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All Journal Asian Journal of Science, Technology, Engineering, and Art Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics African Multidisciplinary Journal of Sciences and Artificial Intelligence
Shah, Neha
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Agriculture, Biological Sciences & Forestry Arts Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Geometric Foundations of Engineering Design: The Role of Conic Sections Enhanced by Artificial Intelligence Das, Rajnandani; Shah, Neha; Sahani, Suresh Kumar
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 1 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i1.8700

Abstract

Many branches of engineering rely on four fundamental geometric shapes: circle, ellipse, parabola, and hyperbola, whose intrinsic properties enable engineers to develop more accurate mathematical models, optimize trajectories, and enhance structural integrity in complex design contexts. This study examines how these classical conic sections are applied in real-world engineering problems and explores the utilization of geometric principles in robotics, signal processing, and automated systems to support efficient problem-solving. By relating the properties of conic sections to engineering requirements in areas such as bridge design, trajectory optimization, and structural analysis, the study elucidates how these forms underpin both analytical modelling and practical implementation in contemporary engineering practice. The analysis shows that the relevance of conic sections to practical engineering applications is clearly demonstrated across multiple domains, highlighting their role in improving modelling accuracy, guiding system optimization, and informing robust design strategies. The study concludes that classical geometry, particularly the theory of conic sections, continues to play a vital role in shaping modern engineering practices and carries important implications for advancing engineering education, promoting interdisciplinary integration, and sustaining innovation in technology and infrastructure development.
Deep Learning - Based Shape Recognition and Classifications of Conic Geometries in Engineering Drawing Das, Rajnandani; Shah, Neha; Sah, Dilip Kumar; Sahani, Kameshwar; Sahani, Suresh Kumar
Asian Journal of Science, Technology, Engineering, and Art Vol 4 No 2 (2026): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v4i2.9335

Abstract

Engineering drawings frequently contain conic geometries such as circles, ellipses, parabolas, and hyperbolas, which are fundamental to mechanical design and industrial applications. Accurate identification and classification of these shapes are therefore essential for computer-aided design (CAD) systems, automated inspection, and intelligent design analysis. However, conventional geometry-based or rule-based approaches often perform poorly when drawings are noisy, complex, or partially incomplete. This study proposes a deep learning-based approach using convolutional neural networks (CNNs) to automatically extract features and classify conic shapes in engineering drawings. By learning discriminative visual representations directly from input data, the proposed method enhances classification accuracy, improves robustness, and reduces the need for manual intervention. The study concludes that CNN-based conic shape recognition offers a reliable and efficient solution for engineering and industrial contexts, with practical implications for improving automation and intelligent analysis in design-related applications.
Time as Dimension or Illusion? A Critical Analysis within the Framework of Relativity Sah, Praveen; Shah, Neha; Sah, Dilip Kumar; Sahani, Suresh Kumar
Mikailalsys Journal of Advanced Engineering International Vol 3 No 1 (2026): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v3i1.9337

Abstract

The nature of time remains a central problem in both physics and philosophy, particularly in light of the tension between classical and relativistic conceptions of temporality. This paper examines the question of whether time is an illusion within the framework of relativity. Whereas classical physics treats time as absolute, universal, and uniformly flowing, Einstein’s theory of relativity demonstrates that temporal intervals vary according to relative motion and gravitational fields. Building on this framework, the paper argues that time is operationally real insofar as it can be measured and modeled physically, yet the notion of a universally shared and continuously flowing present has no firm basis in modern physics. The analysis further suggests that the relativistic view of spacetime supports the coexistence of past, present, and future within a unified four-dimensional structure. It also considers whether the human experience of temporal passage arises from fundamental physical laws or from cognitive and thermodynamic asymmetries. The paper concludes that time itself is not an illusion; rather, what is misleading is the classical intuition that time flows identically for all observers. This study contributes to ongoing interdisciplinary debates by clarifying how relativity reshapes the philosophical interpretation of temporal reality.
Utilizing Permutation and Combination Techniques in Business Decision-Making Processes Sah, Bardan; Jayswal, Ritika; Thakur, Satyam; Shah, Neha; Sah, Dilip Kumar; Sahani, Suresh Kumar
Mikailalsys Journal of Mathematics and Statistics Vol 4 No 2 (2026): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v4i2.9336

Abstract

Although permutations and combinations are often regarded as purely theoretical mathematical topics, they play a significant role in practical decision-making and contemporary business operations. This study examines the application of permutations and combinations in everyday decision-making and real business contexts, particularly in quality control, marketing strategy, resource planning, and inventory management. Using real-world examples and case studies, the article demonstrates how organizations employ these combinatorial concepts to improve productivity, reduce costs, optimize available resources, and strengthen competitive advantage in increasingly complex market environments. The findings indicate that a sound understanding of permutations and combinations enhances managerial and executive decision-making, especially when evaluating numerous alternatives, assessing the likelihood of possible outcomes, selecting appropriate combinations of people or products, and determining optimal configurations. The study concludes that permutations and combinations are not merely academic concepts but practical analytical tools that support more effective and strategic business decisions. This study contributes to a broader understanding of how foundational mathematical reasoning can be applied to improve organizational efficiency and decision quality in business practice.
Model-Free Reinforcement Learning for Parabolic Trajectory Optimization in Robotic Arms Karn, Aadarsh; Shah, Neha; Sah, Dilip Kumar; Sahani, Suresh Kumar
African Multidisciplinary Journal of Sciences and Artificial Intelligence Vol 3 No 1 (2026): African Multidisciplinary Journal of Sciences and Artificial Intelligence
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/amjsai.v3i1.9338

Abstract

Robotic arms are widely employed in applications that require smooth motion and energy-efficient operation, particularly in tasks such as object throwing and liquid dispensing, where movement often follows a curved path toward a target point. However, conventional trajectory planning methods that rely on predefined mathematical equations may not accurately represent real-world robotic systems due to uncertainties and payload variations. This study aims to optimize the trajectory of a robotic arm moving along a parabolic path using reinforcement learning and to evaluate whether this approach can successfully learn improved trajectory patterns during motion. The research integrates initial classical physics principles for curved motion with a reinforcement learning framework to enhance trajectory following toward a desired point. The findings indicate that reinforcement learning can effectively learn optimized trajectory paths and improve the motion performance of the robotic arm. The study concludes that reinforcement learning offers a promising approach for achieving smoother robotic motion with satisfactory energy efficiency under dynamic conditions. This work contributes to the advancement of intelligent motion planning by demonstrating the potential of reinforcement learning to improve trajectory optimization in robotic systems operating under practical uncertainties.
Co-Authors Das, Rajnandani Jayswal, Ritika Karn, Aadarsh Sah, Bardan Sah, Dilip Kumar Sah, Praveen Sahani, Kameshwar Sahani, Suresh Kumar Thakur, Satyam