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Perceptions of Safety and Social Interaction in Urban Neighborhoods
This study aims to understand the patterns of safety and social relations in residential areas of cities with special reference to Delhi. Qualitative data from the interviews and focus group discussions are combined with quantitative data collected from the survey questionnaire. This paper aims to establish the factors that affect residents perception of safety such as urban design features, engagement activities, and social capital. The study emphasizes the importance of lit streets, accessible greens, and pedestrian infrastructure in improving safety perceptions and social inclusion. Neighborhood watch and other cultural activities surface as vital in the promotion of trust and responsibility among people in the community. This paper also reveals how social networks and community resilience influence the dynamics of neighborhoods. Suggested strategies for the improvement of urban planning are the incorporation of the residents opinions into the design process, financing of public areas, and backing of projects that enhance social connections. By so doing, cities can foster an environment within which people feel secure, valued, and able to play an active part in the development of the citys fabric. 2025, Green Publication. All rights reserved. -
Geographies of Gender and Leadership: Regional Inequalities and Women in Omans Oil & Gas Sector
Introduction / Main Objective: This research explores Omani women's spatial and cultural barriers to leadership in the Oil & Gas industry. It looks at geographic location and regional differences and how these affect women's access to and experience of leadership. Background of the Problem: While significant advancements have been made in women's participation in the labor force in Oman, spatial disparities still exist whereby women in urban areas have more chances of leadership compared to women in rural or rural-remote towns. Cultural expectations and infrastructural constraints add to these geographical disparities. Novelty: This study innovatively combines a geographic perspective in gender and leadership research in Oman's Oil & Gas sector with an emphasis on regional disparities influencing women's career development. Research Methods: A qualitative method involving questionnaires was conducted among thirty women managers within various regions of Oman. Responses were processed to determine spatially connected obstacles and coping mechanisms. Findings: Women in urban areas like Muscat enjoy better access to education, professional networks, and organizational support, while women in rural areas are confronted by cultural conservatism, poor infrastructure, and lower promotion opportunities. There was an urban-rural leadership training and family support divide. Conclusion: Geography profoundly influences women's leadership paths in Oman's Oil & Gas industry. For policies to enhance gender equity, leadership development policy needs to take regional disparities into consideration and adapt interventions to local contexts. 2025, Green Publication. All rights reserved. -
The Role Of Leadership Behaviour On Team Success In Omani Healthcare: A Mediation Analysis In Diverse Clinical Settings
This study investigates the mediating role of leadership behavior in enhancing team processes and overall team success within the healthcare sector in Oman. Drawing upon the Leading Diversity (LeaD) model, the research conceptualizes the dynamics between leadership behavior, team process effectiveness, and team success in healthcare teams. A structured survey was administered to 25 team leaders representing hospitals and primary care centers across Oman, capturing perspectives on leadership competencies, team collaboration, and outcomes. Using Confirmatory Factor Analysis (CFA), the findings reveal that task-based leadership behavior partially mediates the relationship between effective team processes and team success. This indicates that while structured team processes are essential, their effectiveness is significantly enhanced when complemented by proactive, goal-oriented leadership. The study reinforces the critical role of leadership in navigating cultural and professional diversity among healthcare professionalsincluding physicians, nurses, technicians, and administrators. In Omans evolving healthcare landscape, characterized by modernization, resource pressures, and rising patient expectations, effective leadership is shown to improve patient care, reduce clinical errors, and enhance staff morale and communication. While the study accounts for potential social desirability and common method bias, measures were taken to minimize these effects. The application of the LeaD model in a healthcare context marks a novel contribution to leadership and healthcare management literature, emphasizing that inclusive, adaptive leadership is not only beneficial but necessary for delivering high-quality healthcare in multicultural environments. 2025, Green Publication. All rights reserved. -
