Metallurgical and Materials Engineering (E-Journal)
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Understanding Deepfake Technology In The Age Of Artificial Intelligence
Get PDFEmerging technologies like Big Data, Artificial Intelligence, Data Analytics, Machine Learning, Artificial Neural Networks, and Deep learning are the vast point of apprehension for technocrats these days. These technologies are required to cope up with the challenges of the data generated by this generation. Deep fake is one such technology that is a combination of deep learning and synthetic media. Deep fakes are hyper-realistic videos digitally controlled to illustrate people saying and doing things that never actually happened. The modus operandi involves footage of two people into a deep learning algorithm to train it to swap faces. In other words, deep fakes use Facial Mapping Technology and Artificial Intelligence that swap the face of a source person with the target person on a video. In the past few years, Deep fake has become a problem that is a threat to public discourse, human society, and democracy. False information flows quickly through social media, where it can hit millions of users. This paper gives an insight into Deep Fake technology to evade its use to spread misinformation, damage reputations, and harm individuals
Computational Approaches to Predict the Behavior of Advanced Composites under Stress
Get PDFThe comprehension of material stress responses alongside behavior forecasting remains essential for all three fields including aerospace travel and automotive production and building structures. The paper investigates different computational methods which predict advanced composite stress behavior. Besides the review it provides an outline of future research paths and major barriers in this field
The Role of Chatbots in Customer Service: Examining Language Use and Its Impact on Customer Satisfaction
Get PDFIn the digital age, chatbots have become a pivotal tool in customer service, offering businesses the ability to provide 24/7 support and enhance customer interactions. This article explores the impact of chatbot communication styles on customer satisfaction, trust, and engagement, particularly in the context of service failures. Drawing on recent research, we examine how task-oriented and social-oriented communication styles influence consumer perceptions and behaviors. We also investigate the role of expectancy violations and mind perception theory in shaping these interactions. Our findings suggest that social-oriented chatbots, which exhibit empathy and warmth, significantly enhance customer satisfaction and trust, especially in high-expectancy violation scenarios. This study provides valuable insights for businesses aiming to optimize chatbot interactions and improve customer service experiences
Effect of Yoga Intervention on Polycystic Ovarian Disease - Bibliometric Analysis
Get PDFPolycystic Ovarian Disease (PCOD) is an endocrine pathology prevalent among women of reproductive age, distinguished by menstrual irregularities, hyperandrogenic manifestations, and the presence of polycystic ovarian morphology. Non-pharmacological interventions, such as yoga, are increasingly explored for managing PCOD symptoms. This study evaluates the research trajectory on yoga interventions for PCOD using bibliometric data analysis. A bibliometric analysis was conducted using the SCOPUS database. Stringent inclusion criteria were applied, focusing on studies centered on yoga interventions for PCOD, published in English, and accessible through Open Access journals. Out of the initial pool of 26 journals, 17 articles were closely examined, resulting in the final selection of 6 articles for in-depth analysis. The analysis revealed a significant rise in publications on yoga for PCOD, highlighting its growing recognition as a non-pharmacological treatment. These findings emphasize the need for interdisciplinary collaboration and further evidence-based studies to confirm yoga's effectiveness in managing PCOD, suggesting its potential role in comprehensive care for women with PCOD
Effect Of Marl Soil Specification On Seismic Response Of Tall Building Under Near And Far Field Earthquakes
Get PDFMarls are challenging soil types that pose a threat to the safety of construction projects. This type of soil tends to have reduced resistance and increased deformation as humidity levels rise. During an earthquake, the properties and characteristics of the soil change when it shakes. A study was conducted to assess the interaction between soil and structures during both near and far earthquakes on marls. The research focused on marly soils in the northwest region of Shiraz City. The findings indicate that for near-field earthquakes, the structural response values in non-marl regions are significantly lower compared to marly sections. Additionally, the response of drift, bending moment, and base shear for a specific cross-section is greater in high-rise buildings (e.g., 12-story buildings) compared to shorter ones (e.g., 6-story buildings). In marl sections, an increase in the thickness of marl layers leads to a higher building response. The highest response rate is observed when the thickness of the marl layer exceeds 5 meters, while the lowest structural response occurs when the thickness is 5 meters or less. Similar conditions apply for far-field earthquakes, with the distinction that the damping and frequency response of a 12-story building are greater than those of a 6-story building. This indicates that high-rise buildings are more affected by far-field earthquakes with longer periodicity
