Improved deep convolutional neural networks using chimp optimization algorithm for Covid19 diagnosis from the X-ray images

User preference interaction fusion and swap attention graph neural network for recommender system

Recommender systems are widely used in various applications. Knowledge graphs are increasingly used to improve recommendation performance by extracting valuable information from user-item interactions. However, current methods do not effectively use fine-grained information within the knowledge graph. Additionally, some recommendation methods based on graph neural networks tend to overlook the importance of entities to users when performing aggregation operations. To alleviate these issues, we introduce a knowledge-graph-based graph neural network (PIFSA-GNN) for recommendation with two key components. The first component, user preference interaction fusion, incorporates user auxiliary information in the recommendation process. This enhances the influence of users on the recommendation model. The second component is an attention mechanism called user preference swap attention, which improves entity weight calculation for effectively aggregating neighboring entities. Our method was extensively tested on three real-world datasets. On the movie dataset, our method outperforms the best baseline by 1.3% in AUC and 2.8% in F1; Hit@1 increases by 0.7%, Hit@5 by 0.6%, and Hit@10 by 1.0%. On the restaurant dataset, AUC improves by 2.6% and F1 by 7.2%; Hit@1 increases by 1.3%, Hit@5 by 3.7%, and Hit@10 by 2.9%. On the music dataset, AUC improves by 0.9% and F1 by 0.4%; Hit@1 increases by 3.3%, Hit@5 by 1.2%, and Hit@10 by 0.2%. The results show that it outperforms baseline methods.

Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition

Artificial intelligence technology has demonstrated remarkable diagnostic efficacy in modern biomedical image analysis. However, the practical application of artificial intelligence is significantly limited by the presence of similar pathologies among different diseases and the diversity of pathologies within the same disease. To address this issue, this paper proposes a reinforced collaborative-competitive representation classification (RCCRC) method. RCCRC enhances the contribution of different classes by introducing dual competitive constraints into the objective function. The first constraint integrates the collaborative space representation akin to holistic data, promoting the representation contribution of similar classes. The second constraint introduces specific class subspace representations to encourage competition among all classes, enhancing the discriminative nature of representation vectors. By unifying these two constraints, RCCRC effectively explores both global and specific data features in the reconstruction space. Extensive experiments on various biomedical image databases are conducted to exhibit the advantage of the proposed method in comparison with several state-of-the-art classification algorithms.

Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification

Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming. Swarm intelligence algorithms have been widely adopted to solve many highly nonlinear, multimodal problems and have succeeded significantly. The Hunger Games Search (HGS) is a recent swarm intelligence algorithm that has shown good performance across various applications. However, the standard HGS still faces limitations, such as restricted population diversity and a tendency to get trapped in local optima, which can hinder its effectiveness. In this paper, we propose an optimized deep learning architecture called EHGS-VGG16 designed based on the VGG16 model and boosted by an enhanced Hunger Games Search (EHGS) algorithm for hyperparameter tuning. The proposed enhancement to HGS involves modified search strategies, incorporating the concepts of "local best" and a "local escaping mechanism" to improve its exploration capability. To validate our approach, the evaluation is conducted in three folds. First, the EHGS algorithm is evaluated through 30 real-valued benchmark functions from the IEEE CEC2014 suite. Second, a custom-developed VGG16 model is tested on the Flickr-27 logo classification dataset and compared against state-of-the-art deep learning models such as ResNet50V2, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2. Finally, EHGS is integrated into the VGG16 model to optimize its hyperparameters. The experimental results show that VGG16 outperformed the other counterparts with an accuracy of 0.956966, a precision of 0.957137, and a recall of 0.956966. Moreover, the integration of EHGS further improved classification quality by 3%. These findings highlight the potential of combining evolutionary optimization techniques with deep learning for enhanced accuracy in log classification tasks.

