Volume 13 | Issue 4 |

 Abstract

Software bugs are a critical challenge in the development lifecycle, impacting product quality, user satisfaction, and maintenance costs. Traditional bug prediction models often focus on static code metrics or historical bug data, without considering the developer-centric context. This paper proposes a Developer-centered bug prediction model that leverages both code-level features and developer-specific behavioral patterns to enhance prediction accuracy. The model integrates features such as code complexity, change history, developer expertise, and commit frequency, using advanced machine learning techniques like ensemble learning and deep neural networks. We validate our approach on real-world datasets from open-source repositories, demonstrating significant improvements over conventional models in terms of precision, recall, and F1-score. The results suggest that incorporating developer-related factors provides a more nuanced understanding of bug-prone code, enabling more targeted and effective bug management strategies.

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 Keywords

Developer-Centric Analysis, Change Scattering, Code Quality Assessment, Predictive modeling, Logistic Regression, Cross-Validation

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This research examines the convergence of Blue Victorianism, gender dynamics, and water symbolism in two lesser-studied yet thematically significant Victorian novels: The Octoroon (1861) by William Gilmore Simms, also known as Mayne Reid, and The Odd Women (1893) by George Gissing. Engaging with interdisciplinary critical frameworks--including blue Victorian and ecocriticism the study argues that water in these texts functions not merely as a setting but as an active symbolic force that reflects and shapes the conditions of women's lives in Victorian society. The concept of Blue Victorianism, encompassing affective states such as melancholy, nostalgia, and aesthetic estrangement, is employed to examine how fluid spaces such as rivers, oceans, and coastal landscapes become metaphors for female subjectivity, social instability, and resistance to normative gender roles. In The Octoroon, the Mississippi River and the Atlantic Ocean emerge as charged and paradoxical spaces--sites where ideologies of race, gender, and colonialism intersect. The novel's mixed-race heroine, Zoe, is positioned at the confluence of freedom and constraint, where water signifies both escape and fatal entrapment. In contrast, The Odd Women presents water in more domestic and introspective forms: coastal fog, rain, and reflective seascapes that parallel the emotional and social displacement of unmarried, economically precarious women. In both novels, water mediates themes of marginality, transition, and emotional isolation, rendering it central to the construction of gendered space and narrative movement. By offering a comparative reading of these two novels through the lens of water symbolism and Blue Victorian aesthetics, this study contributes to broader scholarly conversations surrounding gendered spatiality, ecofeminist criticism, and Victorian representations of emotional and social transgression. However, it argues that water operates as a transformative and often destabilising narrative force--simultaneously marking boundaries and enabling resistance--within the gendered imaginaries of nineteenth-century fiction.

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 Keywords

Blue Victorianism, gender, water symbolism, Victorian fiction, The Octoroon, The Odd Women, ecocriticism, feminist theory, maritime space.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Abstract: Mathematics achievement is a critical component of academic success, often influenced by multiple factors, including intelligence, gender, and school type. This study examines the relationship between intelligence and mathematics achievement among secondary school students in Lucknow, with a particular focus on how school type (boys-only, girls-only, and co-educational) affects performance differences between male and female students. A quantitative research design was employed, involving a sample of students from six different schools representing different educational environments. Intelligence and mathematics achievement were assessed using standardized tests, and data were analysed through statistics, correlation analysis, and comparative techniques. The results suggest a strong positive correlation between intelligence and mathematics achievement, confirming that students with higher intelligence scores tend to perform better in mathematics. However, intelligence alone was not the sole determinant of success. The results also reveal gender-based differences in mathematics performance, with girls outperforming boys across all intelligence levels. Additionally, students from girls-only and co-educational schools scored higher on average compared to those from boys-only schools, suggesting that a structured and supportive learning environment plays a crucial role in academic performance. The study's findings challenge traditional gender stereotypes and highlight the importance of effective teaching strategies, personalized learning interventions, and gender-sensitive education policies. While intelligence remains a significant factor in mathematics achievement, the study emphasizes the role of external influences such as school environment, motivation, and instructional quality. The research underscores the need for inclusive and equitable educational approaches to improve mathematics learning outcomes for all students.