The Role Of Leadership Behaviour On Team Success In Omani Healthcare: A Mediation Analysis In Diverse Clinical Settings
This study investigates the mediating role of leadership behavior in enhancing team processes and overall team success within the healthcare sector in Oman. Drawing upon the Leading Diversity (LeaD) model, the research conceptualizes the dynamics between leadership behavior, team process effectiveness, and team success in healthcare teams. A structured survey was administered to 25 team leaders representing hospitals and primary care centers across Oman, capturing perspectives on leadership competencies, team collaboration, and outcomes. Using Confirmatory Factor Analysis (CFA), the findings reveal that task-based leadership behavior partially mediates the relationship between effective team processes and team success. This indicates that while structured team processes are essential, their effectiveness is significantly enhanced when complemented by proactive, goal-oriented leadership. The study reinforces the critical role of leadership in navigating cultural and professional diversity among healthcare professionalsincluding physicians, nurses, technicians, and administrators. In Omans evolving healthcare landscape, characterized by modernization, resource pressures, and rising patient expectations, effective leadership is shown to improve patient care, reduce clinical errors, and enhance staff morale and communication. While the study accounts for potential social desirability and common method bias, measures were taken to minimize these effects. The application of the LeaD model in a healthcare context marks a novel contribution to leadership and healthcare management literature, emphasizing that inclusive, adaptive leadership is not only beneficial but necessary for delivering high-quality healthcare in multicultural environments. 2025, Green Publication. All rights reserved. -
Framing Conflict And Development: Media Narratives, Security Planning, And Regional Recovery In Post-Article 370 Jammu And Kashmir
The repeal of Article 370 in 2019 has brought about a drastic change in the political, security, and media situation in Jammu and Kashmir, changing the way the events related to the conflict are framed and perceived. This paper will analyze the reporting of the 2025 Pahalgam terror attack and the following Operation Sindoor in two of the most popular regional dailies, Greater Kashmir and Daily Excelsior. The study is based on the qualitative comparative methodology that is supported by the framing theory to compare the tone, stress, and editorial strategy through the purposive analysis of the front-page coverage of April 23-May 8, 2025. The results are contrasting: Daily Excelsior adopts nationalist and security-centered frame that highlights military heroism and state intervention whereas Greater Kashmir adopts humanitarian frame that highlights civilian victimization, emotional appeal and community healing. Such competing frames not only affect the perception of the population, but also the discourses of security planning, tourism recovery, and regional development. The study suggests the significance of the media discourses as a dynamic element of the process of defining policy directions and planning outcomes in conflict-sensitive environments. 2025, Green Publication. All rights reserved. -
Midwifery, Witchcraft, And Forensic Blindness: A Gendered Bioanalytical Inquiry Into Historical Practices
In Europe, going on witch hunts was a prevalent activity between the fourteenth and seventeenth centuries. Tens of thousands of people were tortured, executed, and burned in Germany, Italy, and eventually expanded to France and England; women made up three-quarters of the victims. The discovery of "the persecution of midwives as witches," which expanded throughout the Middle Ages and has drawn harsh criticism from historians of the 20th century, is one of the most startling disclosures. This paper examines the intersection between midwifery and witchcraft in Early Modern times, focusing on the risks that female midwives had to deal with in the field of work. Women who practiced the crucial profession of midwifery had their experiences impacted by the historical backdrop of extensive witch hunts and prosecutions. The paper seeks to offer a thorough understanding of the difficulties faced by midwives in balancing their vital role in birthing with the widespread suspicions of witchcraft by looking at the socioeconomic, cultural, and legal aspects. The paper also relies on the recent published work of Dr. Philippa Carter (2023). 2025, Green Publication. All rights reserved. -
Cognitive Behavioral Therapy for Tinnitus: An Applied Bioanalytical Perspective
