Digital Database Research Articles

Multi-class Breast Cancer Classification Using CNN Features Hybridization

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for earlier breast cancer classification. Meanwhile, the design of CAD tools using deep learning is becoming popular and robust in biomedical classification systems. However, deep learning gives inadequate performance when used for multilabel classification problems, especially if the dataset has an uneven distribution of output targets. And this problem is prevalent in publicly available breast cancer datasets. To overcome this, the paper integrates the learning and discrimination ability of multiple convolution neural networks such as VGG16, VGG19, ResNet50, and DenseNet121 architectures for breast cancer classification. Accordingly, the approach of fusion of hybrid deep features (FHDF) is proposed to capture more potential information and attain improved classification performance. This way, the research utilizes digital mammogram images for earlier breast tumor detection. The proposed approach is evaluated on three public breast cancer datasets: mammographic image analysis society (MIAS), curated breast imaging subset of digital database for screening mammography (CBIS-DDSM), and INbreast databases. The attained results are then compared with base convolutional neural networks (CNN) architectures and the late fusion approach. For MIAS, CBIS-DDSM, and INbreast datasets, the proposed FHDF approach provides maximum performance of 98.706%, 97.734%, and 98.834% of accuracy in classifying three classes of breast cancer severities.

Prevalence of Zygomatic Air Cell defects in a sample of 6-18-year-old patients in Saudi Arabia and Yemen: a retrospective study

ObjectivesThis study aimed to comprehensively analyze the prevalence and influencing factors of Zygomatic Air Cell Defects (ZACD) among pediatric and adolescent populations in Saudi Arabia and Yemen.Materials and methodsA cross-sectional retrospective study was conducted, utilizing digital panoramic radiographs of dental outpatients aged 6 to 18 years. The data were collected from registered digital databases of dental clinics in Al-Qassim, Saudi Arabia, and an oral and maxillofacial radiology center in Sana’a, Yemen, covering the period from July 2018 to September 2022. The radiographs were evaluated based on inclusion and exclusion criteria, and ZACD was assessed using standardized descriptions. Statistical analysis, including logistic regression, was employed to examine the impact of the associated factors.ResultsThe study encompassed a total of 3,169 participants, revealing an overall ZACD prevalence of 28.5%. Age emerged as a significant predictor (p ≤ 0.0001), with the likelihood of ZACD increasing as the age of the individual advances. Gender and geographic region did not exhibit statistically significant differences in ZACD prevalence.ConclusionThis study provides crucial insights into the prevalence of Zygomatic Air Cell Defects among pediatric and adolescent populations in Saudi Arabia and Yemen. It underscores the prevalence of ZACD and the notable influence of age on its occurrence. Additionally, the research challenges prior notions of gender and regional variations in ZACD prevalence, emphasizing the complexity of the factors involved. Early detection is essential to avoid unwanted complications during any surgical intervention in this area.

Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.

This systematic literature review aims to study the role and impact of artificial intelligence (AI) in transforming Ear, Nose, and Throat (ENT) healthcare. It aims to compare and analyse literature that applied AI algorithms for ENT disease prediction and detection based on their effectiveness, methods, dataset, and performance. We have also discussed ENT specialists' challenges and AI's role in solving them. This review also discusses the challenges faced by AI researchers. This systematic review was completed using PRISMA guidelines. Data was extracted from several reputable digital databases, including PubMed, Medline, SpringerLink, Elsevier, Google Scholar, ScienceDirect, and IEEExplore. The search criteria included studies recently published between 2018 and 2024 related to the application of AI for ENT healthcare. After removing duplicate studies and quality assessments, we reviewed eligible articles and responded to the research questions. This review aims to provide a comprehensive overview of the current state of AI applications in ENT healthcare. Among the 3257 unique studies, 27 were selected as primary studies. About 62.5% of the included studies were effective in providing disease predictions. We found that Pretrained DL models are more in application than CNN algorithms when employed for ENT disease predictions. The accuracy of models ranged between 75 and 97%. We also observed the effectiveness of conversational AI models such as ChatGPT in the ENT discipline. The research in AI for ENT is advancing rapidly. Most of the models have achieved accuracy above 90%. However, the lack of good-quality data and data variability limits the overall ability of AI models to perform better for ENT disease prediction. Further research needs to be conducted while considering factors such as external validation and the issue of class imbalance.

