Volume 13 | Issue 3 |

 Abstract

Early and accurate diagnosis is crucial for effective management and intervention of Down syndrome. By leveraging advanced learning techniques, the goal is to create a model that can accurately differentiate between children with Down syndrome and those without, based on facial characteristics. Traditional diagnostic methods rely on genetic testing and clinical evaluation, which can be invasive and time-consuming. Existing automated diagnostic tools often lack the necessary accuracy and computational efficiency. Down syndrome primarily involve invasive procedures such as amniocentesis and chorionic villus sampling, which carry risks for both mother and child. This aims to develop a non-invasive, efficient, and accurate diagnostic tool for Down syndrome detection using facial images. We utilized a learning approach, combining with learning from the VNLNet architecture for feature extraction. A dataset of facial images, labeled for Down syndrome diagnosis, was processed and augmented using Tensor Flow. The model was trained to extract relevant features from the images. Extracted features were then fed into a Support Vector Machine (SVM) classifier to perform the final diagnosis. Using pre-trained models, such VNLNet or other effective feature extraction networks, this method extracts relevant features from children's faces. After uploading facial photographs, the system runs them through a feature extraction pipeline and uses an advanced classification algorithm to produce diagnostic results along with confidence scores.

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 Keywords

Down Syndrome,VNLNet,Support Vector Machine,Non-Invasive,Tensorflow

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Visually impaired patients can enhance navigation and environmental perception by using the "Third Eye" as an assistive device worn on their palms. Navigation capabilities and environmental perception for individuals with visual impairments, the technology focuses on addressing an essential need for time-sensitive feedback tasks that exceed basic assistive technologies. By such a device merges various features including obstacle sensors while providing object recognition and text interpretation functionalities without any disruption. Through this device users gain better confidence and independence in their environmental interactions. The Operational mode changes in this system are controlled through voice commands which function automatically. The user interface provides easy control functions to match different user requirements. The audio systems deliver feedback information through earpieces in private manner. Users can obtain real-time obstructive information through wireless headphones which deliver precise data regarding discovered threats. The system provides precise obstacle detection alongside object recognition of identified items and text reading abilities and records all user interactions subsequent analysis and system refinement. The initial version of the system exhibits portability features together with straightforward operation requirements. The testing resulted in excellent functionality which enables the system to detect obstacles across 5 feet of distance. The system shows capability to correctly identify everyday items as well as reading stable text connectivity conditions. A cost-effective solution based on embedded artificial intelligence operates in this system. Embeddings enable the development of an innovative tool that transforms the experiences of visually impaired users while eliminating their dependency on external help assistance and enhancing daily mobility. The future development path includes adding sophisticated features to the system. The system should integrate advanced voice recognition functions and detect more objects to enhance its effectiveness, utility and impact.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Inteligence, camera, Object Detection, Obstacle, NPU

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

E-commerce platforms are increasingly leveraging personalized recommendations to provide a more tailored shopping experience for their users. Conventional recommendation methods are usually based on collaborative filtering, meaning that they depend on behavioral data and the similarity of users. Still, they often need to realize individual preferences and individual tastes. Recently, generative models have shown great promise for personalized recommendations, especially in the e-commerce context. Generative models sample data points according to a distribution; as such, generative models learn to represent the underlying distribution of the user's rows. By harnessing data from various sources, businesses can create personalized recommendations that are more relevant than broad recommendations while also factoring in individual interests, preferences, purchase history, and so on. Generative models can learn and improve iteratively, learning on an ongoing basis and providing more accurate suggestions for users. Generative models have been used to provide personalized recommendations in e-commerce, which have been shown to be effective in driving engagement and boosting sales. These models help improve customer satisfaction and loyalty by recommending more relevant and personalized content. They can also serve to distinguish e-commerce platforms in a crowded marketplace and enhance users shopping experiences. Generative models were trained on data that looked solid goals for user-friendly e-commerce with personalized recommendations. As these models continue to improve, we can only expect to see more enhanced experiences in e-commerce in the near future.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

E-Commerce, Data, Preferences, Accurate, Sales, User-Friendly

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 Abstract

A Journey to the Home Town with Family And Meeting With Friends.