Tinnitus affects 1015% of adults worldwide, with 23% experiencing severe and chronic symptoms that impair quality of life. Conventional treatments often provide limited long-term relief, while Cognitive Behavioral Therapy (CBT) has emerged as an evidence-based intervention. The present study evaluated the effectiveness of CBT for tinnitus through an integrated psychometric and bioanalytical approach. A total of 100 participants were randomized into a CBT group (n = 50) and a waitlist control group (n = 50). The CBT program consisted of 12 weekly sessions delivered over 12 weeks. Psychometric assessments included the Tinnitus Handicap Inventory (THI), Hospital Anxiety and Depression Scale (HADS), Pittsburgh Sleep Quality Index (PSQI), and WHOQOL-BREF. Bioanalytical measures comprised functional MRI, EEG, salivary cortisol, heart rate variability (HRV), and inflammatory cytokines (IL-6, TNF-?). The CBT group achieved a 45% reduction in THI scores (58.4 9.2 to 32.1 8.5), while the control group showed only a 5% change. Anxiety and depression scores decreased by 42% and 40%, respectively, and sleep quality improved by 38%. Morning cortisol increased by 48%, HRV improved by 38%, and inflammatory cytokines decreased significantly (IL-6: ?26%; TNF-?: ?19%). Neuroimaging and EEG findings confirmed reduced auditory cortex hyperactivity and abnormal connectivity. These results demonstrate that CBT not only alleviates tinnitus distress but also induces measurable neurophysiological and systemic changes, reinforcing its role as a cornerstone of evidence-based tinnitus management. 2025, Green Publication. All rights reserved. -
An Analysis of the Influence of Artificial Intelligence on Human Behaviour and Well-Being
Artificial intelligence is a phenomenon that has transformed the society and tends to have a strong impact on the human behavior and well-being. This is an empirical study which looks at and gauges on the various effects of AI on behavior and well being. It considers the interaction between AI and humanity in different walks through the wide literature review and a high number of empirical data collection. The research analyzes how personalization, recommendation systems and AI-led content curation change decision-making and interconnections of individuals. It also looks in to how AI is influencing health care, educational and mental wellbeing, ethical effects of AI e.g. infringing on privacy, biases in the algorithms and psychological effects of AI-based social media networks. The article gauges the influence of AI in employment systems and economic patterns, shedding light on the prospect of the workforce in possibilities and threats. It also speaks of how AI makes healthcare, education, and convenience improved. The research will be oriented to understanding how AI can influence the human behavior and well-being with the help of the comprehensive statistical processing of the research and data-driven analysis. The findings ought to assist students, professionals, and the society to ethically and safely negotiate AI technologies and concentrate on the necessity to create a measured strategy to utilize the advantages of AI and minimize the harm that could be inflicted on a person and society. Finding and reviewing the factors that influence the human behavior and well-being due to AI is the primary goal of the study. 2025, Green Publication. All rights reserved. -
Ensuring Organizational Sustainability through HR Practices: Moderating Role of Leadership in the Banking Industry in the Context of SDGs
The study inspects the moderating role of leadership in the association between human resource (HR) practices and organizational sustainability, with a particular focus on Sustainable Development Goals (SDGs) 8 (Decent Work and Economic Growth) and 12 (Responsible Consumption and Production). It explores how leadership behaviors shape the effectiveness of HR practices in driving sustainability across economic, environmental, and social dimensions, while also situating these outcomes within the broader context of regional development and spatial planning. By analyzing the role of banks as institutional actors, the research highlights their contribution to financial inclusion, community well-being, and balanced urbanrural growth. A stratified random sample of 500 banking associates from urban, semi-urban, and rural branches was surveyed using a structured questionnaire, and data were analyzed through Structural Equation Modeling with SPSS and AMOS. HR practices, including recruitment, onboarding, performance management, compensation, and employee engagement, were assessed alongside leadership behaviors such as decision-making, resource allocation, empowerment, and vision. The findings indicate that leadership has a significant impact on the positive effects of HR practices on sustainability