Daily Archives: September 22, 2025

Authors: Praveen Lokanath

Abstract: The rise of artificial intelligence (AI) is reshaping the nature of work in ways that echo past industrial revolutions, yet with unprecedented speed and complexity. This paper explores how previous waves of technological transformation — from mechanization in the 18th century to the digital revolution of the late 20th century — can inform our understanding of AI's current and future impact on employment, labor markets, and workforce dynamics. Drawing lessons from history, the study highlights patterns of job displacement, creation, and evolution, emphasizing the critical roles of policy, education, and social adaptation. It also examines the unique characteristics of AI that distinguish it from earlier innovations, particularly its capacity to automate cognitive tasks and decision-making processes. By synthesizing historical insights and contemporary developments, the paper offers a framework for anticipating the challenges and opportunities AI presents, aiming to guide stakeholders in shaping a more equitable and resilient future of work

Published by: vikaspatanker

Authors: Atharva Daga, Viraj Laddha, Prathmesh Jain, Tanmay Sharma

Abstract: Brain tumor detection is important in neuroimaging, affecting patient outcomes and prognosis. To improve detection capabilities, this study uses MRI & CT Scan Image to classify brain tumor while employing deep learning techniques. We test how well pre-trained models like VGG-19, DenseNet-121, and ResNet-50 perform by using detailed information from MRI and CT scans to improve the accuracy of detecting brain tumors and help identify them more clearly and precisely, facilitating swift diagnosis and informed treatment planning. This research utilizes image fusion and prediction algorithms to address challenges such as limited data diversity and difficulties in differentiating tumor boundaries from surrounding tissues, thereby improving model performance. By evaluating the results, we identified the most accurate model for brain tumor diagnosis and provided insights into its use and impact on diagnosis. This research advances technology and improves patient outcomes through more accurate and timely diagnoses. Analysis shows Resnet-50 achieving the highest accuracy among all other models is effective for tumor detection.

 

Published by: vikaspatanker

Authors: M Devendar Reddy, S Akhil Reddy, Anand Jawdekar, N Saiprem,, B UdayKiran Reddy

Abstract: The results of the experiment indicate that combining reinforcement learning with vision-based techniques can offer signifi- cant improvements in autonomous naviga- tion [2],[5]. Scale-Invariant Feature Trans- form (SIFT) was particularly effective in recognizing both the delivery target and potholes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Transform proved to be highly efficient tools for lane identification [4],[6], allowing the robot to maintain pre- cise lane alignment during movement. Fur- thermore, IMU-based orientation correction provided additional robustness, preventing errors caused by yaw drift and other orien- tation issues [7]. Collectively, these meth- ods enabled the robot to adapt dynami- cally to its environment and demonstrate consistent success across repeated trials [2]. These findings suggest that the proposed framework not only addresses the imme- diate problem of pothole detection [9],[10] but also enhances the overall safety and reliability of autonomous vehicles. Look- ing ahead, the study shows strong poten- tial for real-world applications, as it pro- vides a scalable and practical solution that can be integrated into future self-driving systems to improve passenger safety, vehi- cle durability, and overall traffic efficiency [5]. Autonomous driving continues to be one of the most promising innova- tions in intelligent transportation sys- tems, but real-world challenges such as potholes still pose serious risks to safety and efficiency [2],[5]. This study explores the application of rein- forcement learning for addressing the issue of pothole detection and avoid- ance in self-driving cars [2]. To evalu- ate the framework, a detailed robot simulation was built in the Webots environment, making use of Python programming and OpenCV for vision processing [8]. Within this setup, the robot was designed to complete three key tasks: it first identifies a delivery target symbolized by a gnome placed in the environment, then transitions into lane-following mode to maintain safe navigation, and finally responds appropriately by halting when a pot- hole is detected on its path [8]. Each of these components plays a crucial role in ensuring safe and reliable op- eration. The framework integrates several technologies, including real- time computer vision for object detec- tion, IMU sensor feedback for orien- tation correction, and motor control for smooth navigation [7]. These el- ements work together to enable the robot to perceive its surroundings, adapt to hazards, and make sequen- tial decisions that reduce the risk of accidents [2]. The results of the experiment in- dicate that combining reinforcement learning with vision-based techniques can offer significant improvements in autonomous navigation [2],[5]. Scale- Invariant Feature Transform (SIFT) was particularly effective in recogniz- ing both the delivery target and pot- holes with a high degree of accuracy [7],[10], ensuring reliable performance under varying conditions. Canny edge detection and the Hough Line Trans- form proved to be highly efficient tools for lane identification [4],[6], al- lowing the robot to maintain pre- cise lane alignment during movement. Furthermore, IMU-based orientation correction provided additional robust- ness, preventing errors caused by yaw drift and other orientation issues [7]. Collectively, these methods enabled the robot to adapt dynamically to its environment and demonstrate consis- tent success across repeated trials [2]. These findings suggest that the pro- posed framework not only addresses the immediate problem of pothole de- tection [9],[10] but also enhances the overall safety and reliability of au- tonomous vehicles. Looking ahead, the study shows strong potential for real-world applications, as it provides a scalable and practical solution that can be integrated into future self- driving systems to improve passenger safety, vehicle durability, and overall traffic efficiency [5].

