak quality to their counterparts subjected to quality reduction. Its function involves identifying and classifying artifacts within the altered images, and evaluating their perceptibility to human observers. Our neural network undergoes rigorous training using an expansive dataset derived from prevalent game engine scenes. A meticulous process to establish accurate classification data involves manually annotating image regions afflicted by diminished quality. As a testament to our methodology, we implement a prototype forward renderer based on OpenGL. Employing the trained neural network within this application enables the evaluation of image quality across diverse anti-aliasing settings, culminating in identifying settings minimizing artifact visibility. Empirical validation via user studies substantiates our network's efficacy, demonstrating its superior discernment of artifact visibility compared to established image quality metrics.

Investigation of Freeway Incident Duration Using Classification and Regression Trees Based on Multisource Data

Targeted contingency measures have proven highly effective at reducing the duration and harm caused by incidents. This study utilized the Classification and Regression Trees (CART) data mining technique to predict and quantify the duration of incidents. To achieve this, multisensor data collected from the Hangzhou freeway in China spanning from 2019 to 2021 was utilized to construct a regression tree with eight levels and 14 leaf nodes. By extracting 14 rules from the tree and establishing contingency measures based on these rules, accurate incident assessment and effective implementation of post-incident emergency plans were achieved. In addition, to more accurately apply the research findings to actual incidents, the CART method was compared with XGBoost, Random Forest (RF), and AFT (accelerated failure time) models. The results indicated that the prediction accuracy of the CART model is better than the other three models. Furthermore, the CART method has strong interpretability. Interactions between explanatory variables, up to seven, are captured in the CART method, rather than merely analyzing the effect of individual variables on the incident duration, aligning more closely with actual incidents. This study has important practical implications for advancing the engineering application of machine learning methods and the analysis of sensor data.

Geo-classification of drug-resistant travel-associated Plasmodium falciparum using Pfs47 and Pfcpmp gene sequences (USA, 2018-2021).

Travel-related malaria is regularly encountered in the United States, and the U.S. Centers for Disease Control and Prevention (CDC) characterizes Plasmodium falciparum drug-resistance genotypes routinely for travel-related cases. An important aspect of antimalarial drug resistance is understanding its geographic distribution. However, specimens submitted to CDC laboratories may have missing, incomplete, or inaccurate travel data. To complement genotyping for drug-resistance markers Pfcrt, Pfmdr1, Pfk13, Pfdhps, Pfdhfr, and PfcytB at CDC, amplicons of Pfs47 and Pfcpmp are also sequenced as markers of geographic origin. Here, a bi-allele likelihood (BALK) classifier was trained using Pfs47 and Pfcpmp sequences from published P. falciparum genomes of known geographic origin to classify clinical genotypes to a continent. Among P. falciparum-positive blood samples received at CDC for drug-resistance genotyping from 2018 to 2021 (n = 380), 240 included a travel history with the submission materials, though 6 were excluded due to low sequence quality. Classifications obtained for the remaining 234 were compared to their travel histories. Classification results were over 96% congruent with reported travel for clinical samples, and with collection sites for field isolates. Among travel-related samples, only two incongruent results occurred; a specimen submitted citing Costa Rican travel classified to Africa, and a specimen with travel referencing Sierra Leone classified to Asia. Subsequently, the classifier was applied to specimens with unreported travel histories (n = 140; 5 were excluded due to low sequence quality). For the remaining 135 samples, geographic classification data were paired with results generated using CDC's Malaria Resistance Surveillance (MaRS) protocol, which detects single-nucleotide polymorphisms in and generates haplotypes for Pfcrt, Pfmdr1, Pfk13, Pfdhps, Pfdhfr, and PfcytB. Given the importance of understanding the geographic distribution of antimalarial drug resistance, this work will complement domestic surveillance efforts by expanding knowledge on the geographic origin of drug-resistant P. falciparum entering the USA.

Accuracy of immunological tests on serum and urine for diagnosis of Taenia solium neurocysticercosis: A systematic review.

