Metallurgical and Materials Engineering (E-Journal)
Not a member yet
915 research outputs found
Sort by
Cloud Based Face Recognition and Augmented Display of Google Glass using Hadoop
Get PDFFace recognition applications can benefit from the cloud computing as they become widely available and easy to acquire today. There are numerous applications of face recognition in terms of security, assistance, guidance and so on. By performing the face recognition on cloud, we can greatly reduce the processing time and clients will not have to store the big data for the image verification on their local machine (cell phones, pc's etc). Cloud computing increases the processing power and storage with very less cost comparing to the cost of acquiring an equally strong server machine. In this research the plan is to enhance the user experience of augmented display wearing google glass, and for doing that, this system is being proposed in which a person wearing google glass will send an image of a person to cloud server powered by Hadoop (open-source software for reliable, scalable, distributed computing) cloud server will recognize the face from the database already present on server and then response to client device (google glass). Then google glass will display the face details in a form of augmented display to the person wearing them. By moving the face recognition process on cloud, the device will require less processing power, and by having the database on cloud server, multiple clients will no longer require to maintain their local database
A Pilot Study Evaluating the Effectiveness of Metaverse-Based Psychiatric Diagnosis: A Comparative Study with Traditional Clinic Settings
Get PDFOver the past few decades, healthcare organizations worldwide have recognized the numerous ways information technologies might prove helpful. Metaverse, AI, and data science are three emerging technologies that are changing the smart health industry. Metaverse is a new advancement in smart health. It represents an integration of three main technologies: artificial intelligence (AI), augmented reality (AR), and virtual reality (VR). It opens new opportunities and options that are still being discovered. Artificial intelligence, in conjunction with machine learning and the metaverse, is revolutionizing the healthcare sector. As they enhance operational efficiency, they elevate patient care, reduce expenses, and decrease workloads for healthcare professionals. Mental health is among the different fields of healthcare that have already started to use these advanced technologies. This research is a pilot study aimed at evaluating the feasibility of using a metaverse-based environment for psychiatric diagnosis, specifically for assessing anxiety and depression. It is an initial exploration of a small sample size of four participants. This study used a within-subjects design, where each participant experienced two sessions: one in the pre-designed metaverse environment (MetaverseClinic) and the other in a traditional in-person clinic. Later participants asked to rate their experience using the STAR-P (Patient version) and the Satisfaction Scale questionnaires. They were selected to measure the therapeutic relationship, patient satisfaction, and the overall experience in both settings. The results show promising similarities in patient satisfaction and therapeutic relationships between the two settings
Ethical Issues And Scientific Validity In The Use Of Narcoanalysis In Criminal Investigations
Get PDFCriminal investigations are increasingly relying on scientific methods such as polygraph tests, narcoanalysis, DNA profiling, and forensic foot printing, among others. These techniques have contributed significantly to advancing the accuracy and efficiency of criminal investigations. Among these, narcoanalysis has gained particular prominence in certain jurisdictions, including India, as a potential tool for extracting information from suspects. This method involves the administration of certain chemicals (such as sodium pentothal) to induce a state of sedated or altered consciousness, under the assumption that the subject may disclose truthful information during this state. Despite its widespread use, narcoanalysis is not without controversy. This paper examines the scientific foundations of narcoanalysis, its applications in criminal investigations, and the ethical and legal challenges it raises within the context of the Indian criminal justice system. While proponents argue that narcoanalysis can enhance investigative processes by uncovering vital information, it is also criticized for potentially infringing on constitutional rights, particularly the right against self-incrimination, as guaranteed by Article 20(3) of the Indian Constitution.Narcoanalysis derived evidence's legal admissibility is also up for question, especially in light of its ethical implications and dependability. The criminal justice system must implement stringent precautions when using this technique because of the possibility of abuse and basic rights violations. The study also looks at how to strike a balance between the constitutional safeguards that people have within the criminal justice system and its potential value in improving the truth-discovery process. The narco analysis method and its evidentiary relevance in criminal investigations are the author's main areas of interest. In order to address the investigative value of narcoanalysis as well as the constitutional, ethical, and legal issues, this book will critically analyse the scientific foundations of the practice and offer a thorough analysis of its application in the judicial system
