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A Real-Time Sign Language to Text Conversion System for Enhanced Communication Accessibility
No full textThe research addresses the problem of converting American Sign Language (ASL) finger spelling into text in real-time, enhancing communication for the deaf and hard of hearing. A convolutional neural network (CNN) is utilized to recognize hand gestures from camera images, focusing on the position and orientation of the hand to create accurate training and testing data. The methodology involves filtering hand images, followed by classification to predict the corresponding sign language characters. The calibrated images are then used to train the CNN model. Key findings demonstrate that the proposed system effectively recognizes ASL finger spelling with high accuracy, offering a valuable tool for improving accessibility in communication. These findings suggest significant potential for further applications in real-time sign language interpretation
Harmony in Temporality: A Psychoanalytic, Scientific, and Pedagogical Inquiry into Surah Al-Asr in the Quran
No full textThis essay examines Surah Al-Asr from the Quran, exploring the complex interaction between spiritual teachings, psychoanalytic theory, and contemporary challenges. The study explores the multidimensional concepts of time, loss, faith, righteous deeds, and patience. Faith is presented as a psychological anchor, countering fear—the most primal human emotion—while righteous deeds align with honesty and authenticity, reflecting psychoanalytic practices such as free association. Patience emerges as a virtue vital for navigating intrapsychic conflict, promoting resilience and emotional regulation. The paper also highlights the modern challenge of instant gratification perpetuated by technology and social media. It points out the significance of teaching delayed gratification, especially in education for children, to cultivate self-regulation, resilience, and holistic development. Through these discussions, the essay reveals the enduring relevance of Surah Al-Asr, offering profound insights into the symbiosis between spiritual wisdom and psychological understanding in addressing timeless and contemporary human challenges
An application with meta-methods (MetaRF) based on random forest classifier
No full textMeta classifiers are an area of intense study in the field of machine learning to improve classification performance. On the other hand, Random Forest is an important classifier in terms of providing fast and effective results. In this study, a meta-ensemble classifier called MetaRF based on the Random Forest basic learner is presented to use and combine the advantages of meta classifiers. For experimental results, the Random Forest base learner and eight meta-learners (AdaBoost, MultiBoostAB, Bagging, Stacking, UltraBoost, FeatureselectedClassifier, RandomSubSpace, FilteredClassifier) were used for ensemble classification on five datasets from the UCI Machine Learning Repository. Experimental results are promising in terms of accuracy rates, precision, recall and F-measure values. The method designed in the study is recommended to be used in machine learning studies and meta-classifier applications
The Application of Artificial Intelligence (AI) in Adsorption Process of Heavy Metals: A Systematic Review
No full textThe application of Artificial Intelligence (AI) has shown significant promise in optimizing adsorption processes for heavy metal removal, an essential component of water treatment plant (WTP) operations. This systematic review presents a comprehensive analysis of AI techniques applied to improve adsorption performance, focusing on machine learning (ML) and metaheuristic algorithms. AI models, such as neural networks and support vector machines, have been leveraged to analyze large datasets related to adsorption parameters, enhancing prediction accuracy and optimizing operational efficiency. Additionally, metaheuristic algorithms like Genetic Algorithms and Simulated Annealing contribute to efficient solution exploration, identifying optimal parameter configurations for the adsorption process. The integration of AI enables real-time monitoring, predictive maintenance, and dynamic adjustment of process parameters, thus ensuring the continuous improvement of adsorption efficiency. AI-based approaches also facilitate the identification of key adsorption features, allowing for precise control and improved resource utilization. Moreover, by combining AI with traditional adsorption models, such as Langmuir and Freundlich isotherms, this review explores new methods for improving adsorption kinetics and thermodynamics. The structured implementation of AI is demonstrated as a path forward in achieving sustainable, adaptive, and reliable solutions for water quality control. Future studies should prioritize the development of more advanced AI-driven predictive systems, enhancing the applicability of these methods across different adsorption contexts and pollutant types. This review underscores the transformative potential of AI in advancing adsorption technology, paving the way for smarter water treatment solutions that enhance environmental sustainability
Green Chemistry in the Hydrolysis Process of Cellulose by Acids: Modelling, Optimization, and Life Cycle Assessment
No full textCellulose is a critical material in a variety of engineering applications, holding significant importance in the evolving landscape of future lifestyles. The imperative for its production in the future is underscored by its versatile utility. However, conventional production processes involve the application of numerous chemicals. In this research, MATLAB-based simulation played a pivotal role in modeling the hydrolysis process during cellulose production. We further extended the simulation to optimize the production process. Notably, the application of life cycle assessment (LCA) brought an additional layer of sustainability to the study, resulting in the development of a more environmentally sound and sustainable acidification method for the hydrolysis process. This integrated approach holds promise for fostering a more sustainable and eco-friendly future in cellulose production
Powering Autonomous Sensors Using Radio Frequency Harvesting for IoT Applications
