Volume 11 | Issue 11 |

 Abstract

Vehicle counting, classification, and detection have become essential tasks in modern transportation systems and surveillance applications. This abstract presents a comprehensive overview of these critical functions using OpenCV, a popular computer vision library. Vehicle counting involves tracking and tallying the number of vehicles passing through a specific region. OpenCV provides tools for frame differencing, contour detection, and object tracking, allowing for real-time and accurate vehicle counting in various scenarios. Classification goes a step further by categorizing vehicles into different classes, such as cars, trucks, or motorcycles. OpenCV can leverage deep learning models like Convolutional Neural Networks (CNNs) to achieve high-precision vehicle classification based on image features. Vehicle detection, on the other hand, identifies and localizes vehicles within an image or video stream. OpenCV offers pre-trained models like Haar Cascades and YOLO(You Only Look Once) for robust vehicle detection.

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 Keywords

object detection, YOLO, OpenCV, CNN ,

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Daruharidra, also known as Berberis aristata, is a medicinal plant native to the Himalayas and widely distributed across India. It has been used for centuries in traditional Ayurvedic medicine for its various therapeutic properties. In Ayurveda, Daruharidra is considered a bitter herb with potent medicinal properties. It is primarily used to support digestive health and treat gastrointestinal disorders such as diarrhoea, dysentery and indigestion. Additionally, it is believed to possess antimicrobial, anti-inflammatory, antioxidant, and hepatoprotective properties. Phytochemical analysis of Daruharidra has revealed the presence of various bioactive compounds, including alkaloids (berberine, palmatine), flavonoids (quercetin, isoquercetin), lignans, tannins, and essential oils. Daruharidra valuable medicinal plant in traditional Ayurvedic medicine. Although further research is needed to validate its traditional uses and explore its full potential.

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Daruharidra, Berberine, Hepatoprotective, Anti-inflammatory, Liver diseases

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Leila Aboulela, who perceives herself as a 'silent writer' is a Scottish-based Sudanese-origin fictional writer, playwright, and essayist whose insightful narratives are themed on identity, migration, Muslim culture, and superposed East and the West. This study critically examines Leila Aboulela's strategic essentialism against the backdrop of colonial continuance and globalization. It further focuses on how Africa needs liberation from both guilt and gratitude through the characters who have migrated to England in Leila Aboulela's short stories Missing Out, The Ostrich, and Doctor on the Nile. Migrants in the West often encounter 'colonial subjugation' and at same time are culturally displaced from their own space. In this realm, Leila Aboulela's illustration of Khartoum can be studied in the light of Salman Rushdie's conceptualization of 'imaginary truth'. This research predominantly analyzes the perplexity of cultural praxis through Homi Bhabha's concepts of 'mimicry' and 'hybridity' in the context of Leila Aboulela's alterity in these selected works.

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 Keywords

Colonialism, Khartoum, Leila Aboulela, Migration, Mimcry, Orientalism

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Artificial Intelligence has emerged as a dynamic force reshaping business operation across diverse industries. It encompasses a broad spectrum of applications, from automation and data analytics to personalization and customer service. AI's growing significance in business value creation is undeniable. Organizations are increasingly relying on AI to gain a competitive edge and enhance their operations. However, despite substantial investments in time, effort, and resources, many AI initiatives end in failure due to a lack of comprehensive understanding of how AI technologies can create tangible business value. To address this knowledge gap, this paper presents a narrative review that identifies how organizations can effectively deploy AI and the mechanisms through which AI generates value. This review delves into the multifaceted influence of AI on businesses, discussing its implications for research, innovation, market deployment, and the evolving landscape of business models. Drawing insights from Neo-Schumpeterian economics, we explore the pivotal roles of innovation, knowledge, and entrepreneurship in AI-driven business transformations. The research model employed offers a three-dimensional perspective that navigates through the significant dimensions of AI's impact. As AI continues to evolve, businesses must navigate ethical, regulatory, and workforce-related considerations. This review underscores the evolving dynamics of AI in the business domain and its transformative potential in an increasingly AI-driven world.

