Volume 13 | Issue 11 |

 Abstract

Fundamental building blocks in the field of autonomous vehicle perception, which has seen significant progress in recent years, are object detection and classification. These innovations are essential for giving cars the ability to sense and comprehend their environment, which promotes safe navigation and well-informed decision-making. Even with these advancements, present-day perception algorithms sometimes struggle to remain dependable under harsh weather situations like dimly lit rooms, intense sunlight, reflecting glare, or obscuring fog. These circumstances have the potential to seriously impair perception system performance, raising safety concerns. As we move closer to the objective of complete autonomy, it is critical that we create a robust perception system--a system that can confidently manage these various and demanding external circumstances. With the help of such a system, autonomous cars will be able to function consistently safely and precisely, even in unpredictable real-world situations. Achieving this degree of perception system complexity is not just a goal, but also a prerequisite for the development of fully autonomous cars that have the potential to completely transform the way we travel. Our study uses the CNN method, which is a powerful tool for deep learning, to take on this difficulty head-on. The CNN is being trained to be exceptionally good at identifying cars in bad circumstances, such as fog and rain. The objective is to provide our system with the capacity to surpass the constraints of hazy images and accomplish high-fidelity, real-time vehicle recognition. This results in a notable advancement in AV safety, guaranteeing their ability to confidently traverse even the most difficult weather situations. We're laying the groundwork for a time when autonomous cars will be able to drive on roads with greater safety and efficiency in all weather conditions by fusing the capabilities of deep learning and WDS. In this research, we are detecting vehicles in inclement weather by utilizing an algorithm based on YOLO.

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 Keywords

Object Detection; Vehicle Detection; YOLO; Deep Learning; Adverse Weather Conditions; Weather-Degraded Images; Image Enhancement; Intelligent Transportation Systems (ITS); Autonomous Vehicles; Robust Perception; Transfer Learning; Real-Time Detection; CNN-Based Models; Visibility Restoration; Performance Benchmarking.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents an AI-driven dermatology application designed to assist users in identifying skin diseases through image-based analysis. By leveraging advanced algorithms, the app provides accurate diagnostic predictions along with relevant educational content and preliminary treatment suggestions. A key feature is the integrated Severity Analysis and Risk Assessment module, which categorizes conditions as mild, moderate, or severe and evaluates personalized risk based on factors such as skin type, age, medical history, and UV exposure. This enhances clinical relevance and supports better decision-making for users, especially in underserved regions with limited access to dermatologists. Overall, the system aims to bridge the gap between technology and professional skincare by offering a reliable, accessible tool for early detection and monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-driven dermatology, Skin disease detection, Image-based analysis, Severity assessment, Risk evaluation, Early diagnosis, Personalized skin health monitoring

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Study of social issues in the present times

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Creative Commons Attribution 4.0 and The Open Definition

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Study of social issues in the present times

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Music has long been understood as a powerful medium capable of influencing human behaviour, perception, and development. In teacher education, its value extends beyond artistic appreciation, becoming a multidimensional tool that strengthens emotional intelligence, cognitive functioning, and social competence among student teachers. This paper provides an extensive scholarly analysis of the role of music in shaping the holistic readiness of teacher trainees, who must navigate demanding academic environments, interpersonal classroom relationships, and complex pedagogical responsibilities. Drawing from psychological theories, educational frameworks, and neuroscientific findings, the study employs a qualitative descriptive methodology to investigate how structured musical engagement impacts self-regulation, memory processes, communicative ability, empathy, collaboration, cultural sensitivity, and professional identity formation. The discussion demonstrates that music enhances the internal and external capacities required of future educators and supports the transformation of student teachers into well-rounded, emotionally grounded, intellectually alert, and socially responsive professionals. Recommendations include curriculum integration, professional awareness programmes, pedagogical innovations, and research-intensive music modules within teacher education institutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Music Education, Emotional Intelligence, Cognitive Skills, Social Development, Teacher Training, Neuroscience of Music, Pedagogical Competence, Holistic Development

