A practical evaluation of machine learning for classification of ultrasound images of ovarian development in channel catfish (Ictalurus punctatus)

FCM-NPOA: A hybrid Fuzzy C-means clustering with nomadic people optimizer for ovarian cancer detection

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

Advancing genetic improvement in the omics era: status and priorities for United States aquaculture

BACKGROUND

The innovations of the "Omics Era" have ushered in significant advancements in genetic improvement of agriculturally important animal species through transforming genetics, genomics and breeding strategies. These advancements were often coordinated, in part, by support provided over 30 years through the 1993-2023 National Research Support Project 8 (NRSP8, National Animal Genome Research Program, NAGRP) and affiliate projects focused on enabling genomic discoveries in livestock, poultry, and aquaculture species. These significant and parallel advances demand strategic planning of future research priorities. This paper, as an output from the May 2023 Aquaculture Genomics, Genetics, and Breeding Workshop, provides an updated status of genomic resources for United States aquaculture species, highlighting major achievements and emerging priorities.

MAIN TEXT

Finfish and shellfish genome and omics resources enhance our understanding of genetic architecture and heritability of performance and production traits. The 2023 Workshop identified present aims for aquaculture genomics/omics research to build on this progress: (1) advancing reference genome assembly quality; (2) integrating multi-omics data to enhance analysis of production and performance traits; (3) developing resources for the collection and integration of phenomics data; (4) creating pathways for applying and integrating genomics information across animal industries; and (5) providing training, extension, and outreach to support the application of genome to phenome. Research focuses should emphasize phenomics data collection, artificial intelligence, identifying causative relationships between genotypes and phenotypes, establishing pathways to apply genomic information and tools across aquaculture industries, and an expansion of training programs for the next-generation workforce to facilitate integration of genomic sciences into aquaculture operations to enhance productivity, competitiveness, and sustainability.

CONCLUSION

This collective vision of applying genomics to aquaculture breeding with focus on the highlighted priorities is intended to facilitate the continued advancement of the United States aquaculture genomics, genetics and breeding research community and industries. Critical challenges ahead include the practical application of genomic tools and analytical frameworks beyond academic and research communities that require collaborative partnerships between academia, government, and industry. The scope of this review encompasses the use of omics tools and applications in the study of aquatic animals cultivated for human consumption in aquaculture settings throughout their life-cycle.

Evaluation of industrial and consumer 3-D resin printer fabrication of microdevices for quality management of genetic resources in aquatic species

Aquatic germplasm repositories can play a pivotal role in securing the genetic diversity of natural populations and agriculturally important aquatic species. However, existing technologies for repository development and operation face challenges in terms of accuracy, precision, efficiency, and cost-effectiveness, especially for microdevices used in gamete quality evaluation. Quality management is critical throughout genetic resource protection processes from sample collection to final usage. In this study, we examined the potential of using three-dimensional (3-D) stereolithography resin printing to address these challenges and evaluated the overall capabilities and limitations of a representative industrial 3-D resin printer with a price of US$18,000, a consumer-level printer with a price Collapse

Key Words

• 3-D resin printing
• Aquaculture industry
• Aquatic species
• Genetic resources
• Germplasm repository
• Photolithography
• Soft lithography

Collapse

MESH Headings Collapse

Grants

• R24 OD028443 NIH HHS
• R24 OD034058 NIH HHS

Collapse

Affiliation(s)

Seyedmajid Hosseini Department of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, USA
Jack C. Koch Aquatic Germplasm and Genetic Resources Center, School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
Yue Liu Aquatic Germplasm and Genetic Resources Center, School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA
Ignatius Semmes Department of Biological & Agricultural Engineering, Louisiana State University and Louisiana State University Agricultural Center, Baton Rouge, LA, USA
Isabelina Nahmens Department of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, LA, USA
W. Todd Monroe Department of Biological & Agricultural Engineering, Louisiana State University and Louisiana State University Agricultural Center, Baton Rouge, LA, USA
Jian Xu Department of Electrical & Computer Engineering, Louisiana State University, Baton Rouge, LA, USA
Terrence R. Tiersch Aquatic Germplasm and Genetic Resources Center, School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA, USA

Collapse

Exploring the influential factors and improvement strategies for digital information literacy among the elderly: An analysis based on integrated learning algorithms

Objective

Despite previous research identifying factors such as age, education level, income, and interest in technology that influence digital literacy among the elderly, this study attempts to use machine learning algorithms, especially ensemble learning algorithms, to predict and identify the key factors that affect the digital information literacy of the elderly, so as to propose effective strategies to improve the elderly's ability to utilize digital information and better integrate into the digital society.

Methods

This study used primary data on older adults from the Digital Divide Survey 2022 conducted by the Korea National Information Society Agency. A predictive model was built, and 15 variables that were highly important in predicting digital information literacy were identified. Prediction accuracy was assessed using an ensemble of algorithms including Random Forest, LGBM, XGBoost, AdaBoost, and CatBoost.

