BMC Medical Informatics and Decision Making
volume 24, Article number: 83 (2024)

Emergency Department (ED) overcrowding is a global concern, with tools like NEDOCS, READI, and Work Score used as predictors. These tools aid healthcare professionals in identifying overcrowding and preventing negative patient outcomes. However, there’s no agreed-upon method to define ED overcrowding. Most studies on this topic are U.S.-based, limiting their applicability in EDs without waiting rooms or ambulance diversion roles. Additionally, the intricate calculations required for these scores, with multiple variables, make them impractical for use in developing nations.

Objective

This study sought to examine the relationship between prevalent ED overcrowding scores such as EDWIN, occupancy rate, and Work Score, and a modified version of EDWIN newly introduced by the authors, in comparison to the real-time perspectives of emergency physicians. Additionally, the study explored the links between these overcrowding scores and adverse events related to ED code activations as secondary outcomes.

https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-024-02456-9

The experimental results show that the proposed model outperformed the baseline methods, with F-scores of 0.81 for DenseNet + Logistic Regression hybrid model, (F-score: 0.73) for Visual Geometry Group (VGG) + Logistic Regression hybrid model, (F-score: 0.74) for VGG + Random Forest, (F-score: 0.79) for DenseNet + Random Forest, and (F-score: 0.79) for VGG + Densenet + Logistic Regression hybrid model on the dataset of histopathological images. 

https://www.techscience.com/cmc/v74n2/50245

The electroencephalogram (EEG) introduced a massive potential for user identification. Several studies have shown that EEG provides unique features in addition to typical strength for spoofing attacks. EEG provides a graphic recording of the brain’s electrical activity that electrodes can capture on the scalp at different places. However, selecting which electrodes should be used is a challenging task. 

Such a subject is formulated as an electrode selection task that is tackled by optimization methods. In this work, a new approach to select the most representative electrodes is introduced. The proposed algorithm is a hybrid version of the Flower Pollination Algorithm and β-Hill Climbing optimizer called FPAβ-hc. The performance of the FPAβ-hc algorithm is evaluated using a standard EEG motor imagery dataset. The experimental results show that the FPAβ-hc can utilize less than half of the electrode numbers, achieving more accurate results than seven other methods https://www.mdpi.com/1424-8220/22/6/2092

 Signet Ring Cell (SRC) Carcinoma is among the dangerous types of cancers, and has a major contribution towards the death ratio caused by cancerous diseases. Detection and diagnosis of SRC carcinoma at earlier stages is a challenging, laborious, and costly task. Automatic detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of research. 

In the presented framework, we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning (DL) technique named Mask Region-based Convolutional Neural Network (Mask-RCNN). In the first step, the input image is fed to Resnet-101 for feature extraction. The extracted feature maps are conveyed to Region Proposal Network (RPN) for the generation of the region of interest (RoI) proposals as well as they are directly conveyed to RoiAlign. Secondly, RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected (FC) network and performs classification along with Bounding Box (bb) generation by using FC layers. The annotations are developed from ground truth (GT) images to perform experimentation on our developed dataset. Our introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical trials. We aim to release the employed database soon to assist the improvement in the SRC recognition research area 

In this study, linguistic variables in terms of triangular fuzzy numbers (TrFn) are used to manage such kind of rough information, then the rough approximations of the fuzzy hypersoft set (FHS-set) are characterized which are capable of handling such informational uncertainties. The FHS-set is more flexible as well as consistent as it tackles the limitation of fuzzy soft sets regarding categorizing parameters into their related sub-classes having their sub-parametric values. Based on these rough approximations, an algorithm is proposed for the optimal selection of suppliers by managing experts’ opinions and rough information collectively in the form of TrFn-based linguistic variables. To have a discrete decision, a signed distance method is employed to transform the TrFn-based opinions of experts into fuzzy grades. 
The proposed algorithm is corroborated with the help of a multi-criteria decision-making application to choose the best supplier for real estate builders. The beneficial facets of the put forward study are appraised through its structural comparison with few existing related approaches. The presented approach is consistent as it is capable to manage rough information and expert’s opinions about suppliers collectively by using rough approximations of FHS-set.
https://www.mdpi.com/2075-5309/12/7/940

The study proposes ReSVM, an approach combining deep features of ResNet-50 with the SVM classifier, for distraction detection of a driver. ReSVM is compared with six state-of-the-art approaches on four datasets, namely: State Farm Distracted Driver Detection, Boston University, DrivFace, and FT-UMT. Experiments demonstrate that ReSVM outperforms the existing approaches and achieves a classification accuracy as high as 95.5%. The study also compares ReSVM with its variants on the aforementioned datasets.

This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors’ knowledge. 

The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. 

This research discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.

The proposed work presents a robotized predictive structure, dependent on machine learning (ML) methods for the forecast of AD. Neuropsychological measures (NM) and magnetic resonance imaging (MRI) biomarkers are deduced and passed on to a recurrent neural network (RNN). In the RNN, we have used long short-term memory (LSTM), and the proposed model will predict the biomarkers (feature vectors) of patients after 6, 12, 21 18, 24, and 36 months. These predicted biomarkers will go through fully connected neural network layers. The NN layers will then predict whether these RNN-predicted biomarkers belong to an AD patient or a patient with a mild cognitive impairment (MCI). The developed methodology has been tried on an openly available informational dataset (ADNI) and accomplished an accuracy of 88.24%, which is superior to the next-best available algorithms.

