Conventional Machine Learning Algorithms Research Articles

Minirocket Kullanarak Güçlendirilmiş ve Verimli Atriyal Fibrilasyon Tespiti

Atrial Fibrillation (AF) detection from intracardiac Electrogram (EGM) signals is a critical aspect of cardiovascular health monitoring. This study explores the application of Minirocket, a time series classification (TSC) algorithm, for robust and efficient AF detection. A comparative analysis is conducted against a deep learning approach using a subset of the dataset from Rodrigo et al. (2022). The study investigates the robustness of Minirocket in the face of shorter EGM sequences and varying training sizes, essential for real-world applications such as wearable and implanted devices. Empirical runtime analysis further assesses the efficiency of Minirocket in comparison to conventional machine learning (ML) algorithms. The results showcase Minirocket's notable performance, especially in scenarios with shorter signals and varying training sizes, making it a promising candidate for streamlined AF detection in emerging cardiovascular monitoring technologies. This research contributes to the optimization of AF detection algorithms for increased efficiency and adaptability to dynamic clinical scenarios.

A comprehensive study on automatic non‐informative frame detection in colonoscopy videos

AbstractDespite today's developing healthcare technology, conventional colonoscopy is still a gold‐standard method to detect colon abnormalities. Due to the folded structure of the intestine and visual disturbances caused by artifacts, it can be hard for specialists to detect abnormalities during the procedure. Frames that include artifacts such as specular reflection, improper contrast levels from insufficient or excessive illumination gastric juice, bubbles, or residuals should be detected to increase an accurate diagnosis rate. In this work, both conventional machine learning and transfer learning methods have been used to detect non‐informative frames in colonoscopy videos. The conventional machine learning part consists of 5 different types of texture features, which are gray level co‐occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray‐tone difference matrix (NGTDM), focus measure operators (FMOs), and first‐order statistics. In addition to these methods, we utilized 8 different transfer learning models: AlexNet, SqueezeNet, GoogleNet, ShuffleNet, ResNet50, ResNet18, NasNetMobile, and MobileNet. The results showed that FMOs and decision tree combination gave the best accuracy and f‐measure values with almost 89% and 0.79%, respectively, for the conventional machine learning part. When the transfer learning part is taken into account, AlexNet (99.85%) and SqueezeNet (98.80%) have the highest performance metric results. This study shows the potential of both transfer learning and conventional machine learning algorithms to provide fast and accurate non‐informative frame detection to be used during a colonoscopy, which may be considered the initial step in identifying and classifying colon‐related diseases automatically to help guide physicians.

Estimation of photosynthetic parameters from hyperspectral images using optimal deep learning architecture

The maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) of leaves are crucial for comprehending carbon cycling in farmland. Nevertheless, estimating these photosynthetic parameters precisely and rapidly faces a considerable challenge. This study designed an optimal deep learning architecture that accurately extracts photosynthetic parameters from hyperspectral images and evaluated its stability across different crop species. Photosynthetic parameter models jointly driven by hyperspectral images and deep learning were compared with models jointly driven by one-dimensional reflectance and simple machine learning methods, as well as chlorophyll driven models. The proposed optimal deep learning architecture incorporated spatial attention and prior knowledge of spectral indices calculations. Our results demonstrated that the hyperspectral images and deep learning jointly driven models outperformed traditional models. Notably, incorporating the module of spatial attention and spectral indices calculation networks achieved the best estimations for Vcmax25 (R2: 0.86, RMSE: 10.18 μmol m−2 s−1) and Jmax25 (R2: 0.83, RMSE: 24.27 μmol m−2 s−1). In contrast, the performance of the one-dimension reflectance driven models deteriorated (R2: 0.43–0.58, RMSE: 19.71–39.99 μmol m−2 s−1). Moreover, the best architecture was interpreted. Weight analysis revealed that the hyperspectral information on the middle part of the leaf contributed most to photosynthetic parameters. Feature map analysis indicated that the spectral indices calculation module utilized the information of the visible light spectrum. Even though the migration of models across various crop species may lead to a slight degradation, the model performance remained satisfactory after fine-tuning. The proposed deep learning models, which used 3D hyperspectral images for estimation of photosynthetic parameters, outperformed the models jointly driven by 1D reflectance data and conventional machine learning algorithms. The results highlighted the significance of spatial information from hyperspectral images and prior knowledge through spectral indices calculations. Moreover, the stability and effectiveness of the proposed architecture remained excellent across different species. This study presents advanced and highly effective deep learning techniques for evaluating the photosynthetic capacity of crop leaves and modeling the carbon cycle.

