Volume 13 | Issue 5 |

 Abstract

The stock market is inherently volatile and influenced by numerous factors, making accurate prediction a complex task. In recent years, the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques has shown promising potential in forecasting stock market trends by uncovering hidden patterns within large volumes of historical and real-time data. This research paper presents a comprehensive approach to stock market prediction using various AI/ML models including Linear Regression, Support Vector Machines (SVM), Random Forest, and Recurrent Neural Networks (RNN), particularly Long Short-Term Memory (LSTM) networks.

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 Keywords

Research papers on stock market prediction using AI and ML commonly include keywords such as Stock Market Prediction, Artificial Intelligence, Machine Learning, and Deep Learning. Frequently used algorithm-related keywords are LSTM, RNN, SVM, Random Forest, and Linear Regression. Other important terms include Time Series Forecasting, Technical Indicators, Sentiment Analysis, Financial Data, Feature Engineering, and Model Evaluation. Tools and libraries like Python, Scikit-learn, TensorFlow, and

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Art developed specifically for a particular location is known as site-specific art. Usually, the artist plans and creates the artwork with consideration for the place. According to Russian artist Andrej Monastirsky site-specific Installation art is "an artist working on an installation uses space as an instrument, but creates something located in time that looks like an aesthetic act". A site-specific piece of art is created for a particular place; as such, it loses all or most of its meaning if taken out of that place. This study aims to determine how contemporary art appreciation is facilitated by the placement of site-specific installation artworks. The study focuses on the artist Baalaa.R and his site-specific installation art known as "Dynamism and Machine Speed". He created this Installation art at the site of the Chennai Rail Museum in Tamil Nadu, India in 2018. He took one year to complete the work and used the concept "Invisible to visible" in his artwork. This is a conscious initiation of Baalaa.R after the futurists like Marcel Duchamp, Jacamo Bella, Umberto Boccioni, and Jean Tinguely. Invisible-to-Visible visualizes real truth through constructed reality to create the ultimate connected experience for viewers. It helps to realize unseen truth with meaning. With his installation art, Baalaa.R reveals the force and speed of mechanical origin. He created his art like a dynamic machine in one direction and speed in another direction. He brought the invisible Dynamism and Machine Speed into a visible form of art. Railway iron and other metal scrap materials are used in this installation art. Baalaa created this installation art as a movable installation and this can be shifted to any other Rail Museum. Chennai Rail Museum maintains this art in a good way and Baalaa's art has an everlasting life

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 Keywords

Installation art, Site-specific, Dynamism, Machine Speed, Invisible to visible

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper presents a deep learning-based approach for detecting the presence or absence of helmets on individuals in real-time. The proposed system utilizes convolutional neural networks (CNNs) to analyze images or video frames and classify whether a person is wearing a helmet. A user-friendly interface allows for live video stream input or image upload, making the solution suitable for traffic surveillance and workplace safety monitoring. The model is trained on a diverse dataset of annotated images capturing various helmet types, lighting conditions, and backgrounds, ensuring reliable detection accuracy. Designed for offline deployment, the system is well-suited for use in areas with limited internet access, offering authorities and industries an effective tool to promote safety compliance and prevent accidents.

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 Keywords

Safety Compliance, Computer Vision, Real-time Monitoring, Convolutional Neural Network (CNN), Deep Learning, Helmet Detection

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 Abstract

Technology has significantly changed educational techniques and improved learning results in the always changing sector of education. Virtual reality (VR) is one such invention that has shown promise in revolutionizing the teaching of visual communication courses. This study investigates how virtual reality (VR) technology may be used to teach visual communication studies, looking at how well it works to increase student engagement, promote experiential learning, and help students remember difficult ideas.

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 Keywords

Virtual Reality, Visual Communication Studies, Technology in Education, Experiential Learning, Pedagogy

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This study examined principals' accountability and teachers' job productivity in public secondary schools in Delta State. Three research questions and one hypothesis guided the investigation. The study employed correlation method of ex-post facto research design. The population for the study consisted of 13,220, made up of 465 principals and 12, 755 teachers of all the public secondary schools in the three Senatorial Districts of Delta State. The sample of the study consisted of 60 principals and 400 teachers in public secondary schools in Delta State selected using stratified sampling technique. Data was collected using two instruments entitled 'Principals' Accountability Scale (PAS)' and Teachers' Job Productivity Scale (TJPS)' The PAS and TJPS were face and content validated by two experts from the Department of Educational Management and Foundations, Delta State University, Abraka. The reliability coefficients were 0.82 for PAS and 0. 84 for TJPS. Data analysis was conducted using mean, standard deviation and coefficient of determination to answer the research questions. Pearson Product Moment Correlation, r was used to test the hypothesis The findings of the study revealed that Principals' levels of accountability were moderate. Teachers' level of job productivity was high. There was a positive relationship between principals' accountability and teachers' job productivity public secondary schools in Delta State, Nigeria.

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 Keywords

Accountability, job productivity, principals, secondary school, teachers.

