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Research Article
Data Analysis of Cancer Incidence and Mortality Disparities Among African Americans in Louisiana
Prasanthi Sreekumari*
,
Abhishek Amgain
Issue:
Volume 8, Issue 2, June 2025
Pages:
40-51
Received:
5 March 2025
Accepted:
21 March 2025
Published:
27 April 2025
DOI:
10.11648/j.ajcst.20250802.11
Downloads:
Views:
Abstract: Cancer is a major cause of death in the United States. Different racial and ethnic groups have different cancer rates and survival chances. In Louisiana, African Americans have some of the highest cancer rates in the state, according to a recent report. Louisiana also ranks fifth in the country for cancer deaths, making the issue even more serious. Several factors contribute to these differences. Many African Americans in Louisiana have limited access to healthcare, struggle financially, and live in areas with environmental risks. Because of these challenges, they often cannot get cancer screenings and treatment early, leading to late diagnoses and worse health outcomes. Low income and unemployment make it even harder to get proper medical care. Some areas also have high pollution, which can increase cancer risk. Cancer rates vary between cities and rural areas in Louisiana. Rural areas have fewer hospitals and doctors, causing delays in diagnosis. Cities may have more hospitals, but more pollution, which can raise cancer risks. Gender also affects cancer rates. African American men are more likely to get prostate and thyroid cancer, while African American women are more likely to develop severe types of liver and breast cancer. These differences show the need for better prevention and treatment plans. In this research, we study the five most common cancers among African Americans in Louisiana over the past five years, focusing on gender and location. Using Python and tools like Pandas, Seaborn, Matplotlib and SAS, the research analyzes cancer patterns and causes. The goal is to find useful information to improve public health and make healthcare more accessible for African Americans in Louisiana.
Abstract: Cancer is a major cause of death in the United States. Different racial and ethnic groups have different cancer rates and survival chances. In Louisiana, African Americans have some of the highest cancer rates in the state, according to a recent report. Louisiana also ranks fifth in the country for cancer deaths, making the issue even more serious....
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Research Article
Multi-model Database Design and Query Processing in NewSQL DBMSs
Joachim Tankoano*
Issue:
Volume 8, Issue 2, June 2025
Pages:
52-71
Received:
4 April 2025
Accepted:
17 April 2025
Published:
9 May 2025
Abstract: NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enterprise's operations, with the goal of storing and managing them within only one database. This method is based on a Relational Data Store and the Nested Relational Model. It allows the designer to use the Data Store as a guarantor of integrity and to optimize it for hybrid workloads (transactional and analytical), performance, scalability, and continuous data availability. As for the nested relational model, it is used by the designer to: (1) clarify their choices regarding storage models that can enable fast access to data about complex real-world entities; (2) specify access paths that can meet user needs. The main interest and the originality of this methodological approach are that this enables us to use the Nested Relational Model as a Pivot Model to: (1) automatically generate the global external schemas of the NoSQL virtual databases, allowing users to view and manipulate the Data Store as if it were a NoSQL database (object-relational, XML, JSON, or graph-oriented), and (2) unify the processing of cross-model SQL queries through an innovative and efficient approach. This method consistently integrates, through five levels of abstraction, the design process of the relational Data Store and that of the virtual databases. The research method used consisted of: (1) defining the objectives of this approach, (2) identifying the required levels of abstraction in light of the targeted objectives, (3) determining, for each level of abstraction, its specific objectives as well as the role to be played by the designer, a design support tool, and the DBMS, and (4) applying this approach to a typical example reflecting the most common needs, in order to facilitate the understanding of its contributions and relevance with respect to the intended objectives.
Abstract: NewSQL DBMSs are hybrid systems that combine the advantages of both SQL DBMSs and NoSQL DBMSs. This paper proposes a method for designing a database to be implemented on a NewSQL DBMS. The objectives of this method are identifying and defining heterogeneous collections of values that adhere to different data models, which are essential for an enter...