outcomes. In particular, leading by example and effective resource allocation emerge as strong moderators that advance SDG 8 and SDG 12. The findings underscore that sustainable HR leadership integration in banking not only improves organizational outcomes but also contributes to regional development and planning agendas by reinforcing equitable growth and sustainability across diverse spatial contexts. This study also situates banking institutions within the field of geography, planning, and development by showing how HR-leadership interactions contribute to territorial equity, financial inclusion, and spatial planning objectives. By linking organizational practices to regional sustainability trajectories, the findings highlight banks as critical institutional actors in advancing balanced urbanrural development. 2025, Green Publication. All rights reserved. -
Artificial Intelligence Based Recruitment Prediction and Sentiment Analysis for Enhanced HR Efficiency
In the present era of data-driven organizational environment, the practice of Human Resource Management (HRM) has become increasingly reliant on intelligent Decision-Support Systems (DSS). This study develops a multifaceted two-pipeline model of Predictive Modelling (PM) and Sentiment Analysis (SA) to enhance workforce analytics capabilities. A publicly available HRM analytic dataset is used to train supervised classification models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM), as well as an ensemble model that integrates these classifiers. These approaches use structured data to predict employee attrition based on features such as age, job role, experience, and job satisfaction. The unstructured textual data sources, including resumes and employee reviews, are handled using state-of-the-art Natural Language Processing (NLP) such as tokenization, Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations as Transformers (BERT)-based embeddings. The new Mathematically Modified Robustly Optimized BERT Pretraining (MM-RoBERTa) is proposed for extracting the PM and SA. All the models are evaluated using k-fold Cross-Validation (CV) and standard evaluation measures, namely Accuracy, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), and Mean Absolute Error (MAE). The ensemble model achieves a predictive accuracy of 91.3%, and MM-RoBERTa outperforms existing SA with an accuracy of 93.1 %. The combination of predictive and affective insights is of practical use in fine-tuning talent retention, empowering HRM professionals to make informed decisions based on objective performance indicators and subjective emotional states. 2025 The Authors. -
Significance of Suffering: A Neuroscience Perspective
Pain and suffering are inevitable realities of life. Not only do humans suffer from physical pain but animals too. Recently, the advent of the covid-19 pandemic has led to a global rise in suffering. The significance of physical pain and the emotional dimension of pain is long understood. Here we are trying to understand the significance of suffering pathway in the human brain. The recent advancement in neuroscience related to insights into pain perception, mirror neuron networks, suffering and compassion has created an appeal to revisit the pain and suffering from a contemporary neuroscience perspective. This article analyzes the benefits of suffering from an evolutionary and neuroscientific approach. Suffering affects people differently as some may become more compassionate and/or resilient while others develop depression. Here we are attempting to explain the underlying neural circuitry involved in suffering, empathy and compassion and to point out the interconnectedness among them. Subsequently, the article proposes a neuroscientific perspective to manage the emotional overdrive associated with suffering. 2025, Imprint Academic. All rights reserved. -
An Effective IoT based Vein Recognition Using Convolutional Neural Networks and Soft Computing Techniques for Dorsal Vein Pattern Analysis
In this research, we provide a CNN-based system that can reliably identify the dorsal veins of the hand. In order to get better results on different picture quality datasets, the suggested model makes use of refined variants of the pre-trained VGG Net-16 and VGG Net-19 designs. We use the BOSPHORUS dataset, which provides medium-quality photos, in addition to two self-constructed datasets that provide good-and low-quality images. By using state-of-the-art augmenting image methods, streamlined pre-processing procedures, and meticulously designed CNN designs, the fine-tuned VGG Net-16 model achieves superior performance in comparison to all other models. Using ROI pictures with a resolution of 22424 pixels, a multi-class technique is employed for arranging the vein patterns. Improving data quality during training makes the approach more broad, which helps prevent over fitting. On every dataset, the proposed method achieves better results than standard ML models like K-NN and SVM, and the experimental outcomes demonstrate significant improvements in accuracy. The modifying process led to a considerable decrease in the equal error rates (EER) when compared to benchmark methods. The structure enhances efficiency in computing with GPU-accelerated studying. It was built with the help of Python extensions like as OpenCV, Keras, and TensorFlow. Results from extensive testing of the proposed method show an accuracy of 99.98%, a precision of 98.98%, and a recall of 98.8%. From what we can see, the technique is both adaptable and dependable; making it well suited for use in practical biometrics vein recognition applications. 