XAI Transformer based Approach for Interpreting Depressed and Suicidal User Behavior on Online Social Networks

Online social networks can be used for mental healthcare monitoring using Artificial Intelligence and Machine Learning techniques for detecting various mental health disorders and corresponding risk assessment. Recent research in this domain has primarily been focused on leveraging deep neural networks and various Transformer based Large Language Models, which have now become state-of-the-art for most natural language processing and computational linguistic tasks due to their unmatched prediction accuracy. Unlike conventional machine learning algorithms, these deep neural networks are black box architectures, where it is difficult to interpret and explain their predicted outcome. However, a black box classification outcome is insufficient for healthcare applications. Such systems will not be widely adopted and trusted by healthcare practitioners if they are not able to understand and explain the reasoning behind the predicted decisions made by an AI and ML based healthcare diagnostic system. The key objective of our research is to demonstrate the applications of model agnostic, post hoc surrogate XAI techniques for providing explainability to classification decisions of pretrained LLMs (Transformers) based mental healthcare diagnostic systems fine-tuned (or trained) to detect depressive and suicidal behavior using UGC from online social networks. For this, we have used the two most recent and popular techniques, SHAP and LIME. We have conducted extensive and in-depth experiments with four datasets and six pretrained LLMs, three of which have already been domain-adapted using mental health related datasets. We have also performed Few Shot Learning experiments with these three pretrained mental health domain-adapted LLMs. The results of qualitative and descriptive data analysis in this paper demonstrate that in order to build a comprehensive understanding of a person’s psychological state, emotion, and behavior and to discover the causes, symptoms, and triggers of mental health issues, it is essential to utilize eXplAInable (XAI) techniques with Transformer based LLMs (supervised). Alternatively, Transformer based unsupervised topic modeling technique BERTopic may be used for mental health risk monitoring and cause or symptom extraction when supervised training of LLMs is not feasible due to dataset annotation or availability challenges.

Predicting peak deviatoric stress of gravels with a novel segmented feature transformation approach

The assessment of peak deviatoric stress (qpeak) in gravels is crucial for engineering stability and safety. Due to the various influencing factors and the limited available samples, present empirical methods and conventional machine learning algorithms have limitations in generalization capability and achieving high performance in this complex relationship. This paper proposes a novel deep learning-based model termed Seft-Net. The model utilizes a segmented feature transformation (Seft) approach to improve convergence and robustness by leveraging prior knowledge that grouping gravel properties into three categories: particle (e.g., shape, hardness), soil mass (e.g., gradation, void ratio), and external factors (e.g., confining pressure). Subsequently, it applies a feature transformation module using depthwise separable convolutions to learn and fuse hierarchical representations across multiple layers. This enables Seft-Net to automatically extract high-level features from gravel properties that capture intricate nonlinear relationships with qpeak. The model is trained and validated using a large-scale practical dataset from triaxial test results. Ablation studies confirmed the superiority of the proposed segmented feature transformation approach over the unsegmented approach, and the feature transformation module demonstrated optimal feature extraction capabilities. Comparative evaluations with three machine learning methods further validated the superior performance of Seft-Net.

Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems.