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Research Article
A Next-Generation Metaheuristic Inspired by Nomadic Behavior: The Raute Algorithm
Yam Krishna Poudel*,
Rajiv Kumar
Issue:
Volume 8, Issue 2, June 2025
Pages:
72-84
Received:
11 April 2025
Accepted:
21 April 2025
Published:
22 May 2025
Abstract: The Raute community of Nepal is the one of the last remaining nomadic indigenous groups. Which exhibits a unique lifestyle centered on adaptation, mobility, and resource management. Their dynamic movement patterns, decision-making hierarchy, and sustainable utilization of resources provide inspiration for a novel computational approach. Which is called the Raute Metaheuristic Algorithm. This algorithm applies key aspects of the Raute people's survival strategies to address complex optimization problems. The Rautes continuously migrate to locate optimal resources while avoiding depletion and the Raute Algorithm dynamically balances exploration and exploitation. This is inspired by the Raute leadership structure which has its hierarchical decision-making model allows solutions to adapt based on the best-performing agents. Therefore, it preventing early convergence and ensuring an efficient search for global optima in high-dimensional spaces. Also, the algorithm integrates a resource-based movement probability function and which ensuring strategic migration away from low-quality solutions. To validate the effectiveness of the Raute Algorithm it is apply to the Rastrigin function. Which is a well-known multimodal benchmark problem in optimization. Comparative analysis with Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) shows that the Raute Algorithm achieves competitive accuracy. It improves the convergence speed, and superior robustness in avoiding local optima. A comprehensive attribute comparison highlights its scalability, adaptability, and computational efficiency which making it particularly well-suited for dynamic and real-time optimization challenges. The Raute Algorithm presents substantial opportunities in real-world domains beyond theoretical applications which including engineering optimization, artificial intelligence, supply chain management and data science. This research not only enhances computational innovation but also highlights the importance of cultural heritage in shaping innovative problem-solving methodologies while integrating indigenous wisdom into computational intelligence. The Raute Algorithm is the findings which is nature-inspired and human-centric approaches: contribute to the next generation of efficient and adaptive metaheuristic techniques.
Abstract: The Raute community of Nepal is the one of the last remaining nomadic indigenous groups. Which exhibits a unique lifestyle centered on adaptation, mobility, and resource management. Their dynamic movement patterns, decision-making hierarchy, and sustainable utilization of resources provide inspiration for a novel computational approach. Which is ca...
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Research Article
Prompt-oriented Output of Culture-Specific Items in Translated African Poetry by Large Language Models: An Initial Multi-layered Tabular Review
Adeyola Opaluwah*
Issue:
Volume 8, Issue 2, June 2025
Pages:
85-101
Received:
17 March 2025
Accepted:
15 May 2025
Published:
18 June 2025
Abstract: This paper examines the output of culture-specific items (CSIs) generated by ChatGPT 3.5 and ChatGPT Pro in response to three prompts to translate three anthologies of African poetry. The first prompt was broad, the second focused on poetic structure, and the third emphasized cultural specificity. To support this analysis, five comparative tables were created. The first and second tables presents the results of the CSIs produced by Chat GPT 3.5 and ChatGPT Pro respectively after the three prompts; the third table categorizes the unchanged CSIs based on Aixelá’s framework of “Proper nouns and Common expressions”; the fourth summarizes the CSIs generated by the human translators, a custom-built translation engine (CTE), and the two versions of a Large Language Model (LLM). The fifth table shows how the seven CSIs that were repeated in translation in French were rendered after the three prompts. The sixth table shows the strategies employed by ChatGPT 3,5 and ChatGPT Pro after the culture-specific prompt on the CSIs that were not translated unrepeated. Compared to the outputs of CSIs from the reference human translation (HT) and the CTE in prior studies, the findings indicate that the culture-oriented prompts used with ChatGPT Pro did not yield significant enhancements in the CSIs during the translation of the three African poetry from English to French. On evaluation however, ChatGPT Pro scored better in BLEURT than ChatGPT 3.5. A combined total of 20 CSIs were generated by the LLM versions, where 13 were repeated as the source word. The repeated CSIs were inconsistent with the outcome of the HT and CTE; some of the translations of the remaining seven unrepeated CSIs were also inaccurate compared to the reference HT and CTE. While the corpus of this investigation is small, the results show that the data used to build LLMs has not been French-centric nor poetry domain-specific and thus LLMs could benefit from a higher and better performance when tailored to other languages and specific domains.