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 Keywords

Introduction ,Main body ,Conclusion

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The integration of AI into assistive technologies is revolutionizing accessibility for persons with disabilities (PwDs) by addressing key barriers outlined in Article 9 of the UNCRPD. This article critically examines the dual impact of AI, highlighting its potential to enhance mobility, communication, and digital access while also addressing the complex challenges that arise in its development and implementation. Issues such as algorithmic bias, data privacy risks, and the digital divide present significant concerns that may limit equitable access to AI-driven solutions. Through real-world case studies, the article explores how AI-powered assistive technologies can be aligned with UNCRPD principles of inclusivity, non-discrimination, and universal design to ensure broader accessibility. Additionally, it presents policy recommendations aimed at strengthening regulatory frameworks, promoting inclusive AI design, and fostering multi-stakeholder collaboration. By addressing these challenges proactively, AI can serve as a powerful tool for enhancing accessibility rights and contributing to a more equitable and inclusive society.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Accessibility Rights, Inclusivity, Artificial Intelligence (AI), Assistive Technologies (AT) UNCRPD

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study explores the impact of implementing Just-In-Time (JIT) inventory systems on customer satisfaction and operational efficiency within organizations. The research is guided by four primary objectives: analyzing the influence of JIT inventory on customer satisfaction, assessing its effect on operational efficiency, examining the correlation between customer satisfaction, operational efficiency and JIT inventory. The findings of the study reveal notable differences based on organizational size and employee experience. Smaller organizations tend to report higher levels of customer satisfaction and operational efficiency following the implementation of JIT practices. In contrast, larger organizations encounter more challenges, with greater variability in JIT performance and lower overall satisfaction. Employees with less experience report higher satisfaction and efficiency, whereas more experienced staff show lower satisfaction and more diverse views on JIT practices. The study underscores the need for tailored JIT strategies that consider organizational size and employee experience to optimize performance. Regression analysis confirms a significant positive impact of customer satisfaction and operational efficiency on JIT implementation, highlighting the potential for substantial improvements in inventory management and overall organizational performance. The reliability of these findings is supported by high internal consistency in the measurement scales, emphasizing the interconnectedness of customer satisfaction, operational efficiency, and JIT practices. The research sample includes 53 participants, providing insights into the practical implications of JIT inventory systems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Operational Efficiency, Just in time, Customer Satisfaction, Inventory System, Service Quality

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Foundational Design Studio is an important first stepping stone for first-year architecture students, giving them a broad introduction to basic design principles, such as but not limited to form, space, order, and human scale. This study explores the importance of including hands-on exercises in the education of fundamental architectural concepts, particularly highlighting the investigation of forms and grids. Through the formal workshop framework, students become actively engaged with exercises that incorporate exploration of spatiality, anthropometry, and design fundamentals in the detailed creation and analysis of a 2m x 2m x 2m grid. Through shared exercise, learners can analyze how different activities affect space arrangements, which culminate in the development of scaled models that facilitate the understanding of form transformation from basic geometric forms to more organic and fluid ones. Through this detailed exploration, the central function of grids in structuring space is revealed, enabling students to contribute to issues of proportions, modularity, and the interaction of people with engineered spaces.The ongoing process of modeling and experimentation works to advance students' understanding of architectural visualization methods and the dynamic interaction of space. In addition, the results clearly prove that interactive learning experiences through experiential exercises promote a greater degree of critical thinking, creativity, and innate understanding of spatial relationships than is possible through more conventional lecture-driven instruction. Technically, the final results of this research accentuate the extremely valuable gain produced by implementing practical workshops in architectural curricula because it allows students to develop their skills in imagining, manipulating, and perfecting design ideas with unprecedented effectiveness.

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 Keywords

forms and grids, hands on workshop, basic forms, fluid forms, activities

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Carnatic music, one of India's most profound classical traditions, has been historically linked to emotional well-being and therapeutic applications. Recent research highlights its potential role in music therapy, particularly in reducing stress, enhancing mental clarity, and promoting relaxation. This study explores the scientific basis of Carnatic music therapy, focusing on the emotional and physiological effects of specific ragas. Through computational analysis using time and frequency domain techniques, we examine how different raga structures interact with neural and psychological processes. The findings demonstrate that certain ragas have measurable benefits in mental health applications, paving the way for AI-driven personalized music therapy solutions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Carnatic music, Raga therapy, Music informatics, Signal processing, Machine learning, Wellness applications