A Transformative Technology Linking Patient’s mRNA Expression Profile to Anticancer Drug Efficacy

As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate, and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. The major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicability only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome the current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via the transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data were performed. Finally, a semi-supervised method was used for the database searching, matching, annotation, and ranking of drug efficacies from a pool of ~700 approved, investigational, or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decisions within a week. The PGA (patient-derived gene expression-informed anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. The implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

Preoperative Counseling in Spinal Cord Stimulation: A Designated Driver in Implantable Pulse Generator–Related Inconveniences?

BackgroundSpinal cord stimulation (SCS) has been reported to cause substantial pain relief and improved quality of life (QoL) in patients with persistent spinal pain syndrome (PSPS). Despite implantable pulse generator (IPG)-related inconveniences such as pain, shame, and discomfort affecting QoL and patient satisfaction, these are often neglected. Hence, the current study aims to determine the associations between patient satisfaction, IPG-related inconveniences, and preoperative counseling in a homogeneous group of patients with PSPS receiving SCS with IPG implantation in the gluteal or abdominal area. Materials and MethodsRetrospective data on sample characteristics were gathered from the EPIC (electronic health record software) digital patient data base. Prospective data on patient satisfaction were obtained with a questionnaire that covered various topics such as shame, pain, disturbances in daily/intense activities, night rest and/or sleep, discomfort caused by clothing, and preoperative counseling. The exact location of the IPG and its scar were determined with photo analysis. Thereafter, the site of IPG placement was classified into separate quadrants within the gluteal and abdominal area. Patient satisfaction was defined as accepting the current location of the IPG without having the wish to undergo revision surgery. ResultsIn total, 81 participants (50.9 ± 10 years) were included in this analysis, with patient satisfaction observed in 61 patients (75.3%). Among satisfied patients, more extensive preoperative counseling concerning IPG pain and discomfort was reported compared with patients who were not satisfied (p < 0.001). When comparing the two groups, significant differences were found in shame (8/81, 9.9%), IPG site pain (21/81, 25.9%), disturbance of activities (42/81, 51.9%), and clothing-related discomfort (42/81, 51.9%). ConclusionsOn the basis of the current results, shared decision-making and comprehensive preoperative provision of information are recommended to optimize patient satisfaction regarding IPG pain, discomfort, and inconveniences. Although many patients experience these disadvantages despite successful SCS for pain related to PSPS, most of them accept this if they have received adequate preoperative information about expectations.

Peran Wanita dalam Pengumpulan dan Penyebaran Hadis

The role of women in collecting and disseminating hadith is very significant. Women not only narrated the hadith but also taught it and ensured its preservation for future generations. Even without their contributions, much important knowledge about Islamic teachings and the life of the Prophet Muhammad SAW might not have reached us. Therefore, this research will discuss further the role and contribution of women in the collection and dissemination of hadith which is studied using a qualitative research approach with the search library method. Meanwhile, descriptive qualitative research using the search library method is a research approach used to explore and describe existing phenomena using information sources from books, journals, digital databases, and other reading sources. The aim and benefit of this research is that it can provide insight and raise awareness of the important role of women in collecting and disseminating hadith. By understanding their contributions, we not only gain a more complete picture of Islamic history but also appreciate the efforts and dedication of women in preserving and spreading Islamic teachings.

Navigating the Multimodal Landscape: A Review on Integration of Text and Image Data in Machine Learning Architectures

Images and text have become essential parts of the multimodal machine learning (MMML) framework in today’s world because data are always available, and technological breakthroughs bring disparate forms together, and while text adds semantic richness and narrative to images, images capture visual subtleties and emotions. Together, these two media improve knowledge beyond what would be possible with just one revolutionary application. This paper investigates feature extraction and advancement from text and image data using pre-trained models in MMML. It offers a thorough analysis of fusion architectures, outlining text and image data integration and evaluating their overall advantages and effects. Furthermore, it draws attention to the shortcomings and difficulties that MMML currently faces and guides areas that need more research and development. We have gathered 341 research articles from five digital library databases to accomplish this. Following a thorough assessment procedure, we have 88 research papers that enable us to evaluate MMML in detail. Our findings demonstrate that pre-trained models, such as BERT for text and ResNet for images, are predominantly employed for feature extraction due to their robust performance in diverse applications. Fusion techniques, ranging from simple concatenation to advanced attention mechanisms, are extensively adopted to enhance the representation of multimodal data. Despite these advancements, MMML models face significant challenges, including handling noisy data, optimizing dataset size, and ensuring robustness against adversarial attacks. Our findings highlight the necessity for further research to address these challenges, particularly in developing methods to improve the robustness of MMML models.