Taenia solium neurocysticercosis is a zoonotic neglected tropical disease, for which adequate diagnostic management is paramount, especially in patients with active cysts for whom improved and timely management could prove beneficial. Immunodiagnosis can potentially partially mitigate the necessity for neuroimaging, shortening the diagnostic -and treatment- pathway. An up-to-date review of immunological test performance is however lacking. Searches were performed in PubMed, EMBASE, Web of Science, and Scopus (up to January 2024), with included records fitting the review scope, i.e. accuracy evaluation of an antibody-/or antigen-detecting immunological test, using serum or urine of humans confirmed via reference standard (i.e. neuroimaging or surgery/biopsy). Record data was assessed, with classification of descriptive data on cyst localization and stage according to a developed confidence scale, and with selection of tests evaluated on a sufficiently high sample size. A QUADAS-2 risk of bias assessment was performed. After screening, 169 records were included for data collection, with 53 records-corresponding to 123 tests- selected for analysis. Absence of data and large data heterogeneity complicated result interpretation. The lentil lectin-bound glycoprotein enzyme-linked immunoelectrotranfser blot seems to fulfill high accuracy standards regarding detection of parenchymal active multiple cysts; also antigen-detecting tests on serum and urine performed well, additionally in detection of extraparenchymal neurocysticercosis. A novel multi-antigen print immunoassay is highly promising, with sensitivity for detection of extraparenchymal and parenchymal active single and multiple cysts of 100.0%, and specificity of 98.5%. Point-of-care tests showed promising results, however require further evaluation in targeted resource-poor settings. The review highlights the importance of transparent and unambiguous data reporting. With promising immunological tests in development, the challenge before usage in targeted settings will be to perform large-scale evaluations whilst holding into account both optimized test performance and ease of use. Accessibility to validated tests and feasibility of implementation should also be considered.

Advancing user classification models: A comparative analysis of machine learning approaches to enhance faculty password policies at the University of Buraimi

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

Financial Challenges Faced by IELTS Test-Takers in Sylhet, Bangladesh: An Exploration of Family Support and Assistance

This study explores the sources of financial assistance available to IELTS test-takers, the motivating factors for taking the test, and the influence of family background on these decisions. The research has adopted a tabular analysis study design to collect the required data and information about IELTS test-takers. Quantitative techniques were applied to gather the necessary primary data and information. This study also adopted In-depth Interview (IDI), Case Studies, and Document Review techniques. Data were collected in Sylhet through coaching centers and an online Google form between June and October 2024, specifically from six centers in Modina Market, Amborkhana, Zindabazar, and Uposhohor. The questionnaire was distributed to 190 individuals, comprising 132 participants currently preparing for a retake. After excluding data of 14 participants due to missing information, 176 responses were analyzed. MS Excel and STATA were employed for data classification and interpretation. Authors used MS Excel for data entry, data cleaning, and tabular analysis and STATA-16 for summary statistics. Female participation in IELTS test preparation is impressive and closely comparable to that of male participants. Additionally, a higher number of students take the IELTS exam in Sylhet district compared to the other three districts. Most of the time, 76.70% of the participants’ family (Parents/Brother/Sister) covered their whole cost of IELTS test and Family members often play a key role in encouraging individuals to pursue the test. IELTS test-takers from low-income families face significant difficulties in covering tuition fees and other expenses related to their preparation, coaching such as the cost of academic materials and center fees. The findings reveal that many test-takers struggle to afford the costs of preparing for the exam. As a student in a low-income country, Bangladesh, many individuals face challenges in covering expenses beyond necessities like food and shelter. Therefore, the study recommends scholarship opportunities and special consideration for participants who are facing financial challenges. The researchers and policy makers of IELTS test, should give a focus to their test fee which will be helpful for students of a developing country.

A New Classification Model Using a Decision Tree Generated from Hyperplanes in Dimensional Space

ABSTRACT One of the issues addressed by machine learning, with applications in various disciplines or fields such as the health sector and the agricultural sector among others, involves data classification. For this purpose, various models within supervised learning have been proposed and developed that allow for the classification of these data. However, one of the implications of the No-Free-Lunch theorems is that there is no optimal general-purpose model, i.e. there is no classifier model that achieves the best results for all problems presented. Hence the importance of proposing and implementing new classifier models, evaluating their performance, and comparing them with other classifier models in order to achieve models with good results in specific problems. This work presents a new classifier model that, by constructing hyperplanes from the training set, generates a decision tree and partitions the dimensional space. The proposed model was applied to different problems such as the XOR logical function problem, where the proposed model managed to solve the problem, it was also applied to the Iris Dataset where one of the trees generated by the model managed to classify with 100% accuracy the test set, and finally, the proposed model was applied to the Pima Indians Diabetes Database and compared with other models using the accuracy value. The proposed model obtained an accuracy of 81.81%, achieving the best result in the same way as the Random Forest Classifier. The results obtained in this work show that the proposed model manages to partition the dimensional space adequately from the training set and thus competitively classify the data with other state-of-the-art models.