Design And Implementation Of Dual Input Single Output Converter For Real World Applications
Get PDFWith the adding integration of renewable energy sources such as solar, hydro, wind for household energy systems, the need for effective power operation results has come pivotal. In addition to this, battery is used as energy storehouse system to give provisory power when oscillations happen due to renewable sources. This integration helps to manage inflow of power supply without disturbances in real world operations. The present system concentrates on Dual Input Single Output DC- DC converter. It enhances the connection and distribution of energy from binary energy sources. It maximizes the effectiveness while maintaining a steady and reliable energy force through innovative power inflow operation. This work incorporates with basic and improved level of voltage management systems. Initially system is validated for both simulations and hardware with two battery sources of 12 V connected to converters which provides stable output voltage of 12V without fluctuations. Later, the system is improved by considering solar as one input source and battery as another input source. This system showcases effectiveness of dual inputs to converters by providing 150V of AC voltage which suits for real world applications. This work is validated by MATLAB simulations and Hardware implementations, by providing regulated output voltage. It suits for Domestic grid applications when fluctuations occur thorough renewable sources. In order to implement this esp32 Microcontroller and PIC Microcontroller was utilized. The experimental results satisfy with simulation results, verifying the converter’s ability to efficiently manage energy flow while minimizing waste. This DC DC conversion approach provides a cost-effective and scalable solution for integrating renewable energy into real world applications. By smartly balancing energy sources, the Dual Input Single Output converter enhances overall efficiency and sustainability. Further, contributes to the higher aim of reducing dependency on nonrenewable fossil fuel based power generation and distribution
Handling Imbalance Noisy Dataset By A Hybrid SMOTE-LOF-Transforms Model
Get PDFIn In this study, we aim to evaluate the effectiveness of various machine learning models for fake news detection on PolitiFact and GossipCop datasets obtained from FakeNewsNet, focusing on identifying the most accurate and reliable model. We focused on using state-of-the-art methods combining deep learning transformers and SMOTE resampling techniques for class imbalance, LOF as outlying point detection. The proposed method in this paper, which combines the transformer attention mechanism with SMOTE and LOF resampling techniques, achieved the highest performance metrics on both datasets. The novelty of this model lies in its ability to train on a noisy imbalanced dataset in a short period of time while achieving high accuracy. It recorded an accuracy of 91.5% in PolitiFact and 87.2% in GossipCop, outperforming other models in accuracy, recall and F1 scores. Compared to models such as BERT, BERT + LSTM, and SAFE (multi-faceted), the attention mechanism stands out due to its ability to dynamically focus on relevant items. Overall, this work demonstrates the necessity of retraining models to accommodate the distinctive features of different datasets. It also demonstrates the effectiveness of attention mechanisms in understanding complex narratives and highlights the benefits of a multi-faceted perspective. This indicates that for realistic applications, choosing or designing models with high performance on multiple datasets is necessary, which may pave the way for future work on improving fake news detection using advanced NLP techniques and multimodal approaches
Artificial Intelligence and Smart Pedagogy: Machine Learning in Digital Media for Language Education
Get PDFThis qualitative study explores the experiences, perceptions, and pedagogical practices of language teachers integrating AI-powered language learning tools into their classrooms, aiming to examine pedagogical enhancements and challenges, and their impact on teacher-student interactions and student learning outcomes. The study addresses a significant gap in the literature by investigating the complexities of AI-powered language learning tool integration in real-world classrooms. Utilizing thematic analysis and the Cognitive-Media-Machine Framework as its theoretical foundation, which provides a holistic understanding of the interplay between cognitive processes, media, and machine learning algorithms, the study collects data from 100 language teachers using AI-powered tools in different universities through