An Overview of Inflammatory Spondylitis for Biomedical Imaging Using Deep Neural Networks

Ankylosing Spondylitis (AS) is an axial spine inflammatory illness and also chronic that might present with a range of clinical symptoms and indicators. The illness is most frequently characterized by increasing spinal stiffness and persistent back discomfort. The affect of the sacroiliac joints, spine, peripheral joints, entheses and digits are the main cause of the illness. AS symptoms include reduced spinal mobility, aberrant posture, hip and dactylitis, enthesitis, peripheral arthritis, and buttock pain. With their exceptional picture classification ability, the diagnosis of AS illness has been transformed by deep learning techniques in artificial intelligence (AI). Despite the excellent results, these processes are still being widely used in clinical practice at a moderate rate. Due to security and health concerns, medical imaging applications utilizing deep learning must be viewed with caution. False instances, whether good or negative, have far-reaching effects on the well-being of patients and these are to be considered. These are extracted from the fact of the state-of-the-art of deep learning (DL) algorithms lack internal workings comprehension and have complicated interconnected structure, huge millions of parameters, and also a "black box" aspect compared to conventional machine learning (ML) algorithms. XAI (Explainable AI) approaches make it easier to comprehend model predictions, which promotes system reliability, speeds up the diagnosis of the AS disease, and complies with legal requirements.

Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine

The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.

Enhancing Breast Cancer Prediction through Deep Learning and Comparative Analysis of Gene Expression and DNA Methylation Data using Convolutional Neural Networks

Recent advances in the production of statistics have resulted in an exponential increase in the number of facts, ushering in a whole new era dominated by very large facts. Conventional machine-learning algorithms are unable to handle the most recent aspects of huge data. This is a fact. In order to make an accurate prognosis of breast cancer, researchers employ and evaluate three distinct computer programmes called Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). Within the context of huge statistics, we explore the question of how breast cancer may be predicted in this particular research. Gene expression and DNA methylation are both taken into consideration as part of the analysis (GE and DM, respectively). The purpose of the work that we are doing is to increase the capacity of the Deep Learning algorithms that are now being used for typing by applying each dataset individually and together. As a result of this decision, the platform of choice is MATLAB. In the process of breast cancer prediction, the Convolutional Neural Network (CNN) algorithm is used. Comparisons of GE, DM, and GE and DM are carried out with the help of this method. The results of the CNN algorithm are compared to those of the RF algorithm. According to findings of the experiments, the scaled system that was presented works better than the other classifiers. This is due to the fact that using the GE dataset; it acquired the best accuracy at the lowest cost.

Curing process monitoring of polymeric composites with Gramian angular field and transfer learning-boosted convolutional neural networks

Continuous and accurate monitoring of the degree of curing (DoC) is essential for ensuring the structural integrity of fabricated composites during service. Although machine learning (ML) has shown effectiveness in DoC monitoring, its generalization and extendibility are limited when applied to other curing-related scenarios not included in the previous learning process. To break through this bottleneck, we propose a novel DoC monitoring approach that utilizes transfer learning (TL)-boosted convolutional neural networks alongside Gramian angular field-based imaging processing. The effectiveness of the proposed approach is validated through experiments on metal/polymeric composite co-bonded structures and carbon fiber reinforced polymers using raw sensor data separately collected through the electromechanical impedance and fiber Bragg grating (FBG) measurements. Four indicators, accuracy, precision, recall, and F1-score are introduced to evaluate the performance of generalization and extendibility of the proposed approach. The indicator scores of the proposed approach exceed 0.9900 and outperform other conventional ML algorithms on the FBG dataset of the target domain, demonstrating the effectiveness of the proposed approach in reusing the pre-trained base model on the composite curing monitoring issues.