Performance of Minnesota Functionals on Vibrational Frequency

ABSTRACTMolecular geometry and harmonic frequency calculations are essential in thermochemical computations, with density functional theory (DFT) being widely employed for vibrational frequency predictions due to its efficiency and accuracy. In this study, we assessed the precision of 28 Minnesota based functionals with three different basis sets using the VIBFREQ1295 dataset. Scaling factors are necessary for predicting fundamental frequencies, global scaling factors were fitted by using F38/10 and VIBFREQ1295 datasets. The superior performing functionals were then fitted based on vibrational frequency ranges to obtain frequency‐range‐specific scaling factors. We observed consistent outlier across various model chemistries in vibrational frequency predictions, with alternative scaling factors showing minimal impact on reducing outlier occurrences. Besides, large basis sets are not indispensably required for fundamental frequency predictions. M06‐L, revM06‐L, SOGGA11‐X, PW6B95‐D3(BJ), CF22D, and M06‐2X consistently exhibit excellent performance across the three basis sets. When using frequency‐range‐specific scaling factors, the root mean squard errors (RMSEs) and median absolute errors (MedAEs) of almost all the selected functionals were reduced. PW6B95‐D3(BJ), CF22D, and MN12‐SX exhibited the lowest RMSEs. Comparisons were also done for different data classifications; the dataset was classified by the elements of the molecules, vibrational frequency intervals, and vibrational modes.

Classification of hand movements from EEG using a FusionNet based LSTM network.


Accurate classification of electroencephalogram (EEG) signals is crucial for advancing brain-computer interface (BCI) technology. However, current methods face significant challenges in classifying hand movement EEG signals, including effective spatial feature extraction, capturing temporal dependencies, and representing underlying signal dynamics.
Approach.
This paper introduces a novel multi-model fusion approach, FusionNet-LSTM, designed to address these issues. Specifically, it integrates Convolutional Neural Networks (CNN) for spatial feature extraction, Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and Autoregressive (AR) models for representing signal dynamics.
Main results.
Compared to single models and state-of-the-art methods, this fusion approach demonstrates substantial improvements in classification accuracy. Experimental results show that the proposed model achieves an accuracy of 87.1% in cross-subject data classification and 99.1% in within-subject data classification. Additionally, Gradient Boosting Trees were employed to evaluate the significance of various EEG features to the model.
Significance.
This study highlights the advantages of integrating multiple models and introduces a superior classification model, which is pivotal for the advancement of BCI systems.

Student experiences of cultural competence encounters during MPharm placements and foundation year training

Abstract Introduction Cultural competence is the ability of a healthcare professional to work effectively with clients from different backgrounds. With 18.3% of the UK population identifying with non-white1, pharmacists need to be culturally competent as they have a patient-facing role. Having a multicultural society would require culturally competent pharmacists to effectively deliver healthcare services to patients of different cultures. Exploring pharmacy students' experiences of CC encounters during their MPharm placements and foundation year training is crucial as it helps identify the challenges and barriers they may face when providing healthcare to diverse populations. Understanding these experiences can inform the development of effective training programs to enhance cultural competence among future pharmacists and highlight the impact of cultural factors on patient care and outcomes. Ultimately, this exploration contributes to the overall goal of improving healthcare delivery and promoting patient-centred, culturally sensitive pharmacy practice. Aim This study aimed to explore experiences of student regarding cultural competence encounters during placements and training. Methods A qualitative study was conducted between Oct – Dec 2023. All currently enrolled students in Parts 3, 4 and the foundation year were eligible to participate. Convenience sampling was used. Semi-structured interviews were conducted online using a pilot-tested script. Interviews were audio and video recorded and held online through a Microsoft Teams account, where an automatic electronic transcript was generated. Initial codes were generated from each transcript, to create themes and sub-themes. The researcher scrutinised the themes before defining and naming them. Ethical clearance was obtained. Results A total of 15 students (8 males and 7 females) participated in the study. Four participants were from year 3, five were from year 4, and six were from the foundation year. Six participants were from Asian backgrounds, four were Black, and three were White. All students had a placement in hospital, while 14 had a placement in community pharmacy in addition to hospital, and 8 had a placement in a GP surgery in addition to the hospital and community pharmacy. Key themes identified were 1) Impact of culture on patient health including language barriers and ethnic diversity, 2) Influence of personal culture on practice during placements and training such as using patient’s language to communicate, personal cultural awareness, and the influence of religious teachings, 3) Recommendations for improving cultural competence training in MPharm programme such as having more case studies. Conclusion This study provides a unique exploration into the experiences of pharmacy students and shows the importance of immersing in diverse cultural groups during placements and foundation training. There is a need to enhance pharmacy students’ training through curricular reforms that build on theoretical knowledge to instil confidence during placements and training. This approach will equip future pharmacists to navigate complex cultural dynamics, to meet the demands of an inclusive healthcare system. Ultimately this research extends beyond the current reforms to the MPharm curriculum, and advocates for an equitable and inclusive healthcare system to reduce health disparities. The predominance of participants from Asian backgrounds may limit the generalisability of the findings. Reference 1. Office for National Statistics. Measuring equality: A guide for the collection and classification of ethnic group, national identity and religion data in the UK. Office for National Statistics 2021 https://www.ons.gov.uk/methodology/classificationsandstandards/measuringequality/ethnicgroupnationalidentityandreligion. Accessed 20 Dec 2023.