Volume 13 | Issue 5 |

 Abstract

A very important aspect of the development of smart transportation networks and road safety is 'traffic sign recognition'. This survey covers all types of learning techniques applied to traffic sign recognition with respect to effectiveness, challenges, and progress. The study begins with an overview of the foundational methods of traffic sign recognition: traditional image processing approaches and their limitations. Further below, the heart of the survey is modern learning techniques, especially 'machine learning (ML)' and 'deep learning (DL)' approaches. The report reviews employ learning techniques applied in 'traffic sign recognition systems'. It discusses both the most recent developments in deep learning and advanced machine learning as well as conventional image processing. The review ranges from classical techniques such as colour and shape-based recognition to modern techniques such as 'Convolutional Neural Network (CNN),' which have significantly increased recognition accuracy. Several important datasets on which the training and evaluation should be done have been reported. One of them is 'German Traffic Sign Recognition Benchmark', and the challenges in practical applications along with the future research directions in this field have been highlighted. An overview has been provided regarding the effectiveness and evolution of learning techniques to improve 'Traffic sign recognition systems'.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Traffic sign recognition, machine learning, convolution neural network, convolution neural network,natural language processing(NLP)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial Intelligence (AI) is exerting a transformative influence on the discipline of mathematics, reshaping both theoretical inquiry and practical engagement. In mathematical research, AI systems particularly those employing machine learning, symbolic computation, and automated reasoning are facilitating novel approaches to theorem discovery, proof verification, and conjecture generation. These tools are augmenting human intuition and expanding the frontiers of mathematical exploration by uncovering patterns and relationships that might otherwise remain obscured. In the educational domain, AI technologies are enabling more personalized and adaptive learning environments. Intelligent tutoring systems and algorithm-driven platforms are redefining mathematics pedagogy by offering tailored instruction, real-time diagnostics, and scalable feedback mechanisms. Such innovations hold promise for improving learning outcomes and democratizing access to mathematical knowledge. AI's impact is equally evident in applied mathematics, where data-driven models and optimization techniques are being integrated into fields such as engineering, economics, and the natural sciences. These applications demonstrate AI's capacity to enhance the modeling, analysis, and solution of complex real-world problems. This paper presents a qualitative synthesis of current literature, examining the multifaceted roles AI plays across mathematical subfields. It also engages with the philosophical and ethical considerations emerging from this integration particularly the implications for mathematical rigor, creativity, and epistemology. By surveying contemporary developments, this study aims to provide a nuanced understanding of how AI is reshaping the nature and practice of mathematics in the modern era.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Automated Theorem Proving, Mathematical Education, Applied Mathematics, Ethical Implications of AI.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An automatic scoring system for badminton matches using computer vision techniques is presented in this paper. TrackNetV3 is used for shuttlecock detection, and a custom model is used to predict the corners of the badminton court, in order to convert the frame into a top-view perspective. By connecting the most frequent coordinates, the trajectory of the shuttlecock is determined. The shuttle drop detection method identifies frames with speeds below a predetermined threshold, indicating that the shuttlecock has fallen. An additional model is utilized to predict player poses during service, which aids in the decision-making process. As part of the scoring process, the system creates polygons in the top-view image to mark the locations of shuttle drop-offs. This proposed system is effective at scoring badminton matches accurately, as demonstrated by experimental results.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Badminton, Automated Scoring, Computer Vision, TrackNetV3, Shuttlecock Detection, Homography Matrix, CNN, Pose Prediction, Deep learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cloud computing promises to significantly change the way we use computers and access and store our personal and business information. With these new computing and communications paradigms arise new data security challenges. Existing data protection mechanisms such as encryption have failed in preventing data theft attacks, especially those perpetrated by an insider to the cloud provider, For securing user data from such attacks a new paradigm called fog computing can be used. Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. The motivation of Fog computing lies in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined network .This technique can monitor the user activity to identify the legitimacy and prevent from any unauthorized user access. Here we have discussed this paradigm for preventing misuse of user data and securing information.