MalHyStack: A hybrid stacked ensemble learning framework with feature engineering schemes for obfuscated malware analysis

Since the advent of malware, it has reached a toll in this world that exchanges billions of data daily. Millions of people are victims of it, and the numbers are not decreasing as the year goes by. Malware is of various types in which obfuscation is a special kind. Obfuscated malware detection is necessary as it is not usually detectable and is prevalent in the real world. Although numerous works have already been done in this field so far, most of these works still need to catch up at some points, considering the scope of exploration through recent extensions. In addition to that, the application of a hybrid classification model is yet to be popularized in this field. Thus, in this paper, a novel hybrid classification model named, MalHyStack, has been proposed for detecting such obfuscated malware within the network. This proposed working model is built incorporating a stacked ensemble learning scheme, where conventional machine learning algorithms namely, Extremely Randomized Trees Classifier (ExtraTrees), Extreme Gradient Boosting (XgBoost) Classifier, and Random Forest are used in the first layer which is then followed by a deep learning layer in the second stage. Before utilizing the classification model for malware detection, an optimum subset of features has been selected using Pearson correlation analysis which improved the accuracy of the model by more than 2 % for multiclass classification. It also reduces time complexity by approximately two and three times for binary and multiclass classification, respectively. For evaluating the performance of the proposed model, a recently published balanced dataset named CIC-MalMem-2022 has been used. Utilizing this dataset, the overall experimental results of the proposed model represent a superior performance when compared to the existing classification models.

Comparative study of a newly proposed machine learning classification to detect damage occurrence in structures

Over the past two decades, an increasing number of large-scale structures have been built around the world. Constructing these structures has been a time consuming and highly expensive process. Thus, providing a structural health monitoring system to guarantee their proper functionality is important. In recent years, the advancement of technology and artificial intelligence methods based on signal processing and machine learning has attracted the attention of researchers. The challenges currently exist in the field of structural health monitoring to identify and classify damages to achieve high accuracy in a health-monitoring program. The presence of noise in measurement, various exciting load types, and varying environmental conditions cause difficulty in the practical identification and classification of damage in structures. Recent studies have employed finite element modeling to test the effectiveness of proposed methods for identifying damages in structures. However, detecting damage in real-world structures as mentioned above, presents unique difficulties, and the effectiveness of the proposed methods for damage detection in real-world structures remains uncertain. In order to improve the performance of damage detection methods and increase the accuracy of these methods as much as possible, the most important action is to identify damage sensitive data in the structure. The next challenge is to choose a high performance algorithm for damage identification and classification. One of the advanced algorithms, which has a very high ability to extract the desired features from the measured data, is the XGBoost algorithm. This algorithm has recently attracted the attention of researchers and has been used in different fields. So far, the ability of this algorithm has not been examined in the field of damage detection in order to extract desirable features. This article deals with the identification, classification, and severity of damages in the SMC benchmark bridge, which is an existing megastructure in the real world, as well as the IASC-ASCE benchmark structure, whose responses were taken under applied loads in the laboratory environment. First, using the XGBoost algorithm, the importance of the features extracted from the sensors' data is evaluated, and then the features, which are effective in the damage detection process, are selected. The results of this algorithm indicate that only by selecting 6 features from a large volume of data, the best performance can be achieved and selecting more does not help increase efficiency. In the next step, the Stacking method, which is a hybrid machine learning algorithm for damage classification, is evaluated and compared with some conventional machine learning algorithms that have been used in previous studies. The Stacking method stands out as the top performer with an average accuracy rate of 93.1%, leading to the conclusion that it is the most effective approach. Finally, by applying the presented algorithm to the two mentioned structures, its validation is appraised.

TextNetTopics Pro, a topic model-based text classification for short text by integration of semantic and document-topic distribution information.