2025, American Scientific Publishing Group (ASPG). All rights reserved. -
Silenced, Scarred & Shattered: Unmasking the Wounds of Child Sexual Abuse in Select American Memoirs
The research brings to light the marginalized voices of three American women who have written about their sexual abuse in their respective memoirs Roxane Gay, Hunger: A Memoir of my Body (2017), Nikki Dubose, Washed Away: From Darkness to Light (2016) and Neesha Arter Controlled: The worst Night of my Life and its Aftermath (2015). Using these memoirs as primary data and using thematic analysis the study identified three themes which were further classified into different subthemes. Firstly, the research discovered the challenges faced by the survivors in expressing and communicating about sexual abuse due to fear and shame, the survivors do not come forward because of threats, because of rape stereotypes that permeate the society and the fear of what parents and others might think. Secondly, the research explores the various impact of trauma that is caused by sexual abuse which include shame, guilt and self blame, unworthy self, uncontrollable rage, disruption of safety and trust, isolating themselves from everyone, hostility towards body, destructive behaviours which include eating disorder from Anorexia Nervosa to Binge eating disorder, it also includes self harm and substance abuse. Thirdly, the research focuses on the recovery aspect on how the survivors learn to live with the wounds caused by sexual abuse. It focuses on how the survivors came in terms with the abuse, the conflicting feelings of forgiveness and revenge and how they sought redemption through writing their journey. 2025 Sciedu Press. All rights reserved. -
Narrating Trauma as Victims of Human Trafficking in China: A Study on Select North Korean Memoirs
The memoirs titled In Order to Live; A North Korean Girl's Journey, to Freedom and; A Thousand Miles to Freedom: My Escape from North Korea are written by Yeonmi Park and Eunsun Kim two women who managed to escape from North Korea. They went through an experience of being forced into labour in China as victims of trafficking. In their memoirs these authors vividly depict the pain that comes with being exploited. The main aim of this study is to analyse how memoirs can effectively address the issue of trafficking. These remarkable women skilfully use the memoir genre to make a personal plea for action. They strategically make choices appeal to readers emotions openly share their distressing experiences and support their stories with research and evidence that connect their experiences with the broader problem of human trafficking in China. This study clearly shows that both these memoirs emphasize the importance of the memoir genre in advocating for rights. It also highlights how survivor memoirs have the potential to inspire advocacy and involvement, in combating trafficking. 2025 Sciedu Press. All rights reserved. -
Enhancing Glaucoma Detection in Fundus Images: A ResNet based Segmentation and Advanced ML Algorithms with Duck Pack Optimizer
Untreated glaucoma, a chronic eye illness, can cause irreversible vision loss if not caught early. The condition begins with abnormalities in the eye's drainage flow, leading to a rise in intraocular pressure. As the disease progresses, the optic nerve head deteriorates, resulting in vision loss. Ophthalmologists need extensive training and expertise to interpret findings accurately during medical follow-ups to examine the retina. To address this challenge, deep learning-based algorithms have been developed to screen for and diagnose glaucoma using images of the optic nerve, retinal structures, and retinal fundus. This research explores the use of classification and segmentation algorithms based on ResNet to identify glaucoma in fundus images. We fine-tuned the classifier using the DuckPack optimizer and employed XGBoost, LightGBM, and CatBoost algorithms for classification. The results were promising. The segmentation model based on ResNet effectively extracted features, aiding the classification models in accurately identifying glaucoma. All three algorithms performed admirably, though further fine-tuning is needed to determine the best one. Enhancing the model's performance was straightforward after using the DuckPack optimizer for fine-tuning. This study highlights the promising applications of deep learning and sophisticated machine learning algorithms in glaucoma detection. Its findings could inform the development of future diagnostic tools. The Author(s) 2025. -