No full textThe abundance of connected devices in modern cities and metropolises has contributed to the development of intelligent infrastructure and networks. More specifically, the integration of devices within the Internet of Things (IoT) has become integral to smart cities, enhancing various aspects such as safety, congestion management, and providing real-time information to users. However, the deployment of IoT networks relies on autonomous sensors capable of collecting and transmitting data in real-time to either other devices or centralized units. Having a reliable and non-stop power source to maintain the sensor operational 24/7 poses a challenge, that is why this paper will focus on analysing radio frequency harvesting, to create a continuous power output for the sensors. Mobile networks spectrums are preferred in our case due to the high-power density they offer, compared to other radio technologies. The RF (Radio Frequency) harvester circuit is built from several components, the Long-Term Evolution (LTE) band antenna, the coupler circuit, the rectifier circuit and the load, which in our case will be the sensor. Various circuit elements are evaluated and compared, addressing the requirements of output voltage and power. The findings from this research provide insights into the practicality of RF harvesting as a sustainable energy solution for autonomous sensors within IoT networks, paving the way in the evolving landscape of connected devices
Addressing Global Warming Issues in Schools Through Curriculum Integration
No full textGlobal warming is gradually shifting weather patterns, causing sea levels to rise, and increasing the frequency of extreme weather events. To effectively mitigate its effects and adapt to the changing climate, it is imperative to have a thorough understanding of global warming and its impacts. Thus, the purpose of this study is to use curriculum integration to address global warming issues in schools. It takes educational cues from Finland, Japan, South Africa, and New Zealand about how to teach about global warming. In order to accomplish the study\u27s goal, a systematic review of the literature was carried out, incorporating and deleting journal articles from databases with broader coverage, including Scopus, WoS, and DOAJ. Thematic analysis was used to examine the data. The comparative study\u27s conclusions show that while South Africa implements climate education policies unevenly, Finland is committed to interdisciplinary teaching, Japan incorporates climate change education into curricula, and New Zealand incorporates climate science across a variety of subjects. It was suggested, based on the findings, that countries create and carry out professional development initiatives to give educators the know-how and abilities needed to instruct students on climate change-related subjects
On a Comparative Analysis of Research Performance Trends in Western Balkans through Peer Reviewed Indexing Databases
No full textIn order to be published in scientific journals and conferences, the results of every systematic study must be presented in the form of a research report or paper that satisfies the standards established by the scientific community during centuries of development. It is well acknowledged that the output of research is what defines its performance. Because there are millions of researchers in the world, there has been a significant increase in demand in recent years for boosting the visibility of research outcomes. Every research group aims to maintain its research efforts and obtain further funding by maximizing the impact and visibility of the research results it produces in the scientific community. This obviously holds true for the advancement of any researcher\u27s career in universities and research canters. As a result, over time, the international scientific community has established commonly used standards and measurements to distinguish between different levels of quality in research. Ranking significant and unimportant outcomes is the constant aim of all such groups. In light of the aforementioned observations, indexing has become a crucial criterion for characterizing each research publication. All research groups want to expand their effect in their respective scientific societies and gain more funding by producing more indexed papers than other groups in the same field. Additionally, indexed articles describe the calibre and effectiveness of research conducted by both individuals and groups, but also that of universities and research canters. As a generalization of the aforementioned factors, it makes sense to take into account research performance at the national level if, on the other hand, every organized nation is evaluated in terms of its universities and research canters with reference to local research development. Therefore, comparing research performance and its evolution across nations with comparable organizational characteristics in relation to the global competitiveness is the goal of this study. Here, the Western Balkans are chosen as a collection of "similar" nations. Therefore, starting with the widely used SCOPUS indexed database, an attempt is made to measure their research performance in terms of absolute numbers of indexed papers. Finding patterns in these nations\u27 scientific advancements, however, is the most crucial component of this study. Naturally, this is still a work in progress, and the quantification study that is being undertaken should take into account a lot more aspects. It is obvious that such work could be applied to any other group countries
Analysis of Vibration and Acoustic Sound Radiation of a Rotationally Symmetrical Plate
No full textThe importance of studying the vibration and acoustic radiation of symmetrical plates lies in its widespread applications across various engineering disciplines. Symmetrical plates are fundamental structural components found in aerospace, automotive, marine, and civil engineering systems. This study is focused on vibration behaviour and acoustic sound radiation of a rotationally symmetrical plate.
Therefore, analysing the vibration behaviour of a rotationally symmetrical plate can be helpful to optimize the mechanical and acoustic properties of such structures. Sweep operation, Lissajous curve and stochastic excitation methods have been used to determine the natural frequencies. The acoustic radiation is measured at various frequencies and distances from the plate using a calibrated microphone
Inflammation-induced Deficits to Learning and Memory in C. elegans Through Notch Dysregulation
No full textNeurodegenerative diseases are increasingly common in the aging population. Recently, increased inflammation has been observed in the brains of individuals with neurodegenerative diseases. In this work, we examine the role of inflammation in the regulation of learning and memory in Caenorhabditis elegans. C. elegans exposed to pro-inflammatory cytokines were subjected to a chemotaxis learning and memory assay. A significant decrease in learning and memory was seen, with the greatest decrease observed in the IL-6 treatment group. Exposure to the pro-inflammatory cytokine IL-6 caused a decrease in overall Notch1 and regulator Adm-4 expression. Dysregulation of the Notch pathway may provide a mechanism for the observed decrease in learning and memory following IL-6 exposure