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Predictive Modeling of Student Learning Outcomes Through Cognitive and Emotional Skill Integration
The interplay of factors, including both cognitive and non-cognitive, plays a significant role in the learning patterns of students. However, the majority of the research conducted on such issues mainly puts forward the role of cognitive skills but forgets that a very important role is played by the non-cognitive factor, specifically motivation and emotional intelligence. Therefore, this study focuses on bridging that gap by investigating the combined influence of cognitive and non-cognitive factors on the learning capacities of engineering students during their transition to higher education. A two-year longitudinal study on engineering students of AITAM, Tekele, India was considered in relation to their academic performance, learning preference, and socio-emotional aspects. The approach adopted makes use of predictive analytics. It is deployed here as machine learning algorithms in the form of Logistic Regression (LR), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), and Support Vector Machines (SVM) to classify the learners into very fast, fast, average, and slow learners. The algorithm of k-NN also achieved the highest accuracy classification and showed good robustness for learning the students' learning rates. This study underscores the combination of new teaching approaches as well as personalized self-learning methods to enhance learning performance, especially for slow learners. Indeed, the outcome gives avenues for much more extensive studies done on large datasets using advanced algorithms which can be applied across a range of educational fields to support tailored learning interventions. 2025, Iquz Galaxy Publisher. All rights reserved. -
Kashmir and Conflict: Objectivity and Balance in News Sourcing
Journalistic balance and objectivity have been critical concepts of scholarly debate. While balance traditionally meant giving equal space to opposing views, newer models of impartiality aim to represent a broader range of perspectives. Using a quantitative content analysis, this chapter analyses news published in the two leading English dailies, Rising Kashmir from Kashmir and Daily Excelsior from Jammu of the Indian Union Territory of Jammu and Kashmir from 1 to 31 October 2022. As many as 62 newspaper editions comprising 987 pages of broadsheets are examined and conflict-related news articles are sampled for analysis. A manual analysis is used to analyse the conflict news articles and identify the sources quoted in them. Study findings indicate the dominance of elite political sources in the news reports. Drawing from seminal studies in journalistic sociology, such as Gans Deciding Whats News, this chapter discusses the implications of the high prevalence of elite voices, comprising political, social and economic. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
HEART FAILURE DETECTION USING OPTIMIZATION ALGORITHMS
Heart failure (HF) remains a significant global health challenge, requiring early and precise detection to improve clinical outcomes and reduce mortality rates. Traditional diagnostic approaches often fail to capture the complexity of HF pathophysiology, necessitating advanced computational methods for accurate prediction. In this study, we propose a novel optimized Stacked Support Vector Machine (S-SVM) framework, integrating multiple SVM classifiers with diverse kernel functions to enhance predictive accuracy. A genetic algorithm (GA) is employed to fine-tune hyperparameters, ensuring model robustness and generalizability across patient populations. The model is rigorously evaluated on the UCI Heart Failure Clinical Records Dataset and the Framingham Heart Study Dataset, demonstrating superior performance in accuracy (95.7%), precision (0.90), recall (0.87), and AUC (0.96) compared to conventional machine learning techniques. The proposed system effectively balances computational efficiency with clinical interpretability, making it a promising tool for early-stage HF detection and risk stratification. This research advances the intersection of machine learning and cardiovascular diagnostics, offering a scalable and adaptive solution for real-world healthcare applications. Little Lion Scientific. -
Psychology Teaching and Learning: Innovations, Trends, and Best Practices
Deliver effective psychology education with proven strategies for diverse learners As psychology education evolves amid global shifts, educators need practical, evidence-based strategies that work across contexts. Psychology Teaching and Learning: Innovations, Trends, and Best Practices brings together international experts from twelve countries to address these challenges. Editors Aneesh Kumar and Rituparna Chakraborty have assembled leading voices in psychology education to provide actionable guidance for enhancing teaching effectiveness while promoting equity and inclusion. This volume explores the International Competencies for Undergraduate Psychology Model, culturally responsive pedagogy, online learning strategies, and innovative assessment approaches. Discover how to integrate artificial intelligence and cognitive science into teaching, implement open science pedagogy, and apply the megastudy method. The book addresses student well-being through emotional intelligence, resilience, self-compassion, and creating safe learning environments. Readers will discover: Evidence-based teaching strategies and assessment models proven to enhance effectiveness across diverse learning environments and cultural contexts worldwide International perspectives from psychology educators in twelve countries offering varied approaches to common challenges in contemporary psychology education Practical guidance on integrating emerging technologies like AI and intelligent tutoring systems into psychology teaching and learning experiences Frameworks for promoting student well-being including emotional intelligence development, resilience training, self-compassion, and creating psychologically safe classrooms Reflection questions and supplementary classroom resources in each chapter to facilitate immediate application and adaptation to your teaching context Whether you teach undergraduate courses, develop curricula, or train future educators, this book equips you with forward-thinking approaches to prepare students for real-world challenges. By bridging contemporary educational challenges with actionable strategies, it empowers you to transform your teaching practices. 2026 by John Wiley & Sons, Ltd. All rights reserved. -