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 Keywords

Cloud Computing,Fog Computing,Data Security,Data Theft Attacks,Insider Attacks,Encryptions,Smart Grids,Vehicle Networks,Software Defined Network(SDN),Unauthorized Access,Data Leakage

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study focuses specifically on the constraints that women face in accessing and participating in leisure activities. The study examines the gendered dimensions of leisure and discuss how social, cultural and economic factors restrict women's leisure opportunities while comparing with men. Key themes include time, psychological constraints, economic limitations, safety matters, unpaid care responsibilities and socio-cultural norms. The study highlights the need for inclusive leisure policies and practices that address the systematic inequalities that affect women's access to free time and recreation.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gender, Leisure, Constraints

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Fashion's Answer to Climate Change: Art Design a Collection Inspired by Nature examines the potential of fashion to be both a reflection of and driver of environmental change. With the climate emergency gaining pace, creative industries need to come to terms with their contribution to ecological damage as well as provide visionary solutions. This project places fashion not just as an ornament but as activism a platform where art, sustainability and innovation meet to ignite a more harmonious world. Inspirited by nature's abiding beauty, the collection borrows natural forms like bark patterns, leaf veins, and earthy tones of soil, sand, stone and sky. Earth textures and natural patterns are the key design motifs, evoking a sense dialogue between wearer and landscape. Biomimicry informs aesthetic as well as functionality, embracing the intelligence and strength of nature. Neutral colors offer timelessness and adaptability, guaranteeing that the clothes outlive fast fashion and support a culture of slow, intentional buying. The base of the collection lies on sustainable materials, such as organic cotton, hemp, bamboo, and recycled materials. Through choosing low-impact fabrics, the project keeps its footprint low and upholds a regenerative model of production. Every garment is made with ethical concerns in mind at each step: fair wages, secure working conditions, transparent supply chains and dedication to respecting people as much as the earth. Current menswear is the main canvas upon which to conduct this journey. Conscious masculinity, a mix of strength and sensitivity, coupled with responsibility, shapes the silhouettes, fusing cutting-edge tailoring with nature-influenced softness. Functionality and comfort come first in the designs while radiating ageless sophistication and durability over disposability. By treating clothing as wearable art, the collection combines craft and couture, in which each stitch, pattern and material selection is a testament to environmental responsibility. Methods like hand-dyeing with natural dyes, laser-cut details replicating leaf structures and artisanal surface treatments align art with sustainable practice. Each piece is a living artifact, bearing both aesthetic appeal and ethical integrity. In addition to the material and visual, the collection takes the stance of environmental activism. It poses important questions: How do we mend our broken relationship with nature? How can fashion fuel systemic change instead of destruction? The project stands for regenerative design practices, encouraging not only sustainability but actual restoration of ecosystems through improved sourcing, manufacturing and consumer literacy.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Sustainable fashion, Climate-conscious design, Eco-friendly textiles, Nature inspired fashion, Fashion and climate change, Environmentally responsible design, Green fashion, Biophilic design in fashion.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

TexttoSQL is a Python tool that leverages Retrieval-Augmented Generation (RAG) to facilitate precise SQL query generation tailored to specific databases through the utilization of Large Language Models (LLMs). This paper provides an in-depth review of TexttoSQL, elucidating its core components, functionalities, and usage guidelines. The tool operates through a two-step process: indexing database specifics into a vector store to create a database-specific RAG model, and formulating SQL queries based on user inquiries using the generated model. Key functions of TexttoSQL, including indexing database details and querying databases using natural language, are discussed. Furthermore, the paper outlines the setup process, class hierarchy, and parameters associated with TexttoSQL, providing a comprehensive guide for users. Additionally, the paper explores the significance of context embedding in the vector store, exemplified through the indexing of Data Definition Language (DDL) statements, question-SQL pairs, and documentation strings. The bulk addition of data from external sources is also examined, highlighting the efficiency of data integration into TexttoSQL's knowledge base. Moreover, the paper delves into the ask_db() method, elucidating its role in querying databases, retrieving SQL queries, and presenting results through DataFrames, natural language responses, and visualizations. Overall, this review paper comprehensively analyzes TexttoSQL's capabilities, emphasizing its potential to enhance database interaction efficiency using cutting-edge natural language processing techniques.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