Machine Learning For Early Diabetes Detection And Diagnosis With KNN
Get PDFDiabetes mellitus is a hastily developing international health problem that necessitates early detection and effective management to prevent severe complications. This study leverages machine learning, specifically the K-Nearest Neighbors algorithm, to predict and diagnose diabetes at an early stage. By analyzing a diverse dataset that includes biological, sociological, and clinical features, the studies goals to develop a robust predictive model. The application of KNN, alongside other machine learning techniques, permits for the advent of tools that can assess individual risk, enabling personalized remedy plans and optimizing healthcare management. The findings of this research could appreciably decorate early diabetes detection, leading to better patient outcomes and contributing to the fight against the diabetes epidemic. This study underscores the the capacity of machine gaining knowledge in transforming public health strategies and providing actionable insights for healthcare practitioners
Examining The Role Of Institutional Culture & Power Dynamics In Restrictive Policies And Student Disempowerment In Indian Colleges
Get PDFThis paper looks at how the institute’s internal culture and power structures in Indian colleges often lead to rules and restrictions that limit students’ freedom and voice. In many institutions, strict policies about behavior, dress, movement, or expression are not just about discipline they reflect deeper issues of control, hierarchy, and outdated traditions. By examining student experiences, institutional rules, and real-life cases, this paper appeals that college environments in India often discourage independence and critical thinking. Instead of empowering students, many policies aim to control them.
Indian college students typically witness oppressive and authoritarian systems that constrain expression and discourage empowerment. This research examines how restrictive college policies, lack of faculty support, misuse of authority, and overwhelmed teachers create a culture of silence and fear among students. It also demonstrates the effects of a lack of defined authority and the absence of handling valid student complaints. The research gathers primary data using a questionnaire and examines the wider impact of such settings on student well-being and institutional image. The findings also suggest some reforms in the faculty workload, policy framework, and student support measures
Relationship Between Facebook Use And Desensitization To Ethical Issues: A Study Of University Students In Lahore
Get PDFThis study inquires about Facebook’s role in making university students in Lahore least sensitive over ethical issues like exposure to cyberbullying, fake news, hate speech, privacy violations and academic dishonesty. To achieve this end, a quantitative approach was resorted to, to survey 400 students from 3 universities: University of the Punjab, LUMS and UET Lahore, using stratified random sampling. The study involved data collection with 35‐item Likert‐scale questionnaire using descriptive and inferential statistics. It shows that 68 percent students are using Facebook more than one time a day for 2.8 hours daily. This includes 74% encountering a false story, 56% experiencing cyberbullying, and 48% experiencing hate speech. Indeed, there is a strong negative correlation between Facebook usage and ethical sensitivity, indicating that increase use leads to decrease in the moral responsiveness. Fake news emerges the strongest predictor of desensitization followed by hate speech and cyberbullying. Besides, passive scrolling was more desensitizing than active engagement. The study is consistent with the Desensitization Theory where repeated exposure normalizes unethical behavior. This study contributes to fill the gaps in research on Pakistani students and provides recommendations to educators and policymakers to promote responsible digital citizenship and media literacy
Natural Language Processing for Sentiment Analysis Techniques and Applications
Get PDFSentiment analysis expert systems have gained intensive market attention because of the vast increase in customer-generated online feedback through reviews and social media posts and feedback. The study examines basic methods of sentiment analysis which consist of lexicon-based approaches as well as machine learning and deep learning systems. The paper evaluates practical uses of sentiment analysis along with discussing current field obstacles and prospective research paths for the upcoming years. New NLP technology particularly using transformer models improves sentiment analysis systems while enhancing their precision and speed
Investigating Online Learning Readiness of Indian Undergraduate Students