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Creative Commons Attribution 4.0 and The Open Definition

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Mathematics achievement, Intelligence, Gender differences, Academic performance, Learning environment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Dilli gharane ke Ustad Latif Ahmed Khan Sahab Ka jeewan - parichay

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Dilli gharane ke Ustad Latif Ahmed Khan Sahab Ka jeewan - parichay

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The ancient Indian texts Thirukkural, Vidura Niti, and Arthashastra provide timeless wisdom on leadership, governance, and ethics, shaping principles that remain relevant in modern society. These texts offer diverse yet complementary perspectives on ethical leadership, political strategy, and economic governance, making them invaluable for understanding both historical and contemporary governance models. Thirukkural, authored by Thiruvalluvar, places a strong emphasis on morality, justice, and righteousness in governance. It highlights the importance of a leader's integrity, fairness, and commitment to the well-being of the people. The text argues that a just ruler fosters societal harmony and economic prosperity by upholding ethical principles and making decisions based on dharma (righteousness). Vidura Niti, extracted from the Mahabharata, serves as a guide for ethical rulership, decision-making, and statecraft. Vidura, the wise counselor, advises rulers on the importance of wisdom, humility, and self-discipline in governance. His teachings stress fairness, accountability, and the dangers of greed and arrogance, warning that unethical leadership leads to instability and downfall. Arthashastra, written by Kautilya (Chanakya), provides a pragmatic approach to administration, diplomacy, military strategy, and economic policies. Unlike Thirukkural and Vidura Niti, which focus on moral virtues, Arthashastra emphasizes realpolitik--strategic governance that balances ethics with practical necessity. It offers detailed insights into law enforcement, taxation, wealth management, and national security, making it a comprehensive guide for statecraft. This article explores the relevance of these ancient texts in modern governance and leadership, comparing their principles with contemporary ethical frameworks such as transformational leadership, corporate social responsibility (CSR), and sustainable governance. By evaluating their contributions, this study highlights the enduring impact of Indian knowledge systems on ethical statecraft and policymaking in the 21st century.

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 Keywords

Thirukkural, Vidura Niti, Arthashastra, ethics, governance, leadership, Indian philosophy

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project report entitled to "An Experimental study on partial replacement of cement with granite powder in concrete". The main object of This experimental study is to analyse and investigates the feasibility of utilizing granite powder as a partial replacement for cement in concrete production. With the growing concerns over the environmental impact of cement manufacturing and the need for sustainable construction materials, granite powder--an industrial by-product from the granite cutting and polishing process--presents a promising alternative. The study involved preparing concrete mixes with varying percentages of granite powder (ranging from 0% to 30%) as a partial replacement for cement. Tests were conducted to evaluate the workability, compressive strength, split tensile strength, and durability of the concrete at different curing periods. The results indicate that the inclusion of granite powder enhances the strength properties of concrete up to an optimal replacement level, beyond which the performance slightly declines. Additionally, granite powder contributed to improved sustainability and cost-effectiveness without compromising the structural integrity. This research supports the potential application of granite powder in green concrete technology and promotes the efficient management of industrial waste.

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 Keywords

Granite powder,Cement replacement,Concrete properties,Mechanical proporties.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid advancement of artificial intelligence (AI) has led to the emergence of deepfakes--synthetic media in which a person's likeness is convincingly replaced with another's. While these AI-generated manipulations have potential applications in entertainment and education, they also pose significant risks to privacy, misinformation, and digital security. To combat this growing threat, AI-driven deepfake detection systems have been developed, leveraging machine learning, computer vision, and neural networks. This paper explores the current state of AI technologies in detecting deepfakes, including the use of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. Techniques such as facial landmark analysis, inconsistencies in blinking patterns, and frequency domain artifacts are examined. Moreover, the paper highlights the role of large datasets, adversarial training, and explainable AI in improving detection accuracy. Despite significant progress, challenges remain in generalizing detection models across diverse datasets and evolving deepfake techniques. The ongoing arms race between deepfake generation and detection necessitates continued innovation and collaboration across academia, industry, and policy-making spheres.