interview and observation protocols. The findings reveal three nested themes: Pedagogical Enhancements, Technological Challenges, and Student-Centered Learning, highlighting the potential of AI-powered language learning tools to provide personalized feedback and assessment, while underscoring the need for addressing technological challenges and promoting student-centered learning experiences. The study contributes to the existing literature by providing insights into the practical applications and limitations of AI-powered language learning tools, informing language education policy, practice, and research, and highlighting the need for ongoing professional development, technical support, and pedagogical innovation. The research employs a qualitative approach, utilizing thematic analysis to identify emerging themes, and the Cognitive-Media-Machine Framework, which encompasses three key aspects: cognitive processes, media, and machine learning algorithms. The study's key findings emphasize the potential of AI-powered language learning tools to enhance language learning outcomes, while also posing significant technological challenges, and highlighting the importance of promoting student-centered learning experiences. Ultimately, the study addresses a significant gap in the literature, providing valuable insights for language education stakeholders
University-Industry Partnership for Sustainable Development: A Strategic Approach to Educational Management Practices in STEM Disciplines
Get PDFThis study examines the role of strategic educational management in sustaining university-industry partnerships in Science, Technology, Engineering, and Mathematics (STEM) disciplines. A descriptive survey design was adopted, with the Faculty of Education, University of Nigeria, Nsukka, as the study area. The population comprised 298 academic staff, and since the population was manageable, no sampling was conducted. Four research questions were formulated to guide the study. The Strategic Educational Management and University-Industry Partnership Questionnaire (SEMUIPQ) were developed as the instrument for data collection. The instrument was face-validated by three experts, two from the Educational Administration and Planning Unit, Department of Educational Foundations, and one from the Research, Measurement, and Evaluation Unit, Department of Science Education, all in the Faculty of Education, University of Nigeria, Nsukka, Enugu State, Nigeria. Reliability was established using Cronbach’s alpha method, yielding a coefficient of 0.85. Data were collected through direct administration of the questionnaire, and the decision rule was set at a mean benchmark of 2.50. Mean and Standard Deviation was used to answered the research questions. The findings of the study revealed that strategic educational management enhances university-industry partnerships by aligning university curricula with industry demands and promoting research commercialization. Additionally, inadequate policy frameworks and weak institutional support hinder effective collaboration. Based on the findings of the study, it was recommended among others that universities should develop and implement dynamic policies to strengthen sustainable industry engagement. This study contributes to knowledge by emphasizing the critical role of strategic educational management in fostering long-term university-industry collaboration for the advancement of STEM education
Improving Cattle Transport Safety in India: A CFD-Based Analysis of Cage Structures
Get PDFThe transportation of cattle in India predominantly relies on conventional goods carrier vehicles, which are not optimized for animal welfare. This inadequacy often leads to significant stress, injuries, and even fatalities among livestock. To address this issue, our research introduces a flexible, modular cage design tailored for various types of goods carrier vehicles, aimed at improving the safety and comfort of cattle during transport. The study employs Computational Fluid Dynamics (CFD) to analyze airflow, temperature distribution, and pressure dynamics within different cage designs, focusing on ventilation efficiency and thermal comfort. Three distinct vent configurations were examined: (1) Side openings, (2) Front and rear openings with vertical side vents, and (3) Front and rear openings with cross-linked side vents. CFD simulations revealed that the third design, featuring cross-linked vents, offered superior airflow patterns and reduced thermal stress, ensuring a more stable environment for the livestock. This configuration demonstrated a 60% improvement in ventilation efficiency and a significant reduction in temperature variation compared to conventional designs. The findings suggest that incorporating optimized vent designs and flexible cage structures can significantly enhance animal welfare during transportation. This research highlights the potential of CFD as a powerful tool in designing safer and more effective cattle transport solutions, tailored to the unique requirements of India’s transport sector. Future work will focus on field validation and further refinement of the proposed design for widespread adoption