Category Archives: Uncategorized

Authors: Varshini J S, Praveen, Keshav Acharya. P, Rakesh, Rohit. S

Abstract: This work presents a personal assistant robot designed to minimize human labor in daily activities. Operated by voice command, it features a camera, robotic arm, object detection, and distance measurement, making it suitable for various applications, including chemical industries and healthcare. This scoping review sought to comprehend individuals' experiences using humanoid robots to perform daily living activities. studies were studied, and nine robots to perform different tasks were identified. The majority of participants found the robots safe and convenient but didn't like their size and slowness. Others found the robots fascinating but not appropriate for domestic use. The results indicate the necessity of tailored research to enhance the performance of humanoid robots in health care.

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Authors: Vanaja Kumari Degala

Abstract: Phishing attacks continue to pose a serious cybersecurity threat by exploiting social engineering techniques to deceive users into disclosing sensitive information. These attacks commonly rely on malicious Uniform Resource Locators (URLs), often hosted on newly registered or parked domains to evade traditional blacklist-based detection systems. Early identification of such URLs is essential to reduce financial losses and identity theft. This paper presents a machine learning–based framework for the early detection of malicious URLs, with particular emphasis on newly registered and parked domains. A dataset comprising 211,659 URLs was constructed using real-time SSL certificate monitoring, popular domain listings, and verified phishing reports. The proposed approach incorporates data preprocessing, URL-based feature extraction, class balancing, and model optimization. Experimental results demonstrate that the Light Gradient Boosting Machine (LGBM) classifier achieves a recall of 96.02% and an accuracy of 97.28% using 10-fold cross-validation. Feature selection techniques further reduce model complexity while maintaining detection performance, enabling practical deployment. The framework provides a proactive and scalable solution for phishing prevention and brand protection in sectors such as banking and e-commerce.

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Authors: Bhavesh Vallepu

Abstract: Artificial Intelligence (AI) is transforming the global job market, reshaping industries, and redefining the nature of work. This paper explores the multifaceted impact of AI on employment, highlighting both the opportunities and challenges it presents. While AI drives efficiency and innovation, leading to the creation of new job categories and business models, it also poses a threat to certain traditional roles through automation and displacement. The analysis considers various sectors, skill levels, and geographical regions, emphasizing the need for adaptive education systems, upskilling, and policy intervention to manage the transition. By examining current trends and future projections, this study aims to provide a balanced perspective on how AI will influence employment dynamics in the years to come

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Authors: Ravichandra Mulpuri

Abstract: The growing demand for personalized, efficient, and secure customer interactions has accelerated the adoption of AI-enhanced Salesforce bots across industries. These bots integrate natural language processing, machine learning, and CRM intelligence to streamline engagement while adapting to user needs in real time. Their deployment in hybrid Unix environments provides enterprises with a balance of stability, scalability, and flexibility. However, ensuring compliance with global regulations such as GDPR, HIPAA, and PCI DSS remains a central challenge. This review explores the role of AI-powered Salesforce bots in enhancing customer experiences, examines compliance strategies within hybrid Unix systems, and highlights ethical, operational, and organizational considerations. Future directions emphasize the importance of compliance-by-design, secure integration, and industry-specific applications, positioning AI-driven bots as transformative tools in enterprise digital ecosystems.

DOI: http://doi.org/10.5281/zenodo.17189840

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Authors: Abdullah Tariq

Abstract: The evolution of iOS application development has witnessed a significant shift from traditional monolithic architectures to more sophisticated patterns that balance modularity with performance. This paper examines the concept of modular monoliths in large-scale iOS applications, exploring how this architectural pattern addresses the dual challenges of code reusability and runtime performance. Through analysis of implementation strategies, performance metrics, and real-world case studies, we demonstrate that modular monoliths offer a pragmatic middle ground between rigid monoliths and complex microservices architectures. Our findings suggest that when properly implemented, modular monoliths can achieve up to 40% better build times, 25% improved memory efficiency, and significantly enhanced developer productivity while maintaining the deployment simplicity of monolithic applications.

DOI: https://zenodo.org/records/17183546

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Authors: Sangani Harshil, Kalariya Meet, Baraiya Ravi, Vasani Bhumil, Dr. Vikram B.Kaushik

Abstract: The ability to automatically describe visual content through natural language represents a compelling frontier in artificial intelligence research. Our work addresses this complex challenge by developing a sophisticated neural architecture that translates visual information into coherent textual descriptions. The methodology we employed centers on a two-stage approach: initially, we leverage the robust feature extraction capabilities of InceptionV3, a well-established convolutional neural network, to visual elements present in uploaded images. The extracted visual representations then feed into our custom language generation pipeline, built around a Gated Recurrent Unit (GRU) architec- ture. What distinguishes our implementation is the incorporation of a spatial attention module that enables selective focus across different image regions during the caption formation process. This attention-driven approach mirrors human visual processing, where we naturally emphasize certain areas while describing a scene. To validate the practical utility of our research, we constructed an intuitive web-based platform using Streamlit framework. This interactive system allows users to seamlessly up- load photographs and receive instantaneous caption generation, enhanced with audio narration capabilities through integrated speech synthesis technology.

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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

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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.

 

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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

 

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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

 

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