Establishing African genomics and bioinformatics programs through annual regional workshops.

The African BioGenome Project (AfricaBP) Open Institute for Genomics and Bioinformatics aims to overcome barriers to capacity building through its distributed African regional workshops and prioritizes the exchange of grassroots knowledge and innovation in biodiversity genomics and bioinformatics. In 2023, we implemented 28 workshops on biodiversity genomics and bioinformatics, covering 11 African countries across the 5 African geographical regions. These regional workshops trained 408 African scientists in hands-on molecular biology, genomics and bioinformatics techniques as well as the ethical, legal and social issues associated with acquiring genetic resources. Here, we discuss the implementation of transformative strategies, such as expanding the regional workshop model of AfricaBP to involve multiple countries, institutions and partners, including the proposed creation of an African digital database with sequence information relating to both biodiversity and agriculture. This will ultimately help create a critical mass of skilled genomics and bioinformatics scientists across Africa.

Software maintenance practices using agile methods towards cloud environment: A systematic mapping

AbstractAgile methods have emerged to overcome the obstacles of structured methodologies, such as the waterfall, prototype, spiral, and so on. There are studies showing the usefulness of agile approaches in software development. However, studies on Agile maintenance are very limited in number. Regardless of the chosen methodology, software maintenance can be carried out in either a local (on‐the‐premise) or global (distributed) environment. In a local environment, the software maintenance team is co‐located on the same premises, while in a global environment, the team is geographically dispersed from the customer. The main objective of this Systematic Mapping (SM) study is to identify the practices useful for software maintenance using the Agile approaches in the Cloud environment. We have conducted a comprehensive search in well‐known digital databases and examined the articles that map to the pre‐defined inclusion criteria. The study selected and analyzed 48 articles out of 320 published between 2000 and 2022. The findings of the mapping study reveal that Agile can resolve the major issues faced in traditional software maintenance, making the role of this approach significant in global/distributed software maintenance. Cloud computing plays a vital role in software maintenance. Most of the studies highlight the application of XP‐ and Scrum‐based Agile maintenance models. The study found a need for more Agile maintenance solutions in the cloud, highlighting the importance of agile in software maintenance, both locally and globally. Irrespective of the environment, Cloud computing provides a centralized platform for collaboration and communication, while also offering scalability and flexibility to adapt to diverse infrastructure needs. This allows agile maintenance practices to be implemented across both local and global environments, leveraging the cloud's capabilities to overcome geographical and infrastructural challenges.

Comparative digital study using four methods of measurements of the tibial distal anatomical axes for determining the anatomical-mechanical angle in dogs with cranial cruciate ligament rupture.

This study evaluates the amplitude of the anatomical-mechanical angle (AMA-angle) using 4 measuring methods of the tibial distal anatomical axes (DAA) previously described, comparing the literature results to determine if there are significant differences in patients with cranial cruciate ligament (CrCL) rupture. This study was comprised of 30 tibiae (29 dogs), including 1 bilateral case. A retrospective study was selected for this research. DAA measurements were performed on all surgically confirmed cases of canine CrCL rupture at Hospital Veterinário de Especialidades Bruselas from 2019 to 2022. Four different published methods (identified by surname of the corresponding author of the original publication) were compared. Tibial measurements were made using Veterinary Preoperative Orthopedic Planning Pro software (https://vpop-pro.com/) on mediolateral radiographic projections obtained from a digital database. The mean (range) in the DAA method conducted by Hulse obtained a AMA-angle of 5.4° (3.3 to 8.1°), 3.0° (0 to 5.8°) for Osmond et al, 3.2° (0.9 to 6°) for Miles, and 5.9° (2.4 to 8.8°) for Tudury. Differences among the means of the AMA-angle of the authors methods were found with a statistical difference (P < .05), except between Osmond and Miles. The mean AMA-angle with the Osmond method concurred with previous study results that determined the magnitude using the same measurement method of DAA in patients with CrCL rupture. The AMA-angle magnitude has been associated with higher sensitivity and specificity for predicting the development of CrCL rupture compared to other anatomical factors evaluated; therefore, future comparisons with different methods of measurement of the DAA between healthy and affected patients are recommended to determine whether any of them can increase the percentage value as a predictive factor for the occurrence of this condition.

Evaluation of Serum/Plasma Levels of Interleukins (IL-6, IL-12, IL-17, IL-18, and IL-23) in Adults and Children with Obstructive Sleep Apnea: A Systematic Review, Meta-Analysis, and Trial Sequential Analysis.