Results

The study found that in addition to demographic factors and personal technology use ability factors, relationship support factors and social digital environment factors are also important predictors of digital information literacy for the elderly. Among different predictive models, the CatBoost model, based on boosting ensemble, exhibited the highest predictive accuracy at 86.2%, followed by Random Forest (85.5%), LGBM (85.2%), XGBoost (84.5%), and AdaBoost (83.8%). The predictive accuracies of these models were higher than those of traditional machine learning models, indicating the effectiveness of ensemble learning algorithms in predicting digital information literacy among the elderly.

Conclusions

The academic significance of this study lies in the application of artificial intelligence technologies to the social sciences, specifically demonstrating the effectiveness of ensemble learning algorithms in predicting factors influencing the digital literacy levels of the elderly. This approach provides a novel and powerful tool for addressing complex social issues. The practical significance lies in the proposed strategies for improving the digital literacy of the elderly based on the research results, including education and training, social relationship support, social participation, technical support, and policy formulation, aiming to help the elderly better adapt to the digital environment, narrow the digital divide, and enhance the elderly's sense of participation and happiness in the digital society.

Gynecological Healthcare: Unveiling Pelvic Masses Classification through Evolutionary Gravitational Neocognitron Neural Network Optimized with Nomadic People Optimizer

Accurate and early detection of malignant pelvic mass is important for a suitable referral, triage, and for further care for the women diagnosed with a pelvic mass. Several deep learning (DL) methods have been proposed to detect pelvic masses but other methods cannot provide sufficient accuracy and increase the computational time while classifying the pelvic mass. To overcome these issues, in this manuscript, the evolutionary gravitational neocognitron neural network optimized with nomadic people optimizer for gynecological abdominal pelvic masses classification is proposed for classifying the pelvic masses (EGNNN-NPOA-PM-UI). The real time ultrasound pelvic mass images are augmented using random transformation. Then the augmented images are given to the 3D Tsallis entropy-based multilevel thresholding technique for extraction of the ROI region and its features are further extracted with the help of fast discrete curvelet transform with the wrapping (FDCT-WRP) method. Therefore, in this work, EGNNN optimized with nomadic people optimizer (NPOA) was utilized for classifying the gynecological abdominal pelvic masses. It was executed in PYTHON and the efficiency of the proposed method analyzed under several performance metrics. The proposed EGNNN-NPOA-PM-UI methods attained 99.8%. Ultrasound image analysis using the proposed EGNNN-NPOA-PM-UI methods can accurately predict pelvic masses analyzed with the existing methods.

An automated modular open-technology device to measure and adjust concentration of aquatic sperm samples for cryopreservation

Repositories for aquatic germplasm are essential for safeguarding valuable genetic diversity for species relevant to aquaculture, biomedical research, and conservation. Development of aquatic germplasm repositories is impeded by a lack of standardization within laboratories and across the research community. Protocols for cryopreservation are often developed ad hoc and without close attention to variables, such as cell concentration, that strongly affect the success and reproducibility of cryopreservation. The wide dissemination and use of specialized tools and devices as open hardware can improve processing reliability and save costs. The goal of the present work was to develop and prototype a modular and open-technology approach to help to standardize the cell concentration of germplasm samples prior to cryopreservation. The specific objectives were to: 1) design and fabricate prototypes of the automated concentration measurement and adjustment system (CMAS), incorporating custom peristaltic pumps and optical evaluation modules, and 2) evaluate the performance of the CMAS with biological samples. Linear regression models were obtained for estimation of aquatic sperm concentration >108 cells/mL and for algae concentration > (3 × 105) cells/mL. Algae were diluted with extender medium by an automated process, resulting in a dilution precision of ±12.6% and ±6.7% in two trials, attaining means of 89% and 71% of the target cell concentration. The development of the CMAS as open technology can provide opportunities for community-level standardization in cryopreservation of aquatic germplasm and can invite new users, makers, and developers into the open-technology community. This will increase the reach and capabilities of much-needed aquatic germplasm repositories.

An Unhealthy Webpage Discovery System Based on Convolutional Neural Network

Currently, with the popularity of the internet, people are surrounded by a large number of unhealthy pages which have a serious impact on the physical and mental health of visitors. To protect the legitimate rights and interests of internet users from infringement and maintain the harmonious and stable development of society, a new unhealthy webpage discovery system is needed. First, this paper proposed the knowledge of unhealthy webpages and web crawlers, and then the whole system's plan and design were introduced. The test results show that the unhealthy webpage discovery system can meet the needs of users. This experiment uses a CNN algorithm to classify the text and completes the collection and classification of unhealthy information through URL acquisition and URL filtering. The experimental results show that the unhealthy webpage discovery system based on a convolutional neural network can greatly improve the accuracy of unhealthy webpage discovery and reduce the omission rate, which can meet the needs of users for unhealthy webpage discovery.