Research on intelligent decision support systems for oil and gas exploration based on machine learning

The process of extracting oil and gas via borehole drilling is largely dependent on subsurface structures, and thus, well log analysis is a major concern for economic feasibility. Well logs are essential for understanding the geology below the earth's surface, which allows for the estimation of the available hydrocarbon resources. The incompleteness of these logs, on the other hand, is a major hindrance to downstream analysis success. This study, however, addresses the above challenges and presents a deep Long-Short Term Memory (LSTM) model specialized using a new hyperparameter tuning algorithm. There is an evidence gap that we try to fill: well log prediction using LSTM has not been extensively documented, particularly on reconstruction of missing data. In order to remedy this, we develop a new algorithm entitled Elite Preservation Strategy Chimp Optimization Algorithm (EPSCHOA), which will improve the tuning of LSTM hyperparameters. EPSCHOA enhances prediction performance by preserving the diversity of the strongest candidates and transforming the most effective predictor resources into less effective ones. A comparative analysis of the LSTM-EPSCHOA model was carried out with both LSTM and E-LSTM models, including their various extensions, LSTM-CHOA, LSTM-HGSA, LSTM-IMPA, LSTM-SEB-CHOA, and LSTM-GOLCHOA, even as common forecasting models using Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient Boosting (GB), and AutoRegressive Integrated Moving Average (ARIMA). The results of the performance tests demonstrate that the LSTM-EPSCHOA model outperforms in all aspects, as evidenced by its R2 values of.98, RMSE of 0.022, and MAPE of 0.701% during training, and R2 values of 0.96, RMSE of 0.025, and MAPE of 0.698% during testing. These are considerably superior to other measures used compared to what was achieved using explicit modeling using LSTM, which stood at R2 of 0.59, RMSE of 0.101, and MAPE of 2.588%. The LSTM-EPSCHOA proved to give models faster rates of convergence and lower error measurements than usual models, which clearly demonstrated its efficiency in solving the problem of inadequate well-log data. The new approach is regarded as having many useful potentials to boost well-log interpretations in the oil sector.

Evaluation of modified fire hawk optimizer for new modification in double diode solar cell model

The evaluation of photovoltaic (PV) model parameters has gained importance considering emerging new energy power systems. Because weather patterns are unpredictable, variations in PV output power are nonlinear and periodic. It is impractical to rely on a time series because traditional power forecast techniques are based on linearity. As a result, meta-heuristic algorithms have drawn significant attention for their exceptional performance in extracting characteristics from solar cell models. This study analyzes a new modification in the double-diode solar cell model (NMDDSCM) to evaluate its performance compared with the traditional double-diode solar cell model (TDDSCM). Modified Fire Hawk Optimizer (mFHO) is applied to identify the photovoltaic parameters (PV) of the TDDSCM and NMDDSCM models. The Modified Fire Hawks Optimizer (mFHO) algorithm, which incorporates two enhancement strategies to address the shortcomings of FHO. The experimental performance is evaluated by investigating the scores achieved by the method on the CEC-2022 standard test suite. The parameter extraction of the TDDSCM and NMDDSCM is an optimization problem treated with an objective function to minimize the root mean square error (RMSE) between the calculated and the measured data. Real data of the R.T.C France solar cell is used to verify the performance of NMDDSCM. The effectiveness of the mFHO algorithm is compared with other algorithms such as Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Fire Hawk Optimizer (FHO), Moth Flame Optimization (MFO), Heap Based optimization (HBO), and Chimp Optimization Algorithm (ChOA). The best objective function for the TDDSCM equal to 0.000983634 and its value for NMDDSCM equal to 0.000982485 that is achieved by the mFHO algorithm. The obtained results have proved the NMDDSCM's superiority over TDDSCM for all competitor techniques.