With the exponential growth in the daily publication of scientific articles, automatic classification and categorization can assist in assigning articles to a predefined category. Article titles are concise descriptions of the articles' content with valuable information that can be useful in document classification and categorization. However, shortness, data sparseness, limited word occurrences, and the inadequate contextual information of scientific document titles hinder the direct application of conventional text mining and machine learning algorithms on these short texts, making their classification a challenging task. This study firstly explores the performance of our earlier study, TextNetTopics on the short text. Secondly, here we propose an advanced version called TextNetTopics Pro, which is a novel short-text classification framework that utilizes a promising combination of lexical features organized in topics of words and topic distribution extracted by a topic model to alleviate the data-sparseness problem when classifying short texts. We evaluate our proposed approach using nine state-of-the-art short-text topic models on two publicly available datasets of scientific article titles as short-text documents. The first dataset is related to the Biomedical field, and the other one is related to Computer Science publications. Additionally, we comparatively evaluate the predictive performance of the models generated with and without using the abstracts. Finally, we demonstrate the robustness and effectiveness of the proposed approach in handling the imbalanced data, particularly in the classification of Drug-Induced Liver Injury articles as part of the CAMDA challenge. Taking advantage of the semantic information detected by topic models proved to be a reliable way to improve the overall performance of ML classifiers.

Predicting the risk stratification of gastrointestinal stromal tumors using machine learning-based ultrasound radiomics.

This study aimed to use conventional ultrasound features, ultrasound radiomics, and machine learning algorithms to establish a predictive model to assess the risk of post-surgical recurrence of gastrointestinal stromal tumors (GISTs). This retrospective analysis included 230 patients with pathologically diagnosed GISTs. Radiomic features were extracted from manually annotated images. Radiomic features plus conventional ultrasound features were selected using the SelectKbest analysis of variance and stratified tenfold cross-validation recursive elimination methods. Finally, five different machine learning algorithms (logistic regression [LR], support vector machine [SVM], random forest [RF], extreme gradient boosting [XGBoost], and multilayer perceptron [MLP]) were established to predict risk stratification of GISTs. The predictive performance of the established model was mainly evaluated based on the area under the receiver operating characteristic (ROC) curve (AUC) and accuracy, whereas the predictive performance of the optimal machine learning algorithm and a radiologist's subjective assessment were compared using McNemar's test. Seven radiomics features and one conventional ultrasound feature were selected to construct the machine learning models for GIST risk classification. The mentioned five machine learning models were able to predict the malignant potential of GISTs. LR and SVM outperformed other classifiers on the test set, with LR achieving an accuracy of 0.852 (AUC, 0.881; sensitivity, 0.871; specificity, 0.826) and SVM achieving an accuracy of 0.852 (AUC, 0.879; sensitivity, 0.839; specificity, 0.870), and proved significantly better than the radiologist (accuracy, 0.691; sensitivity, 0.645; specificity, 0.813). Machine learning-based ultrasound radiomics features are able to noninvasively predict the biological risk of GISTs.

A Survey on Deep Learning Based Forest Environment Sound Classification at the Edge

Forest ecosystems are of paramount importance to the sustainable existence of life on earth. Unique natural and artificial phenomena pose severe threats to the perseverance of such ecosystems. With the advancement of artificial intelligence technologies, the effectiveness of implementing forest monitoring systems based on acoustic surveillance has been established due to the practicality of the approach. It can be identified that with the support of transfer learning, deep learning algorithms outperform conventional machine learning algorithms for forest acoustic classification. Further, a clear requirement to move the conventional cloud-based sound classification to the edge is raised among the research community to ensure real-time identification of acoustic incidents. This article presents a comprehensive survey on the state-of-the-art forest sound classification approaches, publicly available datasets for forest acoustics, and the associated infrastructure. Further, we discuss the open challenges and future research aspects that govern forest acoustic classification.

An interpretable decision tree ensemble model for imbalanced credit scoring datasets

Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.

Detection of types cyber-bullying using fuzzy c-means clustering and xgboost ensemble algorithm