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 Keywords

1.Deepfake Detection 2.Artificial Intelligence (AI) 3.Machine Learning 4.Computer Vision 5.Convolutional Neural Networks (CNNs) 6.Recurrent Neural Networks (RNNs) 7.Transformer Models 8.Facial Landmark Analysis 9.Synthetic Media 10.Adversarial Training 11.Digital Forensics 12.Deep Learning 13.Misinformation Detection 14.Video Manipulation 15.Explainable AI (XAI)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Twenty first century is facing many changes in the environmental issues like consequences from pollution, bio waste, e-waste, techno waste etc. which are badly affecting natural resources, negative effect on human life and health and negative effect on society. Green Human Resource Management (Green HRM) combines environmental aspects with HRM policies and practices to facilitates sustainability. Green HRM is the use of HRM policies to promote the sustainable use of resources within the organizations and promotes the cause of environmental sustainability. The objective of this study is to understand the concept of green human resource management and to explore the green human resource management practices of organizations based on the existing literature. The major areas of the study is to find out various the problems faced by organizations in successful implementation of Green HRM Practices and finally, the paper suggests initiatives for Green HRM for Green organizations.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Human Resource Management, Green HRM, Environment Sustainability, Organization,

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Creative Commons Attribution 4.0 and The Open Definition

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In critical situations like battlefields, disaster zones, rural areas, and medical emergencies, doctors and healthcare workers may not always be able to reach patients immediately. This project introduces a remote-controlled health monitoring and medicine delivery system that allows medical personnel to assess a patient's condition and provide necessary aid from a distance. The system uses an ESP32-CAM for live video streaming, enabling health workers to remotely navigate the chassis and collect patients vitals using a web interface. It is equipped with a pulse oximeter and a temperature sensor to measure oxygen levels, heart rate, and body temperature. A machine learning model running on the ESP32 analyzes the collected data and classifies the patient's condition as Good, Bad, or Critical, allowing for faster medical decisions and prioritization of critical cases to alert doctors via telegram. Along with tracking, this system can also deliver essential medicines to patients in remote or high-risk areas. Its lightweight and mobile design ensures easy deployment in hospitals, military camps, and disaster relief operations, making it a valuable tool for telemedicine and emergency healthcare.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Remote Patient Monitoring, ESP32-CAM, Vital Signs, Pulse Oximeter, Temperature Sensor, Machine Learning, Edge Impulse, Telemedicine, Emergency Healthcare, IoT-based Healthcare, Wireless Health Tracking, Telegram Alert System, Embedded Systems, Mobile Robotic Chassis.

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 Abstract

A metallothionein Type II gene was previously isolated from Leucaena leucocephala, a leguminous species and cloned into pET28a(+) vector and moved into E. coli BL21 strain for protein expression. 0.1mM IPTG was used for induction of enhanced expression and total protein content was loaded onto 12% SDS gel. Purification was done using NI-NTA Affinity Column Chromatography and loaded onto 18% SDS gel. In the former, slight increase in band intensity was seen between 5-10 kDa whereas in the purified samples, bands were observed at 7-8 kDa which is congruent to the fact that the predicted molecular weight of the Leucaena MTII protein is 7.6 kDa. Different concentrations of Cu, Cd and As were used to analyze the efficiency of recombinant BL21 cells in combating heavy metal toxicity. OD values taken at intervals of 3, 5 and 7 hours and overnight shows marked decrease in growth rate of control non-recombinant cells whereas growth rate in the recombinant cells are consistently same for all. These findings can lead to the conclusion that heterologous expression of MTII gene from Leucaena in other living systems is highly effective in protection against heavy metal toxicity.

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 Keywords

Metallothionein, heavy metal toxicity, cloning, protein expression.

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Creative Commons Attribution 4.0 and The Open Definition