Predicting ergonomic risk among laboratory technicians using a Cheetah Optimizer-Integrated Deep Convolutional Neural Network

Medical laboratory technicians play a significant role in clinical units by conducting diagnostic tests and analyses. However, their job nature involving repetitive motions, prolonged standing or sitting, etc., leads to potential ergonomic risks. This research proposed a novel hybrid strategy by integrating the Cheetah Optimizer into the Deep Convolutional Neural Network (CHObDCNN) for predicting ergonomic risks in medical laboratory technicians. The presented framework commences with collecting images containing different postures and motions of laboratory technicians working in clinical units. The collected database was pre-processed to eliminate noises and other unwanted features. The DCNN component in the proposed framework performs the ergonomic risk prediction task by examining the patterns and interconnection with the image data, while the CHO component optimizes the DCNN training by tuning its parameters to its optimal range. Thus, the combined methodology offers improved classification results by iteratively updating its parameters. The presented framework was implemented in MATLAB, and the experimental outcomes manifest that the proposed method acquired improved accuracy of 98.74 %, greater precision of 98.56 %, and reduced computational time of 2.45 ms. Finally, the comparative study with the existing techniques validates its effectiveness in ergonomic risk prediction.

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit

Given the limited capacity to accurately determine the necessity for intubation in intensive care unit settings, this study aimed to develop and externally validate an interpretable machine learning model capable of predicting the need for intubation among ICU patients. Seven widely used machine learning (ML) algorithms were employed to construct the prediction models. Adult patients from the Medical Information Mart for Intensive Care IV database who stayed in the ICU for longer than 24 h were included in the development and internal validation. The model was subsequently externally validated using the eICU-CRD database. In addition, the SHapley Additive exPlanations method was employed to interpret the influence of individual parameters on the predictions made by the model. A total of 11,988 patients were included in the final cohort for this study. The CatBoost model demonstrated the best performance (AUC: 0.881). In the external validation set, the efficacy of our model was also confirmed (AUC: 0.750), which suggests robust generalization capabilities. The Glasgow Coma Scale (GCS), body mass index (BMI), arterial partial pressure of oxygen (PaO2), respiratory rate (RR) and length of stay (LOS) before ICU were the top 5 features of the CatBoost model with the greatest impact. We developed an externally validated CatBoost model that accurately predicts the need for intubation in ICU patients within 24 to 96 h of admission, facilitating clinical decision-making and has the potential to improve patient outcomes. The prediction model utilizes readily obtainable monitoring parameters and integrates the SHAP method to enhance interpretability, providing clinicians with clear insights into the factors influencing predictions.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

An intelligent predictive and optimized wastewater treatment plant

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.

Paddy insect identification using deep features with lion optimization algorithm

Pests are a significant challenge in paddy cultivation, resulting in a global loss of approximately 20 % of rice yield. Early detection of paddy insects can help to save these potential losses. Several ways have been suggested for identifying and categorizing insects in paddy fields, employing a range of advanced, noninvasive, and portable technologies. However, none of these systems have successfully incorporated feature optimization techniques with Deep Learning and Machine Learning. Hence, the current research provided a framework utilizing these techniques to detect and categorize images of paddy insects promptly. Initially, the suggested research will gather the image dataset and categorize it into two groups: one without paddy insects and the other with paddy insects. Furthermore, various pre-processing techniques, such as augmentation and image filtering, will be applied to enhance the quality of the dataset and eliminate any unwanted noise. To determine and analyze the deep characteristics of an image, the suggested architecture will incorporate 5 pre-trained Convolutional Neural Network models. Following that, feature selection techniques, including Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and an optimization algorithm called Lion Optimization, were utilized in order to further reduce the redundant number of features that were collected for the study. Subsequently, the process of identifying the paddy insects will be carried out by employing 7 ML algorithms. Finally, a set of experimental data analysis has been conducted to achieve the objectives, and the proposed approach demonstrates that the extracted feature vectors of ResNet50 with Logistic Regression and PCA have achieved the highest accuracy, precisely 99.28 %. However, the present idea will significantly impact how paddy insects are diagnosed in the field.