Obstructive sleep apnea (OSA) is a chronic inflammatory disease characterized by partial or complete upper airway obstruction during sleep. We aimed to evaluate serum/plasma levels of several cytokines (interleukin [IL]-6, IL-12, IL-17, IL-18, and IL-23) in a systematic review meta-analysis in both adults and children with OSA compared with controls. We conducted a comprehensive search of 4 digital databases (PubMed, Web of Science, Scopus, and Cochrane Library) up until October 19, 2023, without any limitations. For our meta-analysis, we used Review Manager, version 5.3, and displayed the data as the standardized mean difference (SMD) and 95% confidence interval (CI) to assess the correlation between cytokine levels and OSA. We utilized Comprehensive Meta-Analysis version 3.0 software to conduct bias analyses, meta-regression, and sensitivity analyses. From 1881 records, 84 articles were included in the systematic review and meta-analysis. In adults, the pooled SMDs for IL-6 level were 0.79 (P value < 0.00001), for IL-17 level were 0.74 (P value = 0.14), and for IL-18 level were 0.43 (P value = 0.00002). In children, the pooled SMD for IL-6 was 1.10 (P value < 0.00001), for IL-12 was 0.47 (P value = 0.10), for IL-17 was 2.21 (a P value = 0.24), for IL-18 was 0.19 (P value = 0.07), and for IL-23 was 2.46 (P value < 0.0001). The subgroup analysis showed that the ethnicity, mean body mass index, and mean apnea-hypopnea index for IL-6 levels in adults and the ethnicity for IL-6 levels in children were effective factors in the pooled SMD. The findings of the trial sequential analysis revealed that adequate evidence has been obtained. The analysis of IL levels in adults and children with OSA compared with those without OSA revealed significant differences. In adults, IL-6 and IL-18 levels were significantly higher in the OSA group, while in children, only IL-6 and IL-23 levels were significantly elevated.

Pemanfaatan Key Opinion Leader (KOL) Brand Scarlett Whitening dalam Pemasaran Digital Pada Generasi Z

In facing the rapidly developing digital era, Scarlett Whitening has succeeded in combining traditional business principles with digital marketing methods. One important approach taken is through collaboration with Key Opinion Leaders (KOL) on various social media platforms, such as Instagram and TikTok. With appropriate KOL support, Scarlett Whitening succeeded in reaching generation Z, its main target market, with a more personal approach and in line with brand needs. This research aims to analyze the use of KOL brand Scarlett whitening in Generation Z. The research was conducted using a descriptive qualitative approach. Research data comes from search results in various digital databases and literature reviews. The research results show that by utilizing Key Opinion Leaders (KOL) in digital marketing strategies, Scarlett Whitening has succeeded in aligning the use of KOLs in its digital marketing strategy, such as through collaboration with KOLs who have significant influence and reach among the younger generation. The selected KOLs are not only popular because of their large number of followers, but also the content they present is of high quality and suits the interests and needs of the target market. Scarlett Whitening's main target market is teenagers aged 15-34 years, namely Generation Z, who have a tendency to be very familiar with digital technology. With a more personal approach and in line with the needs of the target market, Scarlett Whitening has succeeded in reaching generation Z with quality content that suits the interests and needs of the target market. The content produced by KOL for Scarlett Whitening often contains product reviews, usage tutorials, and testimonials from consumers. Through this content, information about the quality, safety and effectiveness of the Scarlett Whitening brand can be conveyed in an authentic and convincing way to potential consumers.

Strategi Pemasaran Flash Sale Live Shopping Tiktok Shop Terhadap Minat Konsumen Dalam Membeli Produk Maybelline

The development of digital marketing has given birth to various marketing innovations that were previously unknown. One form of digital marketing innovation currently is Flash Shale Live Shopping on Tiktok. This research aims to analyze the Tiktok Shop Flash Sale Live Shopping marketing strategy on consumer buying interest in Maybelline products. The research was carried out using a descriptive qualitative approach, research data came from search results in various digital databases. The research results show that Live shopping on the TikTok Shop has proven effective in influencing consumer buying interest in Maybelline products. This success was driven by several factors: 1). Live shopping videos that display products in real time and attractive visualizations. 2). Prices during the duration of live shopping. Maybelline provides several promotions such as Buy 1 Get 1 and discount voucher promos. 3). Time, the Maybelline brand carries out live shopping for 24 hours so that it can always connect and interact with consumers such as product question and answer sessions in the comments column. 4). Maybelline products display single and bundled products at more economical prices when live shopping. The fifth factor is flash sales, the Maybelline brand uses a flash sale marketing strategy to provide big discounts for a limited time. With the live shopping marketing strategy, it also makes it easier for the Maybelline brand to meet consumer buying interest indicators, namely attention, interest, desire and action, that is, buyers can be interested and immediately buy products in live shopping sessions.