Implantable Chip Revolutionizing Early-Stage Liver Cancer Detection with Advanced Diagnosis System
Millions of people die from cancer annually. Advanced metastatic cancers may not respond to traditional therapy. The importance for early diagnosis is highlighted by the difficulty of treating cancers in later stages. Enhancing patient outcomes using tissue-engineered cancer diagnosis and therapy is gaining popularity. Cancer and associated immune problems burden healthcare systems, making efficient, high-throughput drug development strategies essential. Thus, implanted chips may solve these issues. A revolutionary technique for early liver cancer identification is the Machine Learning-based Liver Cancer Diagnosis System (ML-LCDS). K-Nearest Neighbour (KNN) identifies liver tumors precisely in ML-LCDS. The performance evaluation reports sensitivity=97.2%, specificity=91.3%, precision=93.5%, FPR=8.7%, and accuracy=94.1%, computed from the confusion matrix derived through 10-fold cross-validation. Experimental findings validate its consistent performance, establishing ML-LCDS as an efficient and reliable diagnostic tool for early-stage liver cancer detection. The Author(s) 2025. The text of this article is open access and licensed under a Creative Commons Attribution 4.0 International License. -
Anti-epileptic medication induced disturbed calcium-vitamin D metabolism: A behavioral analysis using association rule mining technique
BACKGROUND There is a lack of study on vitamin D and calcium levels in epileptic patients receiving therapy, despite the growing recognition of the importance of bone health in individuals with epilepsy. Associations one statistical method for finding correlations between variables in big datasets is called association rule mining (ARM). This technique finds patterns of common items or events in the data set, including associations. Through the analysis of patient data, including demographics, genetic information, and reactions with previous treatments, ARM can identify harmful drug reactions, possible novel combinations of medicines, and trends which connect particular individual features to treatment outcomes. AIM To investigate the evidence on the effects of anti-epileptic drugs (AEDs) on calcium metabolism and supple-menting with vitamin D to help lower the likelihood of bone-related issues using ARM technique. METHODS ARM technique was used to analyze patients behavior on calcium metabolism, vitamin D and anti-epileptic medicines. Epileptic sufferers of both sexes who attended neurological outpatient and in patient department clinics were recruited for the study. There were three patient groups: Group 1 received one AED, group 2 received two AEDs, and group 3 received more than two AEDs. The researchers analyzed the alkaline phosphatase, ionized calcium, total calcium, phosphorus, vitamin D levels, or parathyroid hormone values. RESULTS A total of 150 patients, aged 12 years to 60 years, were studied, with 50 in each group (1, 2, and 3). 60% were men, this gender imbalance may affect the studys findings, as women have different bone metabolism dynamics influenced by hormonal variations, including menopause. The results may not fully capture the distinct effects of AEDs on female patients. A greater equal distribution of women should be the goal of future studies in order to offer a complete comprehension of the metabolic alterations brought on by AEDs. 86 patients had generalized epilepsy, 64 partial. 42% of patients had AEDs for > 5 years. Polytherapy reduced calcium and vitamin D levels compared to mono and dual therapy. Polytherapy elevated alkaline phosphatase and phosphorus levels. CONCLUSION ARM revealed the possible effects of variables like age, gender, and polytherapy on parathyroid hormone levels in individuals taking antiepileptic medication. The Author(s) 2025. -
MALL-Based Writing Instruction: Assessing the Effectiveness of Digital Platforms Among ESL Learners
