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It is well-known that the Tseng algorithm and its modifications have been successfully employed in approximating zeros of the sum of monotone operators. In this study, we restored various thoracic diseases’ computerized tomography (CT) images, which were degraded with a known blur function and additive noise, using a modified Tseng algorithm. The test images used in the study depict calcification of the Aorta, Subcutaneous Emphysema, Tortuous Aorta, Pneumomediastinum, and Pneumoperitoneum. Additionally, we employed well-known image restoration tools to enhance image quality and compared the quality of restored images with the originals. Finally, the study demonstrates the potential to advance monotone inclusion problem-solving, particularly in the field of medical image recovery.

In recent decades, rapid development has exacerbated climate challenges, posing serious threats to ecological sustainability. To address these issues, renewable energy, ICT technologies, financial development, and globalization have emerged as essential tools for mitigating ecological impacts and fostering green economic growth. These measures align closely with the goals of COP 28, the 2030 Sustainable Development Goals (SDGs), and the commitment to achieve carbon neutrality by 2050. However, the United States faces considerable challenges in reconciling socio-economic development with environmental sustainability. This study, therefore, investigates the key drivers of CO 2 emissions (CO 2 ) in the United States using data from 1990Q1 to 2021Q4. The study employs wavelet quantile-on-quantile regression along with quantile cointegration to analyze these dynamics across different quantiles and timeframes. The results from the study showed that across all quantiles and periods, ICT adoption technologies and fiscal decentralization increase CO 2 , while globalization, renewable energy consumption and financial development lessen CO 2 .

Several studies have demonstrated the value of artificial intelligence (AI) applications in breast cancer diagnosis. The systematic review of AI applications in breast cancer diagnosis includes several studies that compare breast cancer diagnosis and AI. However, they lack systematization, and each study appears to be conducted uniquely. The purpose and contributions of this study are to offer elaborative knowledge on the applications of AI in the diagnosis of breast cancer through citation analysis in order to categorize the main area of specialization that attracts the attention of the academic community, as well as thematic issue analysis to identify the species being researched in each category. In this study, a total number of 17,900 studies addressing breast cancer and AI published between 2012 and 2022 were obtained from these databases: IEEE, Embase: Excerpta Medica Database Guide-Ovid, PubMed, Springer, Web of Science, and Google Scholar. We applied inclusion and exclusion criteria to the search; 36 studies were identified. The vast majority of AI applications used classification models for the prediction of breast cancer. Howbeit, accuracy (99%) has the highest number of performance metrics, followed by specificity (98%) and area under the curve (0.95). Additionally, the Convolutional Neural Network (CNN) was the best model of choice in several studies. This study shows that the quantity and caliber of studies that use AI applications in breast cancer diagnosis will continue to rise annually. As a result, AI-based applications are viewed as a supplement to doctors’ clinical reasoning, with the ultimate goal of providing quality healthcare that is both affordable and accessible to everyone worldwide.

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of −0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.

Malaria continues to be a major barrier to socioeconomic development in Africa, where its death rate is over 90%. The predictive power of many machine learning models—such as multi-linear regression (MLR), artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFISs) and Random Forest classifier—is investigated in this study using data from 2207 patients. The dataset was reduced from the initial dataset of thirty-two criteria samples to fifteen. Assessment measures such as the root mean square error (RMSE), mean square error (MSE), coefficient of determination (R2), and adjusted correlation coefficient R were used. ANFIS, Random Forest, MLR, and ANN are among the models. After training, ANN outperforms ANFIS (97%), MLR (92%), and Random Forest (68%) with the greatest R (99%) and R2 (99%), respectively. The testing stage confirms the superiority of ANN. The paper also presents a statistical forecasting sheet with few errors and excellent accuracy for MLR models. When the models are assessed with Random Forest, the latter shows the least results, thus broadening the modeling techniques and offering significant insights into the prediction of malaria and healthcare decision making. The outcomes of using machine learning models for precise and efficient illness prediction add to an expanding body of knowledge, assisting healthcare systems in making better decisions and allocating resources more effectively.