In this study a neural network model (XGB_CTD) that will prediction which type of bullying the users may expose to, through dataset gained by the cyberbullying scale applied to the young internet users is formulated. Extreme Gradient Boosting (XGboost) algorithm, one of the ensemble learning methods is used in this method. There while this model contains 13 input parameters taken from the scale, there exist one output parameter classified one of the 9 outputs. The reliability of the data set obtained through survey is confirmed by statistical methods. Data set has been fragmented with Fuzzy C-Means (FCM) which is one of fuzzy clustering algorithms. Hyper-parameters for the maximum efficiency of the model training have been defined as model, learning and boosting method. Independent variables in data set have been scaled through standard normalization. As a result, the model has yielded % 91,75 accuracy rate in prediction of the classification as 9 different cyberbullying types. The same data set has been trained by different machine learning algorithms. It is seen that the proposed model has reached the highest accuracy when compared to the conventional machine learning algorithms. This study aims at prediction cyberbullying through the proposed model including different questions without claim by the young users as they were bullied. Similarly, type of the cyberbullying will also be able to be estimated by the help of internet using habits of the young users. Therefore, it is thought that the young can be prevented from experiencing psychological pressure or digital life fear.

Lithium-Ion Battery State-of-Health Estimation Method Using Isobaric Energy Analysis and PSO-LSTM

The precise estimation of the state of health (SOH) for lithium-ion batteries (LIBs) is one of the core problems for battery management systems. To address the problem that it is difficult to accurately evaluate SOH because of the LIB capacity regeneration phenomenon, this paper proposes an approach for LIB SOH estimation using isobaric energy analysis and improved long short-term memory neural network (LSTM NN). Specifically, at first, the isobaric energy curve is plotted by analyzing the battery energy variation during the constant current charging stage. Then, the mean peak value of the isobaric energy curve is extracted as a health factor to characterize the battery SOH aging. Eventually, the LIB SOH estimation model is developed using the improved LSTM NN. In this regard, the improved LSTM NN refers to the selection of the number of hidden layers and the learning rate of the LSTM NN using the particle swarm algorithm (PSO). To verify the precision of the proposed method, validation experiments are performed based on four battery aging data with different charging multipliers. The experimental results indicate that the proposed method can effectively estimate the LIB SOH. Meanwhile, the proposed method is compared with other conventional machine learning algorithms, which demonstrates that the proposed method has better estimation performance.

The dilemma and the way out of the construction of the law profession in the context of new liberal arts based on an intelligent legal learning system

Abstract Due to society’s practical demands, the legal profession’s growth within the context of the new liberal arts is necessary to improve teaching mechanisms and the comprehensive development of interdisciplinarity. In this paper, from the two major dilemmas of the specificity of legal instruments and the correlation between sentencing tasks, to examine the relationship between sentencing prediction, charge prediction, and law suggestion of legal instruments, respectively, the Bi-LSTM-attention model, end-to-end memory network model, and CNN-GRU network model are employed, to build an intelligent legal learning system. The outcomes demonstrate that, compared to the conventional machine learning algorithm, the intelligent legal learning system based on deep learning can increase prediction performance by 5.2% to 6%, global accuracy can reach 93.3%, and accuracy of legal documents processing by 7.9%. The deep learning-based intelligent legal learning system suggested in this study can assist law students in completing legal paperwork duties and increase their learning effectiveness.

DeepGene Transformer: Transformer for the gene expression-based classification of cancer subtypes

Cancer and its subtypes constitute approximately 30% of all causes of death globally and display a wide range of heterogeneity in terms of clinical and molecular responses to therapy. Molecular subtyping has enabled the use of precision medicine to overcome these challenges and provide significant biological insights to predict prognosis and improve clinical decision-making. Over the past decade, conventional machine learning (ML) and deep learning (DL) algorithms have been widely espoused for the classification of cancer subtypes from gene expression datasets. However, these methods are potentially biased toward the identification of cancer biomarkers. Hence, an end-to-end deep learning approach, DeepGene Transformer, is proposed which addresses the complexity of high-dimensional gene expression with a multi-head self-attention module by identifying relevant biomarkers across multiple cancer subtypes without requiring feature selection as a pre-requisite for the current classification algorithms. Comparative analysis reveals that the proposed DeepGene Transformer outperformed the commonly used traditional and state-of-the-art classification algorithms and can be considered an efficient approach for classifying cancer and its subtypes, indicating that any improvement in deep learning models in computational biologists can be reflected well in this domain as well.