Nowadays, mobile-assisted language learning (MALL) has emerged as a globally adopted approach that builds on the earlier development of computer-assisted language learning (CALL) by utilizing the accessibility and flexibility of mobile devices to promote independent and self-directed learning. It enables learners to extend lan guage practice beyond classroom boundaries and provides authentic opportunities to engage with English as a Second Language (ESL). This study investigates the potential of digital platforms, specifically WordPress and Hem ingwayEditor, in enhancing the writing skills of non-native English learners within a MALL framework. WordPress offers a collaborative digital space where learners can publish, share, and receive feedback on their writing, while Hemingway Editor provides real-time analytical feedback to improve readability, grammar, and stylistic accuracy. The research adopted a quantitative design with both control and experimental groups to examine the effective ness of these platforms. Participants included ESL learners who engaged in structured writing tasks, with their progress assessed through pre- and post-tests. The findings of the study reveal that learners using WordPress and Hemingway Editor demonstrated notable improvements in writing performance when compared to the control group. The integration of these tools not only improved grammatical accuracy and stylistic clarity but also encour aged active participation, reflection, and learner autonomy. The results emphasize the pedagogical value of incor porating MALL strategies into language instruction, particularly for developing essential writing skills among ESL learners. In conclusion, this research affirms that mobile technologies, when strategically integrated into teaching, significantly enhance learning outcomes and offer sustainable pathways for improving ESL writing proficiency. 2025, Digital Technologies Research and Applications. All rights reserved. -
A Hybrid Deep-ensemble Decision-Support Framework for Reliable Early Breast Cancer Detection: A Cross-validated Outcome Analysis
OBJECTIVE The necessity to diagnose breast cancer early and correctly is the need to minimize the diagnostic uncertainty and unwarranted clinical procedures. This paper assesses the reliability of a hybrid deep-ensemble decision-support model in terms of diagnostic reliability, stability of outcome, and translational feasibility of the model via structured clinical data to detect early breast cancer. METHODS The Wisconsin Diagnostic Breast Cancer dataset which consisted of 569 cases of benign and malignant tumors was analyzed retrospectively. The framework proposed combines the deep learning of latent representations with stacked classification, ensemble-based feature selection, and stacked classification. Performance evaluation was performed based on sensitivity, specificity, accuracy, F1-score, and area under the curve (AUC) performed using stratified 10-fold cross-validation. The statistical stability across folds and the comparison with baseline models were determined with the help of non-parametric tests (p<0.05). RESULTS The model had good diagnostic performance with an accuracy of between 91.2-100 (Mean 96), Sensitivity of 76.2-100, good specificity value, and AUC 0.973-1.000. Variability in performance between folds was low, and statistically significant enhancement as compared to baseline classifiers were present. CONCLUSION The hybrid deep-ensemble model is highly diagnostic, has robust discriminative ability, and ultimately remains stable, which demonstrates the methodological robustness and diagnostic reliability of the proposed framework as a proof-of-concept decision-support model for early breast cancer detection, with potential translational relevance subject to further external clinical validation. 2026, Turkish Society for Radiation Oncology. -
Data privacy in blockchain management scheme with Nudge Theory for banking sector
Blockchain is an emerging digital transformation technique for processing and storing information. The study explores how blockchain technology can transform the banking sector by improving efficiency, transparency, and security. The main goal is to understand how blockchain can modernize traditional banking operations and address key challenges such as fraud, high transaction costs, and slow processing times. The study uses a qualitative approach, drawing insights from existing research, real-world examples, and current trends in financial technology. Findings show that blockchain offers clear advantages, including faster and more secure transactions, reduced operational costs, and improved record-keeping. It holds strong potential in areas like payments, trade finance, and compliance. However, the paper also highlights significant obstacles such as unclear regulations, difficulties in integrating with existing systems, and technical limitations related to scalability and interoperability. Blockchain is seen as a promising solution for many of the inefficiencies in current banking practices. Still, successful implementation will require careful planning, regulatory support, and collaboration across the financial ecosystem. The study offers practical insights for banks, technology developers, and regulators, recommending a gradual and strategic approach to blockchain adoption to ensure long-term value and sustainability. 2025 by the authors; licensee Learning Gate.