DOI: https://doi.org/10.5281/zenodo.17174389

 

Published by: vikaspatanker

Authors: Ryan Gautam

Abstract: This secondary research review evaluates the extent to which current autonomous vehicle (AV) pedestrian detection datasets and validation protocols represent and protect disabled road users—including wheelchair users, white cane users, guide dog handlers, and mobility scooter users—across lighting and weather conditions. Synthesizing peer reviewed studies, standards analyses, government reports, and advocacy documents from 2015–2025, the review finds systematic underrepresentation of disability categories and accessibility infrastructure in widely used datasets, alongside documented detection biases that elevate risk for vulnerable pedestrians under low light and non standard movement scenarios. Current validation frameworks (e.g., functional safety and SOTIF) and regulatory pathways provide limited, non specific guidance on disability inclusive testing, allowing deployments that lack demonstrable parity performance for disabled pedestrians. The paper proposes a minimum pre deployment standard requiring disability inclusive dataset composition, category specific performance thresholds (with edge case coverage), and independent third party audits, with ongoing post deployment monitoring. This framework is feasible within established safety and regulatory processes and is necessary to align AV deployment with equity and safety obligations for all road users.

DOI: https://doi.org/10.5281/zenodo.17173964

 

Published by: vikaspatanker

Authors: Sandip Bhaskar Patil

Abstract: Hydrogen has been promoted as a versatile clean energy vector capable of decarbonizing hard-to-abate industrial sectors. This paper critically reviews the technical, economic, and infrastructural factors that determine whether hydrogen can move from todays too much "hype" to a reality of fit-for-purpose energy solution for industry and the energy sector. We review production pathways (gray/blue/green hydrogen), electrolyser technologies, storage & transport challenges, and industrial use-cases (steel, chemicals, refineries, and high-temperature heat). Key findings: (1) green hydrogen currently remains significantly more expensive than fossil-derived hydrogen, though projections indicate cost declines with scale and deployment; (2) hydrogen is likely to be most viable where direct electrification is infeasible (high-temperature heat, feedstock); (3) infrastructure and implementation gaps are significant and require policy, supply-chain, and finance coordination. The paper concludes with fit-for-purpose deployment pathways and policy recommendations to enable industrial adoption.

DOI: https://doi.org/10.5281/zenodo.17173815

 

Published by: vikaspatanker

Authors: Neelima Kasarla, Praneeth Kumar Kandukuri, TakeYadav Yerragolla, Srikanth Pandula

Abstract: The demand for digital content is growing quickly. Finding skilled creators is now a challenge. SnapHire is a platform that simplifies the hiring process for photographers, video editors, and reel makers. It has a simple interface. Clients can view portfolios, check skills, see availability in real time, and book services right away.This reduces delays compared to traditional hiring methods. Creators gain better visibility and can handle client requests more easily. Businesses and individ- uals can rely on skilled professionals. SnapHire is built to grow with tools that improve workflow and user experience. The platform emphasizes speed, clarity, and reliability. It connects clients and creators effectively. SnapHire offers high-quality content for marketing, branding, social media, and personal events like weddings and parties. It makes booking simple, increases creator visibility, and improves communication. This method offers a smooth and dependable experience for clients and professionals.

DOI: https://doi.org/10.5281/zenodo.17173653

 

Published by: vikaspatanker