Get PDFOnline teaching and learning has alternated from being ancillary to primary and back through the course of the last few years. From discussion on the use of technology and online learning being a secondary fall back mechanism before COVID 19 to being the only means of education during the pandemic, the importance and impact of online modes of learning has seen many deliberations. Post pandemic, the status changed with the need for online teaching and learning methodology being established pragmatically. This pragmatic acceptance needs to be further investigated to understand the factors influencing learning and teaching. The current study was conducted to develop an instrument to measure online learning readiness of students with specific focus on learning motivation, computer/internet self-efficacy and self-directed learning variables. 18 items focusing on three competencies, namely motivation for learning, self-directed learning, and computer/internet self-efficacy were included in the initial instrument. EFA (exploratory factor analysis) shows that the instrument has three factor structures of online learning readiness with 63.75% variance in relationship among the items. All factors had high reliability as Cronbach’s α> .823. The final instrument had 16 items as two items which cross loaded on multiple items were not considered for the study. Motivation for learning, self-directed learning and computer/internet self-efficacy had 8 items, 5 items and 3 items respectively. Three of the factor structures of the Online Learning Readiness Scale (OLRS) instrument have been conformed through the current study. Academicians and researchers can employ Online Learning Readiness instrument to get better insight about t learning motivation, computer/internet self-efficacy and self-directed learning competencies of students pursuing undergraduate education
Incurred Changes in Water and Fat-Soluble Vitamin Profile of Alternative Ingredient Spirodela Polyrhiza Based Partial Fishmeal Substituted Aquafeed: Effect of Long-Duration Variable Temperature Storage
Get PDFThe study aims to evaluate effects of storage duration and storage temperature on changes in vitamin profile of formulated aquafeeds.Two aquafeeds are formulated one as extrusion processed fishmeal replacement diet comprising aquatic macrophyte greater duckweed(Spirodela polyrhiza) as D1, other as non-substituted fishmeal diet from pelettization D2. Diets are stored for sixmonths (180days) under four storage temperature conditions; -20o, 4o,ambient and 45oC namely T1,T2,T3 and T4, respectively. Changes in water-soluble and fat-solublevitaminprofile is assessed bimonthly up to sixmonth storage (at 0,60,120,180 days) for both the diets. Both diets at all storage temperatures,show substantial loss in water- miscible andfat-miscible vitamins namely A, E, K, B2, B12, C, 60-day further.Noteworthily in D2, intense diminutions in vitaminDcontent was observable during four month storage at T4 condition, while temperature, duration and interaction effects on vitamin D were significant (P≤0.05)for D1.Thiamin retentions in D2 are influenced by interference from antinutrients; whileco-elutants affect B6 determinations in D2. Evidently, incurred loss of most vitamins were highest at end of storage underT4 conditions.The paper highlights effect of temperature, duration on dietary storage profile of vitamins as essential nutrients,affecting quality and shelf-life of stored processed feeds suggesting at best utilizationregimes during storage
Comparative Analysis of 2D-CAD Comparison with Custom-Trained Computer Vision Models
Get PDFIn manufacturing environments, the ability to efficiently compare and analyze 2D Computer-Aided Design (CAD) drawings is critical for ensuring product quality, minimizing errors, and streamlining the design iteration process. Traditional manual comparison methods are time-intensive and prone to human errors, particularly when analyzing annotations, dimensions, and complex structural details. To address these challenges, this study presents a comparative analysis of state-of-the-art deep learning models—YOLOv8m, YOLOv8x, YOLO-NAS, and Faster R-CNN—for automated CAD design evaluation. The models were trained on a dataset of 5,000 CAD images, encompassing diverse mechanical components with varying complexities. The proposed system leverages object detection and Optical Character Recognition (OCR) techniques to extract and compare dimensions and notes with high precision. Experimental results demonstrate that YOLOv8m outperforms other models in terms of detection accuracy. The findings highlight the effectiveness of deep learning-based CAD comparison systems in reducing verification time and improving design evaluation accuracy. This study provides insights into the strengths and limitations of different computer vision models for CAD analysis, contributing to the advancement of automated design validation in manufacturing industries. This approach enhances the recognition accuracy of deep learning models, making dimension extraction and recognition more practical. The system achieves 90-95% accuracy in detecting dimensions and notes in the CAD designs, and 85-90% accuracy in text recognition. These findings highlight the effectiveness of AI-driven CAD comparison systems in reducing verification time and improving design evaluation accuracy, contributing to the advancement of automated design validation in manufacturing industries