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tooth autotransplantation, the surgical relocation of a tooth from one site to another within the same individual, is a biologically sound and effective treatment for tooth replacement, especially in young patients. This procedure offers significant advantages, including the preservation of alveolar bone, natural aesthetics, and the maintenance of proprioception. Success depends on key factors: selecting a donor tooth with incomplete root formation, meticulous surgical handling to preserve the periodontal ligament (PDL), and minimizing extraoral time. Technological advancements like cone-beam computed tomography (CBCT) and 3D-printed replicas have refined preoperative planning and surgical execution, reducing trauma and improving outcomes. Reported success rates range from 75% to over 95% with proper technique. While potential complications exist, autotransplantation remains a predictable and cost-effective alternative to conventional prosthetic options and implants, offering a natural and functional solution for missing teeth.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autotransplantation; Periodontal ligament; Tooth replacement; Pulp revascularization; Dental surgery

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the modern digital era, one of the greatest challenges faced by the primary education system is learners drifting away from academic engagement. The influence of mobile phones, unrestricted internet access, short video formats, reels, and algorithm-based content consumption has created a generation of easily distracted learners. Instead of engaging with textbooks and classroom-based interaction, children are increasingly drawn into fast-paced digital stimuli that reduce sustained attention span and weaken deep cognitive processing.

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 Keywords

Primary Teacher Training, Child Development, Learning Engagement, Attention and Concentration, Digital Distraction, Screen Addiction, Play-Based Learning, Activity-Based Learning (ABL), Storytelling Pedagogy, Positive Reinforcement,

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigated primary school teachers' attitudes toward six classroom strategies that enable inclusive education: cooperative group teaching, peer teaching and peer influences, social skills training, parental involvement, self-regulated learning, and collaborative teaching. A survey of Karnataka teachers (N = 396) using a 22-item Likert subscale showed uniformly positive attitudes (M = 3.47-3.52) with high reliability (? = .93). Exploratory factor analysis supported a four-factor solution, and an 18-item refined scale maintained strong reliability (? = .92) and explained 60.73% variance. Gender differences were trivial, but urban teachers showed more favorable views of cooperative group teaching, social skills training, and parental involvement (d ? .21-.29). No differences emerged by age, qualification, or education division. All domains were strongly correlated, as teachers viewed these strategies as interconnected. Regression showed a small effect of teaching experience and urban location. Overall, positive and coherent attitudes highlight the need for school-based professional development and improved rural resources to support inclusive practices.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inclusive Education; Teacher Attitudes; Primary Education; Strategies; Karnataka

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study introduces a web-based intelligent platform designed to automatically identify, analyse, and estimate the severity of vehicle exterior damage from uploaded images. The proposed system uses a multi-stage deep learning pipeline that combines instance segmentation and regression models to detect damaged vehicle parts and calculate the corresponding repair cost. First, a segmentation network, trained on vehicle-damage datasets, isolates the affected regions and determines the damaged components. The extracted visual information, along with vehicle metadata and up-to-date cost parameters, is then processed by an regression model to predict both the severity category classified as Minor, Moderate, or Severe and an estimated repair cost. Experimental results on a curated dataset demonstrate high localization accuracy and low prediction error, confirming the system's capability to accelerate insurance claim processing and improve transparency in automotive damage assessment.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Deep Learning, Computer vision, Automated Damage Localization, Cost Estimation , Damage Severity Classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This review evaluates current research trends and regulatory challenges, highlighting gaps in knowledge and suggesting future directions for responsible nootropic use.

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Creative Commons Attribution 4.0 and The Open Definition

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Nootropics, Smart Drugs, Cognitive Enhancement, Modafinil, Memory Boosters, Neuroenhancement, Brain Supplements

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study was conducted in selected rural area about occupational dermatitis.The period of data collection was 4 weeks.A sample of 50 male and 50 female agricultural workers were selected from the Banandur,Baredoddi,Chatra villages coming under the Ittamadu PHC by non probability convenient sampling technique. The researcher initially established rapport with the study samples and assured confidentiality and purpose of the study was explained to samples. The data was collected using semi structured questionnaire to find out the knowledge about the occupational dermatitis. An information booklet was given to the participants and their relatives.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Occupational Dermatitis, Male & Female Agricultural workers, Rural area

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Creative Commons Attribution 4.0 and The Open Definition