Viscosity dissipation and BrinkmanBard convection with thermal anisotropy: stability studies in both linear and nonlinear
This study presents both linear and nonlinear stability analyses of BrinkmanBard convection in a porous medium, considering the effects of thermal anisotropy. The flow occurs between two walls maintained at uniform but different temperatures. The critical Rayleigh number is examined, including variations in the Darcy number, porosity, Prandtl number, and anisotropic thermal conductivity, with both linear and nonlinear stability regimes analyzed. Contour plots of streamlines and isotherms are provided to visualize fluid and heat flow directions. The results demonstrate that the presence of the porous medium inhibits convection and reduces the cell size at the onset of instability. Additionally, thermal anisotropy stabilizes the system, with the region of subcritical instability shrinking as the anisotropy parameter increases. While the linear stability analysis does not reveal any significant impact of viscous dissipation, the nonlinear stability analysis shows that viscous dissipation destabilizes the system. These findings contribute to a deeper understanding of the interplay between thermal anisotropy, porosity, and convection behavior in porous media, with implications for various engineering and geophysical applications. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
The talent compass: guiding workforce realignment at Vertex Engineering
Learning outcomes After discussing this case, students will be able to apply the Competency Mapping framework to design redeployment and reskilling strategies for displaced employees; analyze workforce realignment challenges through the lens of Strategic Workforce Planning (SWP) models; evaluate HRs strategic role in balancing financial constraints with talent retention using evidence-based reasoning; assess the risks and outcomes of alternative workforce planning models and propose suitable HR interventions; and design an integrated competency assessment and performance evaluation framework to prepare the organization for future transitions. Case overview/synopsis In early 2023, Suresh Nair, Deputy General Manager of Human Resources at Vertex Engineering, a medium-sized firm in Indias construction equipment sector, faced a major workforce challenge. The company decided to sell its Core Manufacturing Plant, whose capacity utilization had fallen from 85% in 2010 to 50% in 2023. Out of 500 affected employees, 250 were transferred to the acquiring company, while the remaining 250 required redeployment, reskilling or release. This case study explores how Vertex can strategically realign its workforce through the application of Strategic Workforce Planning (SWP) and Competency Mapping frameworks. It emphasizes the HR functions evolving role from administrative redeployment to strategic capability building within an environment of technological change, cost pressure and labor volatility. The protagonist, Suresh Nair, must navigate conflicting priorities between cost reduction advocated by the CFO and the COOs emphasis on talent retention as a long-term competitive asset. Complexity academic level Graduate and under-graduate level Supplementary material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management. 2026 Emerald Publishing Limited -
The Smart Detection of Ovarian Cancer in Complex Medical Images Using Deep Learning
Ovarian cancer is a challenging disease to detect and diagnose, especially in complex medical images where the cancerous lesions may be small and difficult to differentiate from surrounding healthy tissue. The use of deep learning algorithms has shown promising results in computer-aided diagnosis of various cancers. This study aims to develop a smart detection system for ovarian cancer in complex medical images using deep learning techniques. The proposed system will have the ability to accurately and efficiently identify cancerous lesions, leading to earlier detection and improved treatment outcomes. Through the use of advanced computer vision and machine learning methods, the system will be able to learn from a large dataset of medical images and make accurate predictions. This research has the potential to significantly improve the diagnosis and treatment of ovarian cancer, ultimately saving lives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Political propaganda portrayed in Hindi films /
Politics being one of the central object in democratic and highly politicize country like India has its impact and influence on every walk of life of this country. Since the beginning of the 20th century cinema in many ways has influenced social consciousness and helped in shaping our inner understanding. Both cinema & politics become important aspects of our society. It is important to study how they interact and how cinema adopt or take shape via political discourse of this country. -
Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods
Deepfake detection has become a critical component of digital forensics and security, as manipulated images and videos increasingly threaten trust in visual media. However, existing methods often struggle with robustness under post-processing operations such as JPEG compression, Gaussian blur, scaling, and filtering, and with the growing diversity and realism of face image modification (FIM) forgeries. This work proposes CMNV2, a hybrid architecture that integrates MobileNetV2 with a custom CAFFE block to enhance feature extraction and classification accuracy. By adding five additional layers to a pre-trained structure, the model demonstrates superior resilience against complex real-world conditions and achieves 99.10% accuracy across multiple datasets, outperforming 13 baseline CNN models. The study trained and tested CMNV2 on 5,000 images (real and deepfake faces), using a combination of deep neural networks (DNNs), transfer learning (TL), and deep learning (DL) techniques. Compared to 13 CNN-based architectures, the proposed model achieved superior performance across some important evaluation metrics, including accuracy, precision, recall, F1-score, error rate, and computational efficiency. These results highlight hybrid CMNV2 as a robust and efficient solution for deepfake face detection and classification, with potential applications in security, healthcare, and education. 2025 -