TexttoSQL, Python Tool, Retrieval-Augmented Generation, Large Language Models, Database Interaction, SQL Query Generation, Vector Store, Data Definition Language, Context Embedding, Natural Language Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Agriculture faces few challenges, especially since there are many tasks that require great physical effort. Inefficiencies ultimately lead to more expenditure, wastage of resources, and decreased productivity. While India is among the highest agricultural producers, it consumes far more than it produces. This gap highlights the need for some creative and effective farming techniques. To address these problems, AgriBot is developed as a virtual robotic system. It automates three important farming tasks: seeding, irrigation, and weed control. The design basis of AgriBot is the combination of ROS2 (Robot Operating System 2) with simulation using Gazebo. The seed planting module automates the sowing procedure. The irrigation system uses soil moisture sensors to irrigate the crops only when needed to minimize wastage. The weed detection module utilizes a Random Forest algorithm to detect weeds and is removed by a laser-burn-based system. AgriBot reduces the amount of manual labor, conserves resources, and increases the efficacy of farming. Simulation tools such as Gazebo allow for the testing and refinement of a system before it is deployed in the real world. This project aims to make farming modern, efficient, cost-effective, and environmentally sustainable.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Agricultural virtual robotics, Irrigation, Machine learning, Precision farming, ROS2, RViz, URDF, Weed detection.

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Creative Commons Attribution 4.0 and The Open Definition

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This systematic review examines the extent to which integrated teacher education programs assist pre-service teachers in gaining 21st-century competencies. Synonyms such as "Integrated teacher education," "21st-century skills," and "Pre-service teachers" were employed in an extensive search of academic databases, such as ERIC, Scopus, and Web of Science. Studies that examined programs that integrated theoretical and practical training, prioritized critical thinking, collaboration, and problem-solving, and engaged pre-service teachers were included for review. Studies with results that could not be measured or were not empirical were excluded according to some specifications. 25 peer-reviewed articles published between 2010 and 2024 were selected from an initial total of 1,342 records according to methodological quality and importance. Program structure, skill outcomes, and research design dominated the issues for data extraction. The findings indicate that combined teacher education programs highly enhance pre-service teachers' critical thinking (as evidenced in 80% of the studies), teamwork (76%), and problem-solving skills (68%). Programs that were able to integrate coursework and fieldwork obtained the best results. The strongest evidence came from quantitative and mixed-methods studies, but qualitative studies provided valuable contextual background too. The robustness and generalizability of findings, though, were compromised by the presence of small sample sizes, imbalanced evaluation instruments, and multiple implementation models. There is limited evaluation of long-term effects in that 40% of the studies used longitudinal designs. In general, this review demonstrates how integrated teacher education programs can facilitate pre-service teachers' acquisition of critical 21st-century skills. To enhance evidence-based reform of teacher education policy and practice, it also calls for additional research using standardized measures, larger sample sizes, and long-term assessment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pre-service Teacher,21st-Century, Creativity, Critical Thinking

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the influence of organizational values across various stages of the employee life cycle such as recruitment, onboarding, training & development, retention and separation focusing on performance, engagement, leadership, and value reflection. The study adopts a descriptive research design and uses a structured questionnaire to collect primary data from 152 employees. The data were analyzed using statistical tools such as the Mann-Whitney U test, Kruskal-Wallis H test, Correlation analysis and Chi Square test with the help of SPSS. The findings emphasize the importance of leadership in consistently reinforcing the values. The study suggests that organizations should continuously integrate core values at each stage of the employee life cycle to improve both employee outcomes and organizational success.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Organizational values, Employee Life Cycle, Employee Engagement, Leadership, Value Integration.

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Creative Commons Attribution 4.0 and The Open Definition