Abstract: This paper examines the output of culture-specific items (CSIs) generated by ChatGPT 3.5 and ChatGPT Pro in response to three prompts to translate three anthologies of African poetry. The first prompt was broad, the second focused on poetic structure, and the third emphasized cultural specificity. To support this analysis, five comparative tables w...
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Research Article
Adoption of Web Application Framework to Enhance Web Project Result
Biftu Kasahun Olana*,
Diriba Gonfa Tolasa
Issue:
Volume 8, Issue 2, June 2025
Pages:
102-120
Received:
1 April 2025
Accepted:
3 May 2025
Published:
18 June 2025
Abstract: Developers selects a web application frame work to enhance web project result based on their web developing experience, the framework’s amount of documentation, or the framework ‘s popularity (regardless of its functional or non-functional requirements). Selecting the best of the many frameworks available can pose a problem. The purpose of study is to adopt web application framework that to improve the performance of web projects result using five selected web application frameworks. The thesis developed using five frameworks like, Angular, React, Vue, Ember, and Svelte in order to build back-end web framework and compare the difference between them and to give the general ideas of the five selected frameworks, for functionality and user accessibility, front-end web app frameworks are created using CSS3, HTML5, and JavaScript. Consequently, an in-depth data collecting and analyzing technique such as Google form is a useful tool for traveling and recognizing the several aspects of web framework usability or accessibility and specially the decision-making process of using or not using a web framework. From the analysis of the respondents’ answer using web frameworks; we can see the decision-making process of enhancing front end and back-end web app frameworks. The explanatory research method is used to answer the research questions in the study, in inductive approach. The researcher‘s experience is compared to other relevant published sources. Example source codes are extracted from Author Le, Anh. The research results show an expected range. It confirms the positive effects of implementing web framework in web application development. Features, which have been tested in Le, Anh, are presented and compared to their originals. Some example source codes are extracted from the application to prove the result. To conclude, the reasons and recommendations are put forward. Although the frameworks and their communities have grown up grammatically and are near maturing, there are still lacking of features and create a project using the recommended frame works. Therefore, further study is recommended on those and develop additional new framework model.
Abstract: Developers selects a web application frame work to enhance web project result based on their web developing experience, the framework’s amount of documentation, or the framework ‘s popularity (regardless of its functional or non-functional requirements). Selecting the best of the many frameworks available can pose a problem. The purpose of study is...
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Research Article
Transformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Issue:
Volume 8, Issue 2, June 2025
Pages:
121-137
Received:
7 May 2025
Accepted:
27 May 2025
Published:
23 June 2025
Abstract: Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potential of CNNs for medical image analysis faces two limitations: they fail to process both local information and wide contextual data adequately and do not provide explainable AI (XAI) operations which doctors need to accept them in clinics. The researcher developed the MammoFormer framework which unites transformer-based architecture with multi-feature enhancement components and XAI functionalities within one framework. Seven different architectures consisting of CNNs, Vision Transformer, Swin Transformer, and ConvNext were tested alongside four enhancement techniques, including original images, negative transformation, adaptive histogram equalization, and histogram of oriented gradients. The MammoFormer framework addresses critical clinical adoption barriers of AI mammography systems through: (1) systematic optimization of transformer architectures via architecture-specific feature enhancement, achieving up to 13% performance improvement, (2) comprehensive explainable AI integration providing multi-perspective diagnostic interpretability, and (3) a clinically deployable ensemble system combining CNN reliability with transformer global context modeling. The combination of transformer models with suitable feature enhancements enables them to achieve equal or better results than CNN approaches. ViT achieves 98.3% accuracy alongside AHE while Swin Transformer gains a 13.0% advantage through HOG enhancements. Five XAI techniques, including Integrated Gradients, GradCAM, Occlusion, DeepLIFT and Saliency maps demonstrate that transformer models effectively recognize diagnostically significant features by capturing long-range dependencies in mammograms. Using HOG features provides the most reliable enhancement impact (98.4% average accuracy) among all visualization methods. MammoFormer establishes a clinical breast cancer screening workflow with diagnostic precision and interpretability through its simultaneous implementation of architectural design with feature enhancement and explainability features.
Abstract: Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potential of CNNs for medical image analysis faces two limitations: they fail to process both local information and wide contextual data...
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