Role of Strategic Human Resource Management and the Development of Information Systems for the Enhancement of Libraries

Human Resource Management (HRM) plays a significant role in incorporating HRM into the library planning procedure, which enhances Human Resource (HR)'s capacity to foresee the requirements of its users. Efficiently managing the HR of a library is crucial for achieving the library's objectives. The synchronization of HRM refers to the integration of choices about employees with decisions regarding the desired goals of a business. The primary fields of concentration should be diversity, hiring, preservation, personnel growth, and workers' quality. When these tasks are effectively organized and executed, they contribute to achieving the library's objective by using individuals with appropriate expertise in suitable roles. The study's primary goals were to evaluate the current state of HRM, its impact on strategic leadership, and the growth of library Information Systems (IS) projects and services. To achieve these goals, the researcher used the case study. The sample only consisted of Libraries with abundant resources, encompassed a minimum of 0.1 million bound quantities, offered access to digital Databases, and employed at least four qualified librarians throughout the data collecting period. Some libraries had already begun the planning procedure, while others were on the verge of starting it. This research included a combination of qualitative and quantitative methodologies. The data collection tools used were self-administered and semi-structured surveys. The findings indicate a robust correlation between HRM and the advancement of strategic planning in libraries. The growth of workers, staff inspiration, and work happiness should be considered.

The Relationship between Problem-Solving Skills and Student Academic Achievement: A Meta-Analysis in Education

The use of meta-analysis allows the results obtained to be representative, and meta-analysis of the correlation between problem-solving skills and students' learning outcomes and/or achievements is no exception. Problem-solving skills are very important for students in various learning contexts. This study aims to describe the relationship and effects of problem-solving skills on learning outcomes and or student achievement. This study used a descriptive quantitative method using meta-analysis that focuses on the relationship between two or more variables. The meta-analysis was conducted on 22 articles selected from the screening of articles from 2008-2023 in the SINTA (Science and Technology Index) and Garuda (Garba Rujukan Digital) databases. The results showed that the impact of problem-solving ability on learning outcomes and achievement was very significant at 84.8%. There is a strong positive correlation (r = 0.69) between problem-solving ability and student learning outcomes and/or achievement. The distribution of studies in this meta-analysis is relatively symmetrical, and there is no publication bias. Nonetheless, it is necessary to consider potential sources of heterogeneity when applying these findings to educational practice.

Exporting Beekeeping Products: Importance of Detailed Market Analysis and Brand Positioning

To strengthen the export potential of Georgian bees and intervene in international markets, it is necessary to make a comprehensive and profound analysis. The article discusses the influences of market segmentation, consumers’ preferences, international trading agreements, legal regulations, product certification, technological innovations and competition on the development of marketing and branding strategies, supporting the efficient positioning of the high-quality Georgian bee products on the international markets and increase in export. Competition evaluation is critical for determining market possibilities and positioning Georgian beekeeping on global markets. The physiological and qualitative functions of the Georgian mountain grey bee must be emphasized: its gentleness, excessive productivity, and resistance to ailments. The article additionally mentions the importance of a strategic method to boost export capability in integrating technological improvements and contemporary advertising strategies, which include digital technology and database analytics, which will have an instantaneous effect on increasing the charm of beekeeping products. Determining purchaser preferences, cooperation with international organizations and obtaining first-rate certificates are crucial for the global reputation of Georgian beekeeping manufacturers and gaining agreement with customers. Market analysis, contemporary branding, and dynamic marketing are considered the basis for the hit export of Georgian beekeeping products and their established order in international markets.

Persuasive Systems Design Trends in Coronary Heart Disease Management: Scoping Review of Randomized Controlled Trials.