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 Abstract

The way that the constituent components of an image composition interact within a certain visual space generates a visual aesthetic known as visual rhythm and harmony. In art, rhythm is the pattern that is produced visually by the repetition of lines, dots, shapes, and colors. It is employed to highlight specific compositional elements and to establish a sense of flow and connection within a piece of art. Repetition and variation, contrast and gradation can all be used to create rhythm. The balanced placement of related or comparable components together in a design results in a visually pleasant harmony. This visual harmony results in integrating related or comparable elements, such as neighboring hues, related lines, shapes, and textures

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 Keywords

Conceptualization, Visual rhythm, Harmony, Relief painting

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The progress of artificial intelligence (AI) has revolutionized various sectors and impacted society as a whole, enabling advancements in healthcare, automation, and global communication. While these benefits are substantial, the emergence of AI has also introduced notable ethical dilemmas and security risks, particularly related to cybercrime. Cybercriminals are increasingly leveraging AI for sophisticated attacks, including phishing schemes, deep fakes, and AI-generated malware, which pose significant threats to individuals, organizations, and governments. This paper examines the dual nature of AI, highlighting its benefits while also addressing its potential for misuse in the realm of cybercrime. It critically analyzes ethical considerations, accountability challenges, and the effectiveness of global regulatory frameworks, such as the OECD AI Principles and the EU AI Act, in mitigating these risks. By proposing solutions like explainable AI, comprehensive legal reforms, and collaborative interdisciplinary initiatives, the paper emphasizes the necessity of balancing innovation with security in order to harness AI's transformative potential effectively.

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 Keywords

AI, Cybercrime, Ethics, Accountability, Regulation, Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the interplay between classical Tamil vocabulary and free-style, contemporary expressions in Tamil film songs. Tamil cinema, known for its rich musical heritage, often serves as a cultural mirror reflecting linguistic trends and shifts in popular expression. The paper analyzes the usage, context, and impact of classical Tamil words--rooted in Sangam literature and traditional poetics--alongside colloquial and modern slang words that characterize free-style language. Through lyrical analysis of selected songs across different decades, this research investigates how lyricists balance tradition and modernity to connect with diverse audiences. The findings highlight how the fusion of these linguistic styles contributes to the emotional depth, cultural resonance, and evolving identity of Tamil film music. Through lyrical analysis of selected songs spanning from the 1950s to the present, this research investigates how lyricists balance tradition and modernity to connect with audiences across generations. It also examines how sociopolitical changes, globalization, urbanization, and technological advancements have influenced linguistic choices in songwriting. The study considers the role of major lyricists like Kannadasan, Vairamuthu, and Na. Muthukumar, who have skillfully integrated both classical and casual expressions. Further, the paper discusses how the fusion of these linguistic styles not only contributes to the emotional and poetic depth of the songs but also reflects evolving cultural identities and value systems within Tamil society. This analysis provides insights into how Tamil film lyrics function as both artistic expression and a record of language change, offering a lens into the complex relationship between tradition, innovation, and audience reception in popular culture

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 Keywords

Classical Tamil, Free Style Tamil, Tamil Film Songs, Lyrical Analysis, Language Evolution

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Since the early 1990s, when the term "Big Data" first appeared, machine learning (ML) has demonstrated its remarkable efficiency and computational capability while managing a variety of co Complicated issues in numerous fields. Spacecraft design and manufacturing present a variety of multi-objective and restricted optimization challenges that can be effectively resolved with big data analysis and machine learning (ML). These technologies have the potential to be advantageous to the aerospace sector. A thorough analysis of machine learning's application in aircraft engineering is presented in this research article. While the primary focus of rockets is on thermoacoustic instability detection and lunar landing, the artificial satellite portion falls under the categories of satellite communication and default diagnostics. Since each piece of data gathered helps create a better-trained system for attitude control, navigation, and default diagnosis, the widespread application of machine learning in aerospace engineering undoubtedly contributes to the growth of the contemporary aerospace industry by raising the success rate of upcoming space exploration missions.

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 Keywords

Anomaly Detection, Collision Avoidance, Attitude control, Thermoacoustic, Instabilities, Detection, Rocket landing.

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This review comprehensively examines recent advances in microbial biodegradation of xenobiotic compounds, highlighting key mechanisms, challenges, and future prospects. We analyze degradation pathways employed by bacteria, fungi, and algae, focusing on critical enzymes like oxidoreductases and hydrolases that facilitate pollutant breakdown. The paper explores cutting-edge bioremediation technologies including genetically engineered microbes, nano bioremediation, and integrated phytoremediation systems. Case studies demonstrate successful applications for pesticides, pharmaceuticals, and plastic degradation, while also revealing limitations such as incomplete mineralization and ecological risks. Emerging solutions leveraging synthetic biology, AI-driven optimization, and circular economy approaches are discussed as promising directions. The synthesis of 150+ recent studies provides researchers with both a scientific reference and strategic framework for developing sustainable pollution mitigation strategies. This work underscores the need for interdisciplinary approaches to overcome current challenges in scaling bioremediation technologies while ensuring environmental safety and cost-effectiveness.

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 Keywords

Xenobiotics, microbial degradation, bioremediation, enzymatic pathways, environmental biotechnology

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Creative Commons Attribution 4.0 and The Open Definition