Design of Heart Rate Monitoring System Using Visual Simulation Tool
In this research paper, a wireless heart rate and temperature monitoring system using Packet Tracer is proposed and put into operation. A wearable bracelet with sensors that can track temperature and heart rate in real time is integrated into the system. The patient and the attending physician receive the gathered data wirelessly for in-depth health monitoring and analysis. This system is used to provide a practical and effective way to track vital signs in real-time, especially temperature and heart rate. The system's functionality and performance can be thoroughly evaluated and validated in a controlled environment thanks to this simulation. Additionally, the system includes functions for data analysis and visualization to help medical professionals make well-informed decisions. To increase the accuracy and accessibility of heart rate monitoring, which would eventually enhance patient outcomes and general wellbeing, the proposed initiative aims to address these challenges. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Blockchain Technology in Higher Education: Opportunities, Applications, and Network Security Challenges
Blockchain technology has garnered attention beyond its origins in cryptocurrency, positioning itself as a robust solution to the security and administrative hurdles faced by higher education establishments. This paper delves into how the decentralized nature and unchangeable ledger of blockchain could enhance the management of academic records, bolster data security, and streamline administrative processes in institutions. In the contemporary era of digitization, higher education increasingly depends on digital platforms for record-keeping, offering advantages like accessibility and efficiency, yet also presenting susceptibilities to cyber threats. Conventional centralized databases are prone to tampering, breaches, and unauthorized entry, jeopardizing the confidentiality of student information. Blockchain emerges as a feasible remedy by furnishing cryptographic security that ensures the immutability and openness of data. This research scrutinizes blockchains potential to elevate the security of academic records, diminish fraud, and refine administrative workflows within establishments. It scrutinizes real-life cases and practical applications of blockchain to assess their efficacy in safeguarding student data and upholding academic honesty. Moreover, it tackles the challenges and apprehensions related to implementation that are vital for the successful integration by educational institutions. By addressing these issues, the research aids in elucidating how blockchain could enhance the record-keeping processes in higher education. It showcases blockchains capability to authenticate educational accomplishments adequately, thus safeguarding the reputation of institutions and fostering confidence in academic qualifications. Ultimately, this research pinpoints blockchain as an indispensable technology for modernizing school management and preserving the validity of educational information in an increasingly digital landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Transforming Food Waste Management with Blockchain: A Sustainable, Consensus Driven Framework
Food waste management is a stern issue that is prevalent around the word, affecting multiple countries and cultures. In a technology driven era, blockchain has gained intense attention because of its various beneficial facets. Blockchain is defined as a technology with its features like security, transparency and immutability, where only the authorized members in a network are given access. The decentralized technology enables transparency and traceability which can be used in food supply chain in conjunction with the various consensus mechanisms to validate transactions. This would ensure consistency and reliability, ensuring the stakeholders to track vivid stages such as food production, processing and distribution. Wastage of food is a cosmopolitan issue conducive to social, economic and environmental challenges. Our proposed work provides substantial benefits in a way to tackle inefficiencies in the food supply chain, Blockchain based business process reengineering can further automate these processes. The paper presents five popular consensus mechanisms that can be used for a sustainable food waste management. The work focuses on providing a blockchain based solution that is low powered and scalable, which in turn increases sustainability and reduces global food waste. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Blockchain in Drone Systems: Advancements, Security Implications and Community Acceptance
Use of drones is an indication of urbanization. There are many societal acceptance factors that need to be assessed for urban drones. This work also emphasizes on the future of blockchain as a novel technology in the upcoming decade. Acceptance of this novel technology will substantially increase the effectiveness and efficiency of future delivery options. The studys methodology will be determined after performing a detailed literature survey on the topic of drone and blockchain technology usage acceptance and community engagement. This study provides a comprehensive and detailed analysis about the knowledge of drone technology among the diversified population. The primary goal of the study is to analyse the general acceptance of drones in day-to-day activities. The research also focuses on understanding the need to educate people about drone technology in plausible areas of applications. With the emergence of this technology, it is evident that drone have a great prospect to grow in various sectors and industries. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Epileptic Seizure Detection Contribution in Healthcare Sustainability