Improved sports image classification using deep neural network and novel tuna swarm optimization

Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the deep neural network (DNN) with a modified metaheuristic algorithm known as novel tuna swarm optimization (NTSO) for the purpose of sports image classification. The DNN is a potent technique capable of extracting high-level features from raw images, while the NTSO algorithm optimizes the hyperparameters of the DNN, including the number of layers, neurons, and activation functions. Through the application of NTSO to the DNN, a finely-tuned network is developed, exhibiting exceptional performance in sports image classification. Rigorous experiments have been conducted on an extensive dataset of sports images, and the obtained results have been compared against other state-of-the-art methods, including Attention-based graph convolution-guided third-order hourglass network (AGTH-Net), particle swarm optimization algorithm (PSO), YOLOv5 backbone and SPD-Conv, and Depth Learning (DL). According to a fivefold cross-validation technique, the DNN/NTSO model provided remarkable precision, recall, and F1-score results: 97.665 ± 0.352%, 95.400 ± 0.374%, and 0.8787 ± 0.0031, respectively. Detailed comparisons reveal the DNN/NTSO model's superiority toward various performance metrics, solidifying its standing as a top choice for sports image classification tasks. Based on the practical dataset, the DNN/NTSO model has been successfully evaluated in real-world scenarios, showcasing its resilience and flexibility in various sports categories. Its capacity to uphold precision in dynamic settings, where elements like lighting, backdrop, and motion blur are prominent, highlights its utility. The model's scalability and efficiency in analyzing images from live sports competitions additionally validate its suitability for integration into real-time sports analytics and media platforms. This research not only confirms the theoretical superiority of the DNN/NTSO model but also its pragmatic effectiveness in a wide array of demanding sports image classification assignments.

Enhancing Earth data analysis in 5G satellite networks: A novel lightweight approach integrating improved deep learning

Efficiently handling huge data amounts and enabling processing-intensive applications to run in faraway areas simultaneously is the ultimate objective of 5G networks. Currently, in order to distribute computing tasks, ongoing studies are exploring the incorporation of fog-cloud servers onto satellites, presenting a promising solution to enhance connectivity in remote areas. Nevertheless, analyzing the copious amounts of data produced by scattered sensors remains a challenging endeavor. The conventional strategy of transmitting this data to a central server for analysis can be costly. In contrast to centralized learning methods, distributed machine learning (ML) provides an alternative approach, albeit with notable drawbacks. This paper addresses the comparative learning expenses of centralized and distributed learning systems to tackle these challenges directly. It proposes the creation of an integrated system that harmoniously merges cloud servers with satellite network structures, leveraging the strengths of each system. This integration could represent a major breakthrough in satellite-based networking technology by streamlining data processing from remote nodes and cutting down on expenses. The core of this approach lies in the adaptive tailoring of learning techniques for individual entities based on their specific contextual nuances. The experimental findings underscore the prowess of the innovative lightweight strategy, LMAED2L (Enhanced Deep Learning for Earth Data Analysis), across a spectrum of machine learning assignments, showcasing remarkable and consistent performance under diverse operational conditions. Through a strategic fusion of centralized and distributed learning frameworks, the LMAED2L method emerges as a dynamic and effective remedy for the intricate data analysis challenges encountered within satellite networks interfaced with cloud servers. The empirical findings reveal a significant performance boost of our novel approach over traditional methods, with an average increase in reward (4.1 %), task completion rate (3.9 %), and delivered packets (3.4 %). This report suggests that these advancements will catalyze the integration of cutting-edge machine learning algorithms within future networks, elevating responsiveness, efficiency, and resource utilization to new heights.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Machine learning in liver surgery: Benefits and pitfalls

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics

BACKGROUND

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis.

OBJECTIVE

DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing.

METHOD

One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels.

RESULTS

The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification.

CONCLUSION

The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant.