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 Keywords

Business Models, Automation, Robotic Process Automation, Robotic Process Automation, Artificial Intelligence

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The quest for alternative paradigms aimed at expanding epistemic knowledge while analyzing political challenges in the twenty-first century is motivated by limitations inherent in classic orientations of political phenomenon analysis. Modern philosophers have embraced behavioralist techniques in opposition to institutionalist, pluralist, and elitist approaches that are now in use. These approaches have the potential to elevate the empirical status of knowledge in modern political analysis. This paper's topic was examined through an examination of all available literature, debates, and archive sources using the conventional philosophical technique of analysis. In order to pinpoint the advantage that the behavioral method provides modern political scientists, the research objectively assesses the majority of the accusations leveled against it. The study comes to the conclusion that not all of the approach's examples are bad in spite of these objections. We are all becoming behaviorists as a result of the new ideas, advanced analytical tools, and mathematical models that behaviorism has given out.

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Behaviouralists, Behaviouralism, Contemporary, Elitists, Empirical, Pluralists, Paradigms

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This paper aims to develop a system for automated classification of rotten fruits using artificial intelligence (AI) and integrate it with a mechanical arm to segregate. The system will use Deep Learning techniques to analyze the images of fruits and identify the ones that are rotten based on their color, texture, and other visual cues. The data set of images of fruits, both fresh and rotten will be used for training. Once the system is trained, tested and validated it can be used in identifying the freshness of any new fruit given as input to the system. The fruits will be placed before the camera one after the other. The Trained AI Model will classify the images captured by the camera and identify the ones that are rotten. Once the rotten fruit is identified the robotic arm will be activated to separate the rotten fruit from the others.

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 Keywords

AI, CNN, Image Acquisition, Deep learning, Arduino.

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Industrial Relations (IR) comprise relationships between the state on one hand and the employer's and employee's organisation on the other, and the relationship among the occupational organizations themselves. Industrial relations are outcomes of employment relationships in an industrial enterprise.

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Industry, Relations

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In the 21st century, as new and electronic media expand, the relevance of traditional folk media in promoting health, education, tourism, and women's issues in rural areas of developing countries is a subject of inquiry. Despite the appeal of modern media, traditional folk media remains significant due to its strong connection with audiences based on religious, cultural, and linguistic affinities. These forms of art resonate with the population through shared beliefs, folklore, fables, and cultural requirements. They effectively convey information and education while fostering acceptance of scientific and developmental changes in rural communities, which can be resistant to change. India, with its rich heritage of folk media, still showcases these traditions during rural festivities, offering a platform for communicating informative and educational content. The paper underscores the immense potential of traditional folk media in development communication and calls for greater emphasis on their role in promoting health, education, tourism, and women's issues.

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Folk Media, Communication, Information, Population

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The advent of data-driven methodologies has revolutionized the field of materials science, particularly in the analysis and optimization of geopolymer materials. Geopolymers, known for their superior mechanical properties and environmental sustainability, have garnered significant attention in various industrial applications. This comprehensive review delves into the multifaceted role of data science in geopolymer research, emphasizing its contribution to the advancement of material properties, performance, and application domains. We systematically explore the literature, focusing on the integration of data analytics, machine learning, and computational modeling in understanding and predicting the behavior of geopolymer composites. A detailed synthesis of findings from selected studies highlights the efficiency of data-driven techniques in enhancing the structural integrity and durability of geopolymers. The review identifies prevailing trends, pinpoints gaps in current research, and discusses the challenges encountered in data acquisition and analysis. Furthermore, we propose future research directions that promise to foster innovation and optimization in geopolymer technology. By encapsulating the state-of-the-art in data-driven geopolymer analysis, this review aims to serve as a pivotal resource for researchers and industry practitioners alike, aspiring to harness the power of data science in material development and application.

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Geopolymer Materials, Data Science, Machine Learning, Material Analysis, Data Analytics, Computational Modeling, Material Optimization, Structural Integrity.

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Heart disease remains a pervasive global health concern, maintaining its status as a primary cause of mortality. Early detection is pivotal for effective intervention strategies. This research leverages the Random Forest algorithm to predict the risk of heart disease, utilizing a comprehensive dataset encompassing patient attributes such as age, gender, cholesterol levels, blood pressure, and other pertinent clinical features. Renowned for its robustness and accuracy, the Random Forest model undergoes training on historical data to discern intricate patterns and relationships within the dataset. Through a meticulous evaluation process, we showcase the model's proficiency in accurately categorizing individuals into heart disease risk groups. This underscores its significance as a valuable tool for healthcare professionals in the assessment of patient cardiovascular health. This study represents a meaningful contribution to the ongoing endeavors aimed at harnessing machine learning to enhance heart disease prevention and elevate the standard of patient care.

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 Keywords

Machine Learning, Random Forest Algorithm, Risk Prediction and Accuracy

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Creative Commons Attribution 4.0 and The Open Definition