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The adoption of artificial intelligence (AI) in customer service has led to the widespread use of chatbots, which have transformed the way businesses interact with customers. Chatbots are automated conversational agents designed to simulate human interactions, provide instant responses, and assist customers with inquiries. As businesses increasingly rely on digital communication channels, chatbots have emerged as a crucial tool for enhancing customer experience (CX) and fostering brand loyalty. This research paper explores the influence of chatbots on customer experience and brand loyalty, examining both their benefits and challenges. Chatbots significantly improve response time, availability, and service efficiency, ensuring customers receive 24/7 assistance without delays. By leveraging natural language processing (NLP) and machine learning (ML), chatbots personalize interactions, offering tailored responses and recommendations based on user preferences. This personalization enhances customer satisfaction, contributing to a seamless and engaging user experience. Furthermore, chatbots streamline customer support, automate repetitive tasks, and reduce operational costs, allowing businesses to allocate human agents to more complex issues that require empathy and critical thinking. From a brand loyalty perspective, chatbots help build stronger relationships between businesses and customers. A positive, efficient, and frictionless experience increases trust and encourages repeat interactions, leading to long-term brand commitment. Many businesses have observed a rise in customer retention and repeat purchases due to chatbot-assisted interactions, demonstrating their potential as a loyalty-building tool. However, despite these advantages, chatbots face certain limitations that can negatively impact customer satisfaction. Their inability to fully understand emotional nuances, occasional misinterpretation of complex queries, and lack of human empathy may lead to frustration among users. Additionally, over-reliance on chatbots without a seamless transition to human agents can result in a poor service experience, potentially harming brand perception. This study aims to evaluate the effectiveness of chatbots in enhancing customer experience and brand loyalty by analyzing their implementation across industries, assessing consumer responses, and identifying best practices for optimal chatbot integration. The paper also discusses key challenges and limitations of chatbot usage and provides strategic recommendations for businesses to improve chatbot performance. Balancing AI automation with human support is essential to maximizing the benefits of chatbots while minimizing potential drawbacks. Ultimately, while chatbots are revolutionizing customer service and engagement, businesses must continuously refine their AI capabilities, integrate human oversight, and ensure chatbots evolve to meet changing consumer expectations. By doing so, companies can leverage chatbots as a powerful customer relationship management tool that enhances user experience, strengthens brand loyalty, and drives sustainable business growth. Objective The primary objective of this study is to analyze the influence of chatbots on customer experience and brand loyaltyby assessing their effectiveness, advantages, and limitations in modern business environments. Chatbots have become a fundamental tool for automating customer service and improving engagement, but their impact on consumer behavior, satisfaction, and long-term brand relationships requires deeper examination. The specific objectives of this research are: 1. To assess the effectiveness of chatbots in improving customer experience * Analyze how chatbots enhance response times, service efficiency, and query resolution compared to traditional customer support channels. * Evaluate the role of artificial intelligence (AI) and natural language processing (NLP) in improving chatbot interactions. 2. To examine the role of chatbots in providing personalized customer interactions * Assess how chatbots use customer data and AI-driven insights to deliver tailored responses and recommendations. * Understand the impact of personalization on customer satisfaction and engagement. 3. To evaluate the impact of chatbots on brand loyalty and customer retention * Measure the extent to which chatbot-driven customer service contributes to repeat purchases and long-term engagement. * Analyze how chatbot interactions influence customer trust, perception, and overall brand image. 4. To identify potential challenges and limitations in chatbot implementation * Investigate common issues such as misinterpretation of complex queries, lack of emotional intelligence, and customer frustration with AI-driven interactions. * Explore the need for a hybrid customer support model that combines chatbot automation with human assistance. 5. To provide strategic recommendations for optimizing chatbot performance * Suggest ways to improve AI chatbot capabilities, including advancements in sentiment analysis, machine learning, and adaptive learning. * Recommend best practices for businesses to integrate chatbots effectively while maintaining a balance between automation and human touch. By addressing these objectives, this research aims to provide valuable insights into how businesses can maximize the benefits of chatbots while mitigating their limitations, ultimately enhancing customer experience and strengthening brand loyalty.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Chatbots, AI, Customer Experience, Brand Loyalty, Digital Engagement, NLP, Automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: The global push for sustainability is reshaping industries and revolutionizing consumer behavior. As awareness of climate change, pollution, and resource scarcity increases, the demand for sustainable products and services has skyrocketed. A sustainable brand, defined as one that integrates environmental, social, and economic considerations into its operations, provides consumers with an alternative to traditional products that often harm the environment or exploit labor forces. Consumer attitudes toward sustainable brands have evolved significantly over the years, driven by greater environmental awareness, social justice movements, and a growing sense of corporate accountability. This research delves into the factors influencing consumer attitudes toward sustainable brands, examines the underlying motivations driving sustainable consumption, and explores the challenges these brands face in aligning with consumer expectations..

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Consumer Attitude, Sustainable Brands, Comparison

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Creative Commons Attribution 4.0 and The Open Definition