Predicting survival of Iranian COVID-19 patients infected by various variants including omicron from CT Scan images and clinical data using deep neural networks

Purpose: The rapid spread of the COVID-19 omicron variant virus has resulted in an overload of hospitals around the globe. As a result, many patients are deprived of hospital facilities, increasing mortality rates. Therefore, mortality rates can be reduced by efficiently assigning facilities to higher-risk patients. Therefore, it is crucial to estimate patients' survival probability based on their conditions at the time of admission so that the minimum required facilities can be provided, allowing more opportunities to be available for those who need them. Although radiologic findings in chest computerized tomography scans show various patterns, considering the individual risk factors and other underlying diseases, it is difficult to predict patient prognosis through routine clinical or statistical analysis. Method: In this study, a deep neural network model is proposed for predicting survival based on simple clinical features, blood tests, axial computerized tomography scan images of lungs, and the patients' planned treatment. The model's architecture combines a Convolutional Neural Network and a Long Short Term Memory network. The model was trained using 390 survivors and 108 deceased patients from the Rasoul Akram Hospital and evaluated 109 surviving and 36 deceased patients infected by the omicron variant. Results: The proposed model reached an accuracy of 87.5% on the test data, indicating survival prediction possibility. The accuracy was significantly higher than the accuracy achieved by classical machine learning methods without considering computerized tomography scan images (p-value <= 4E-5). The images were also replaced with hand-crafted features related to the ratio of infected lung lobes used in classical machine-learning models. The highest-performing model reached an accuracy of 84.5%, which was considerably higher than the models trained on mere clinical information (p-value <= 0.006). However, the performance was still significantly less than the deep model (p-value <= 0.016). Conclusion: The proposed deep model achieved a higher accuracy than classical machine learning methods trained on features other than computerized tomography scan images. This proves the images contain extra information. Meanwhile, Artificial Intelligence methods with multimodal inputs can be more reliable and accurate than computerized tomography severity scores.

Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images

Computed tomography (CT) scans, or radiographic images, were used to aid in the early diagnosis of patients and detect normal and abnormal lung function in the human chest. However, the diagnosis of lungs infected with coronavirus disease 2019 (COVID-19) was made more accurately from CT scan data than from a swab test. This study uses human chest radiography pictures to identify and categorize normal lungs, lung opacities, COVID-19-infected lungs, and viral pneumonia (often called pneumonia). In the past, several CAD systems using image processing, ML/DL, and other forms of machine learning have been developed. However, those CAD systems did not provide a general solution, required huge hyper-parameters, and were computationally inefficient to process huge datasets. Moreover, the DL models required high computational complexity, which requires a huge memory cost, and the complexity of the experimental materials' backgrounds, which makes it difficult to train an efficient model. To address these issues, we developed the Inception module, which was improved to recognize and detect four classes of Chest X-ray in this research by substituting the original convolutions with an architecture based on modified-Xception (m-Xception). In addition, the model incorporates depth-separable convolution layers within the convolution layer, interlinked by linear residuals. The model's training utilized a two-stage transfer learning process to produce an effective model. Finally, we used the XgBoost classifier to recognize multiple classes of chest X-rays. To evaluate the m-Xception model, the 1095 dataset was converted using a data augmentation technique into 48,000 X-ray images, including 12,000 normal, 12,000 pneumonia, 12,000 COVID-19 images, and 12,000 lung opacity images. To balance these classes, we used a data augmentation technique. Using public datasets with three distinct train-test divisions (80-20%, 70-30%, and 60-40%) to evaluate our work, we attained an average of 96.5% accuracy, 96% F1 score, 96% recall, and 96% precision. A comparative analysis demonstrates that the m-Xception method outperforms comparable existing methods. The results of the experiments indicate that the proposed approach is intended to assist radiologists in better diagnosing different lung diseases.