Behavior change support systems (BCSSs) have the potential to help people maintain healthy lifestyles and aid in the self-management of coronary heart disease (CHD). The Persuasive Systems Design (PSD) model is a framework for designing and evaluating systems designed to support lifestyle modifications and health behavior change using information and communication technology. However, evidence for the underlying design principles behind BCSSs for CHD has not been extensively reported in the literature. This scoping review aims to identify existing health BCSSs for CHD, report the characteristics of these systems, and describe the persuasion context and persuasive design principles of these systems based on the PSD framework. Using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, 3 digital databases (Scopus, Web of Science, and MEDLINE) were searched between 2010 to 2022. The major inclusion criteria for studies were in accordance with the PICO (Population, Intervention, Comparison, and Outcome) approach. Searches conducted in the databases identified 1195 papers, among which 30 were identified as eligible for the review. The most interesting characteristics of the BCSSs were the predominant use of primary task support principles, followed by dialogue support and credibility support and the sparing use of social support principles. Theories of behavior change such as the Social Cognitive Theory and Self-Efficacy Theory were used often to underpin these systems. However, significant trends in the use of persuasive system features on par with behavior change theories could not be established from the reviewed studies. This points to the fact that there is still no theoretical consensus on how best to design interventions to promote behavior change in patients with CHD. Our results highlight key software features for designing BCSSs for the prevention and management of CHD. We encourage designers of behavior change interventions to evaluate the techniques that contributed to the success of the intervention. Future research should focus on evaluating the effectiveness of the interventions, persuasive design principles, and behavior change theories using research methodologies such as meta-analysis.

Addressing Some Problems Related to Research on Mimbres Ceramics and their Iconography

The Mimbres Black-on-white ceramic tradition that flourished in the region centered in southwest New Mexico ca. AD 750-1130 offers the scholar an almost unrivaled corpus of iconography and has been the subject of many interpretative studies. Unfortunately, much of this research is compromised by the incorporation of vessels from both private and museum collections that lack provenience and provenance information. The publication of privately-held ceramics can increase their market value, and both categories potentially incorporate deceptive post-excavation modifications. Most Mimbres archaeologists have long been aware of this situation, but recent analytical and statistical studies have confirmed these claims. Additionally, the creation of the Mimbres Pottery Images Digital Database (MimPIDD) has made it easier for the researcher to investigate the provenience and provenance of known vessels. This paper argues for studying only vessels with full provenience and provenance, and it proposes a secure dataset thereof for future research.

Predictive breast cancer diagnosis using ensemble fuzzy model

Breast cancer continues to be a major global health challenge, necessitating reliable diagnostic methods for early detection and improved patient outcomes. This study introduces a novel ensemble fuzzy model for predictive breast cancer diagnosis, integrating multiple deep-learning classifiers with fuzzy logic to enhance decision-making. Traditional diagnostic approaches often struggle with the complexity and heterogeneity of breast cancer data, which this new model addresses through an innovative ensemble technique. The ensemble model combines the strengths of Inception-V4, Inception-ResNet, and Inception V3/V4 + BN, with fuzzy logic for adaptive priority assignment based on confidence scores. The method employs a re-parameterized Gompertz function to assign fuzzy ranks to constituent classifiers, allowing flexible fusion strategies. The proposed model is evaluated on two benchmark breast cancer datasets: the Digital Database for Screening Mammography (DDSM) and the Breast Cancer Histopathological Image Classification (BACH) dataset. It achieves high performance across key metrics, including accuracy, precision, recall, and F1 score, consistently outperforming individual classifiers. On the DDSM dataset, the ensemble fuzzy model attains an accuracy of 0.97, a recall of 0.93, a precision of 0.95, and an F1 score of 0.96. Similarly, on the BACH dataset, the proposed method records an accuracy of 97.05%, a recall of 99.31%, a precision of 95.44%, and an F1 score of 97.37%, demonstrating its robust capability to identify positive instances and maintain a balanced performance. These results highlight the potential of the ensemble fuzzy model to improve breast cancer diagnosis, offering a reliable solution to the inherent challenges in this field.

Citizen labor

AbstractIn the last two decades, India's system of agricultural land management has been transitioning from paper to digital records. In effecting this shift, engineers and bureaucrats in the city of Bangalore, India's “Silicon Valley,” have tacitly and invisibly shifted the responsibility for maintaining data from the state to individuals. Moreover, the new digital databases of land records have fragmented offices and dispersed data across new sites and actors. Under these transformed conditions, people can access services only through what I call citizen labor. That is, when digitization is applied to land and property—which are quintessential sites for the making and unmaking of citizenship—people are interpellated into laboring on their own data. This shows that the digitization of government extracts a form of labor, one whose benefits accrue to groups and organizations beyond the laboring individual. As a result, people have an increasingly degraded experience of substantive citizenship.