The accumulation of pathologically misfolded tau is a feature shared by a group of neurodegenerative disorders collectively referred to as tauopathies. Alzheimer’s disease (AD) is the most prevalent of these tauopathies. Immunohistochemical evaluation allows neuropathologists to visualize paired-helical filaments (PHFs)—tau pathological lesions, but this is possible only after death and only shows tau in the portion of brain sampled. Positron emission tomography (PET) imaging allows both the quantitative and qualitative analysis of pathology over the whole brain of a living subject. The ability to detect and quantify tau pathology in vivo using PET can aid in the early diagnosis of AD, provide a way to monitor disease progression, and determine the effectiveness of therapeutic interventions aimed at reducing tau pathology. Several tau-specific PET radiotracers are now available for research purposes, and one is approved for clinical use. This study aims to analyze, compare, and rank currently available tau PET radiotracers using the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE), which is a multi-criteria decision-making (MCDM) tool. The evaluation is based on relatively weighted criteria, such as specificity, target binding affinity, brain uptake, brain penetration, and rates of adverse reactions. Based on the selected criteria and assigned weights, this study shows that a second-generation tau tracer, [18F]RO-948, may be the most favorable. This flexible method can be extended and updated to include new tracers, additional criteria, and modified weights to help researchers and clinicians select the optimal tau PET tracer for specific purposes. Additional work is needed to confirm these results, including a systematic approach to defining and weighting criteria and clinical validation of tracers in different diseases and patient populations.

: Monkeypox, a disease caused by a deoxyribonucleic acid (DNA) based virus (MPXV) has posed global health challenges to the entire populace. MPXV is a zoonotic disease with public health concerns, rapid prevalence, and geographical spread resulting in swift preventive techniques, especially for vulnerable nations (population). Its incidence and global widespread have necessitated immediate intervention thus the use of vaccination. This study analyzed three globally recommended monkeypox vaccines, LC16m8, ACAM2000, and JYNNEOS, by assessing their safety and effectiveness in controlling monkeypox. : Multi-criteria decision-making (MCDM) methods; the fuzzy Preference Ranking Organization Method for Enrichment Evaluations (fuzzy PROMETHEE) and the fuzzy Technique for Order Preference by Similarities to Ideal Solution (fuzzy TOPSIS), were applied for the evaluation of these vaccines considering 20 different criteria, mainly focusing on the route of administration, dosage, safety, adverse effects, affordability, and overall effectiveness of the vaccine. : LC16m8 ranked the most preferable vaccine from both MCDM methods with a net outranking flow of 0.4365 and Closeness coefficient value of 0.7916 (95% CI, 0.242-0.894). In terms of safety, both LC16m8 and JYNNEOS vaccines showed equal performance in their profiles mostly in vulnerable populations like human immunodeficiency virus-positive populations, pregnant women, and children, as well as cardiovascular disease patients. : The MCDM models could be flexibly applied to other areas of public health as it has shown their reliability in assessing the monkeypox vaccines and can provide a decision guide for different health policy agencies.

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.

Prostate cancer is a leading cause of cancer-related morbidity and mortality worldwide, with diagnostic challenges magnified in underrepresented regions like sub-Saharan Africa. This study introduces a novel application of ConvNeXt, an advanced convolutional neural network architecture, for multi-class classification of prostate histopathological images into normal, benign, and malignant categories. The dataset, sourced from a tertiary healthcare institution in Nigeria, represents a typically underserved African population, addressing critical disparities in global diagnostic research. We also used the ProstateX dataset (2017) from The Cancer Imaging Archive (TCIA) to validate our result. A comprehensive pipeline was developed, leveraging advanced data augmentation, Grad-CAM for interpretability, and an ablation study to enhance model optimization and robustness. The ConvNeXt model achieved an accuracy of 98%, surpassing the performance of traditional CNNs (ResNet50, 93%; EfficientNet, 94%; DenseNet, 92%) and transformer-based models (ViT, 88%; CaiT, 86%; Swin Transformer, 95%; RegNet, 94%). Also, using the ProstateX dataset, the ConvNeXt model recorded 87.2%, 85.7%, 86.4%, and 0.92 as accuracy, recall, F1 score, and AUC, respectively, as validation results. Its hybrid architecture combines the strengths of CNNs and transformers, enabling superior feature extraction. Grad-CAM visualizations further enhance explainability, bridging the gap between computational predictions and clinical trust. Ablation studies demonstrated the contributions of data augmentation, optimizer selection, and learning rate tuning to model performance, highlighting its robustness and adaptability for deployment in low-resource settings. This study advances equitable health care by addressing the lack of regional representation in diagnostic datasets and employing a clinically aligned three-class classification approach. Combining high performance, interpretability, and scalability, this work establishes a foundation for future research on diverse and underrepresented populations, fostering global inclusivity in cancer diagnostics.