Automatic Measuring of Finger Joint Space Width on Hand Radiograph using Deep Learning and Conventional Computer Vision Methods

Hand osteoarthritis (OA) severity can be assessed visually through radiographs using semi-quantitative grading systems. However, these grading systems are subjective and cannot distinguish minor differences. Joint space width (JSW) compensates for these disadvantages, as it quantifies the severity of OA by accurately measuring the distances between joint bones. Current methods used to assess JSW require users' interaction to identify the joints and delineate initial joint boundary, which is time-consuming. To automate this process and offer a more efficient and robust measurement for JSW, we proposed two novel methods to measure JSW: 1) The segmentation-based (SEG) method, which uses traditional computer vision techniques to calculate JSW; 2) The regression-based (REG) method, which is a deep learning approach employing a modified VGG-19 network to predict JSW. On a dataset with 3,591 hand radiographs, 10,845 DIP joints were cut as regions of interest and served as input to the SEG and REG methods. The bone masks of the ROI images generated by a U-Net model were sent as input in addition to the ROIs. The ground truth of JSW was labeled by a trained research assistant using a semi-automatic tool. Compared with the ground truth, the REG method achieved a correlation coefficient of 0.88 and mean square error (MSE) of 0.02 mm on the testing set; the SEG method achieved a correlation coefficient of 0.42 and MSE of 0.15 mm. Results show the REG method has promising performance in automatic JSW measurement and in general, Deep Learning approaches can facilitate the automatic quantification of distance features in medical images.

Multivariate time series short term forecasting using cumulative data of coronavirus

Coronavirus emerged as a highly contagious, pathogenic virus that severely affects the respiratory system of humans. The epidemic-related data is collected regularly, which machine learning algorithms can employ to comprehend and estimate valuable information. The analysis of the gathered data through time series approaches may assist in developing more accurate forecasting models and strategies to combat the disease. This paper focuses on short-term forecasting of cumulative reported incidences and mortality. Forecasting is conducted utilizing state-of-the-art mathematical and deep learning models for multivariate time series forecasting, including extended susceptible-exposed-infected-recovered (SEIR), long-short-term memory (LSTM), and vector autoregression (VAR). The SEIR model has been extended by integrating additional information such as hospitalization, mortality, vaccination, and quarantine incidences. Extensive experiments have been conducted to compare deep learning and mathematical models that enable us to estimate fatalities and incidences more precisely based on mortality in the eight most affected nations during the time of this research. The metrics like mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are employed to gauge the model's effectiveness. The deep learning model LSTM outperformed all others in terms of forecasting accuracy. Additionally, the study explores the impact of vaccination on reported epidemics and deaths worldwide. Furthermore, the detrimental effects of ambient temperature and relative humidity on pathogenic virus dissemination have been analyzed.

Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.

Enhanced chimp optimization algorithm for high level synthesis of digital filters

The HLS of digital filters is a complex optimization task in electronic design automation that increases the level of abstraction for designing and scheming digital circuits. The complexity of this issue attracting the interest of the researcher and solution of this issue is a big challenge for the researcher. The scientists are trying to present the various most powerful methods for this issue, but keep in mind these methods could be trapped in the complex space of this problem due to own weaknesses. Due to shortcomings of these methods, we are trying to design a new framework with the mixture of the phases of the powerful approaches for high level synthesis of digital filters in this work. This modification has been done by merging the chimp optimizer with sine cosine functions. The sine cosine phases helped in enhancing the exploitation phase of the chimp optimizer and also ignored the local optima in the search area during the searching of new shortest paths. The algorithms have been applied on 23-standard test suites and 14-digital filters for verifying the performance of the algorithms. Experimental results of single and multi-objective functions have been compared in terms of best score, best maxima, average, standard deviation, execution time, occupied area and speed respectively. Furthermore, by analyzing the effectiveness of the proposed algorithm with the recent algorithms for the HLS digital filters design, this can be concluded that the proposed method dominates the other two methods in HLS digital filters design. Another prominent feature of the proposed system in addition to the stated enhancement, is its rapid runtime, lowest delay, occupied area and lowest power in achieving an appropriate response. This could greatly reduce the cost of systems with broad dimensions while increasing the design speed.