This study describes a sustainable EEG data methodology. Classification using Discrete Wavelet Transform (DWT) for feature extraction, with the objective of reducing the computational efforts while keeping accurate neural signal analysis. DWT decomposes the EEG signal into timefrequency specific components which allows extraction of ten key wavelet features, including wavelet energy, entropy, maximal coefficients, zero-crossing counts, and dominant frequency. These features capture essential timefrequency features of EEG signals, providing a comprehensive yet computationally efficient representation. By streamlining feature extraction, this approach reduces data dimensionality and minimizes computational processing time, aligning with sustainable technology objectives. The resulting feature vectors serve as robust inputs for classification models, effectively supporting EEG data interpretation with reduced energy and less resource utilization. This study demonstrates that targeted feature extraction can achieve high classification performance in EEG analysis while adhering to principles of sustainability and resource efficiency. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Developing an Advanced Cybersecurity Framework and Blueprint: A Contemporary Approach to Counter Hacking Through Reverse Engineering Techniques
Recently many of the world's most secure networks have been breached by hackers, resulting in damage, information theft, data corruption, and threats to both national and international security. Protection experts are now doubting the dependability and efficacy of the current protection measures against hacking assaults in light of this dire situation. This research will use a variety of accepted practice models, global standards, information security frameworks, and best practices to achieve this goal of developing a framework and blueprint for a specialized hacking countermeasure. The deliverable outcome is a technical and administrative hacking countermeasure framework and blueprint because the study will concentrate on technological management practices in addition to hacking countermeasure techniques and tools. The framework and the blueprint were validated and authorized, and the effectiveness and reliability of the study deliverable outcome were confirmed using questionnaire and interview surveys, finding that it fully met the established objectives and scope of work. Furthermore, the validation has demonstrated that the introduced solutions for the Defense-in-Breadth and the deception and concealment strategies can further improve the hacker countermeasure and the development of SNORT rules, the construction of a prototype, and the execution of live testing with the ultimate goal of closing the security gap created by hacker countermeasures in the present defense-in-depth-based security models. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Establishing the Cornerstones of Ethical AI in Education
Establishing the cornerstones of ethical AI in education is essential for fostering an inclusive and equitable learning environment. Ethical AI can enhance personalization, improve access to resources, and empower educators with data-driven insights. Key principles include transparency, accountability, fairness, and privacy. Transparency ensures that AI algorithms are understandable and their decisions are explainable, allowing educators and students to trust the technology. Accountability involves clear guidelines on who is responsible for AI's actions and decisions. Fairness seeks to prevent biases that can adversely affect marginalized groups, ensuring equal opportunities for all students. Privacy is crucial to protect sensitive data and safeguard students' rights. Educators, policymakers, and technologists must collaborate to establish frameworks that prioritize these ethical foundations, promoting responsible AI integration. By embedding these cornerstones into the educational system, we can harness AI's potential while safeguarding the rights and dignity of all learners. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
The Influence of Leadership on Organizational Resilience
Leadership plays a crucial role in shaping organizational resilience, influencing how a company responds to challenges and changes. Leaders set the tone for organizational culture, instilling values that promote adaptability, innovation, and collaboration. When leaders demonstrate a clear vision, they empower employees to embrace uncertainty and find solutions, fostering an environment where resilience thrives. Effective leaders communicate openly and inspire trust, which is essential for navigating crises. They create a supportive atmosphere that encourages risktaking and learning from failures. Additionally, leaders who prioritize professional development equip their workforce with the skills necessary to adapt to evolving market conditions. Strategic decision-making, anchored in a deep understanding of both internal and external environments, enhances resilience. Ultimately, the influence of leadership on organizational resilience is profound, as strong leaders cultivate a culture that not only withstands pressures but also flourishes in the face of challenges. 2026 by IGI Global Scientific Publishing. -
An overview of AI applications in wildlife conservation
The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection and preservation of biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing habitat health and tracking changes over time. AI- driven image recognition tools enable the identification of individual animals from camera trap photos, facilitating more accurate population estimates and behavioral studies. Moreover, predictive analytics play a crucial role in forecasting human- wildlife conflicts and informing proactive management strategies. This synthesis of AI technologies demonstrates their potential to enhance conservation efforts, optimize resource allocation, and ultimately foster more effective wildlife protection initiatives. The ongoing advancement of AI in this field promises to create innovative solutions to some of the most pressing challenges. 2025, IGI Global Scientific Publishing. All rights reserved. -
Implementation of Recent Advancements in Cyber Security Practices and Laws in India
In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. This essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. The community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.