In this research, we proposed a deep learning and Moth-Crow optimization-based method for GI disease classification. There are a few key steps in the proposed framework. Initially, the contrast of the original images is increased, and three operations based on data augmentations are performed. Then, using transfer learning, two pre-trained deep learning models are fine-tuned and trained on GI disease images. 

Features are extracted from the middle layers using both fine-tuned deep learning models (average pooling). On both extracted deep feature vectors, a hybrid Crow-Moth optimization algorithm is proposed and applied. The resultant selected feature vectors are later fused using the distance-canonical correlation (D-CCA) approach. For classifying GI diseases, the final fused vector features are classified using machine learning algorithms. The experiments are carried out on three publicly available datasets titled CUI Wah WCE imaging, Kvasir-v1, and Kvasir-v2, providing improved accuracy with less computational time compared with recent techniques

In computer vision, object recognition and image categorization have proven to be difficult challenges. They have, nevertheless, generated responses to a wide range of difficult issues from a variety of fields. Convolution Neural Networks (CNNs) have recently been identified as the most widely proposed deep learning (DL) algorithms in the literature. CNNs have unquestionably delivered cutting-edge achievements, particularly in the areas of image classification, speech recognition, and video processing. However, it has been noticed that the CNN-training assignment demands a large amount of data, which is in low supply, especially in the medical industry, and as a result, the training process takes longer. In this paper, we describe an attention-aware CNN architecture for classifying chest X-ray images to diagnose Pneumonia in order to address the aforementioned difficulties. Attention Modules provide attention-aware properties to the Attention Network. The attention-aware features of various modules alter as the layers become deeper. Using a bottom-up top-down feedforward structure, the feedforward and feedback attention processes are integrated into a single feedforward process inside each attention module.

In the present work, a deep neural network (DNN) is combined with an attention mechanism to test the prediction of Pneumonia disease using chest X-ray pictures. To produce attention-aware features, the suggested network was built by merging channel and spatial attention modules in DNN architecture. With this network, we worked on a publicly available Kaggle chest X-ray dataset. Extensive testing was carried out to validate the suggested model. In the experimental results, we attained an accuracy of 95.47% and an F- score of 0.92, indicating that the suggested model outperformed against the baseline models.

Manual diagnosis of crops diseases is not an easy process; thus, a computerized method is widely used. From a couple of years, advancements in the domain of machine learning, such as deep learning, have shown substantial success. However, they still faced some challenges such as similarity in disease symptoms and irrelevant features extraction. In this article, we proposed a new deep learning architecture with optimization algorithm for cucumber and potato leaf diseases recognition. The proposed architecture consists of five steps. In the first step, data augmentation is performed to increase the numbers of training samples. In the second step, pre-trained DarkNet19 deep model is opted and fine-tuned that later utilized for the training of fine-tuned model through transfer learning. Deep features are extracted from the global pooling layer in the next step that is refined using Improved Cuckoo search algorithm. The best selected features are finally classified using machine learning classifiers such as SVM, and named a few more for final classification results. The proposed architecture is tested using publicly available datasets–Cucumber National Dataset and Plant Village. The proposed architecture achieved an accuracy of 100.0%, 92.9%, and 99.2%, respectively. A comparison with recent techniques is also performed, revealing that the proposed method achieved improved accuracy while consuming less computational time.

https://www.techscience.com/cmc/v74n1/49776

 The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. 

The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier 

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. 

Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. 

The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest technique . . https://www.mdpi.com/2075-4418/11/10/1856# 

Different epidemics, specially Coronavirus, have caused critical misfortunes in various fields like monetary deprivation, survival conditions, thus diminishing the overall individual fulfillment. Various worldwide associations and different hierarchies of government fraternity are endeavoring to offer the necessary assistance in eliminating the infection impacts but unfortunately standing up to the non-appearance of resources and expertise. In contrast to all other pandemics, Coronavirus has proven to exhibit numerous requirements such that curated appropriation and determination of innovations are required to deal with the vigorous undertakings, which include precaution, detection, and medication. Innovative advancements are essential for the subsequent pandemics where-in the forthcoming difficulties can indeed be approached to such a degree that it facilitates constructive solutions more comprehensively. In this study, futuristic and emerging innovations are analyzed, improving COVID-19 effects for the general public. Large data sets need to be advanced so that extensive models related to deep analysis can be used to combat Coronavirus infection, which can be done by applying Artificial intelligence techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Computer vision to varying processing files. This article aims to furnish variation sets of innovations that can be utilized to eliminate COVID-19 and serve as a resource for the coming generations. At last, elaboration associated with future state-of-the-art technologies and the attainable sectors of AI methodologies has been mentioned concerning the post-COVID-19 world to enable the different ideas for dealing with the pandemic-based difficulties. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9471880

we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation (ACO) based selection. The proposed method consists of four fundamental steps: data augmentation to solve the imbalanced dataset, fine-tuned pre-trained deep learning models (NasNet Mobile and MobileNet-V2), the fusion of extracted deep features using matrix length, and finally, a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis (NCA). The best-selected features were eventually passed to many classifiers for final recognition. The experimental process involved an augmented dataset and achieved an average accuracy of 99.7%. Comparison with existing techniques showed that the proposed method was effective.