Volume 12 | Issue 4 |

 Abstract

The sophisticated Python-based chatbot named as 'Government Infobot' represents a pinnacle of technological innovation, meticulously tailored to tackle the multifaceted challenges inherent in disseminating crucial information on government schemes. Placing paramount emphasis on user accessibility, the chatbot's intuitively designed User Interface offers a seamless and engaging experience for users across diverse demographics and technological proficiencies. Its robust architecture integrates state-of-the-art Natural Language Processing and machine learning algorithms, ensuring not only precision in responses but also adaptability to evolving user queries and needs. Moreover, the system's capability for real-time updates and eligibility verification not only enhances its reliability but also underscores its commitment to providing accurate and up-to-date information to citizens. By bridging the gap between citizens and government schemes, this innovative solution heralds a new era of transparent and accessible public service delivery, promising to redefine the landscape of governmental communication and engagement.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Machine Learning, Chatbot, Public Services, Python, Policies, Schemes

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The adoption of cloud computing within educational environments has become pivotal in enhancing not only learning experiences but also operational efficiency (Lopez & Gresham, 2021). Microsoft Azure, as a leading platform in this transformation, exemplifies the integration of cloud technologies in education. This paper delves into the various ways in which Azure is utilized in educational settings, highlighting its role in facilitating a more dynamic and accessible learning landscape. Through Azure, institutions are able to deploy and manage applications seamlessly, scale educational resources flexibly, and enhance collaborative learning across geographically dispersed students and faculty (Patel & Smith, 2022). The significance of Azure in education lies in its robust infrastructure which supports a wide range of educational tools and platforms that adapt to diverse learning and teaching needs. Furthermore, Azure's analytics services provide educators with critical insights into student engagement and learning outcomes, enabling more informed decisions and personalized learning pathways (Turner, 2023). The deployment of Azure in education not only democratizes student access to cutting-edge technologies but also optimizes institutional operations, reducing costs and improving administrative efficiencies. This paper explores these dimensions of Azure's impact, drawing on various case studies and academic evaluations. The integration of Azure into educational frameworks represents a transformative shift towards more agile and responsive educational environments, suggesting a broader trend of digital transformation in the sector.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Education, Azure, Cloud Computing

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

"LTA", "Photoluminescence", "PXRD", "Etching Ananlysis", "Optical Transperency", "Optoelectronical Devices"

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The study was conducted to investigate to see the "Influence of Play therapy in enhancing Motor Skills among Children with Intellectual Disabilities". The sample consists of children with moderate intellectual disability age ranging from 7-14 years. Size of the sample consist 10 subjects. They were randomly assigned into experimental and control groups. Each group consists of five subjects. For conducting the study, Pre-test Post-test Control Group Design was used. The students in the experimental group were taught Play Therapy with colourful materials and control group were taught through conventional method. The results indicated that there is a significant improvement in enhancing Gross Motor Skills and Fine Motor Skills. In addition, the rate of training among the students in experimental group was higher than the control group. From the results it is evident that the children who were taught through Play Therapy have achieved higher score than the children who were taught through conventional method

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Play Therapy, Gross Motor, Fine Motor, Moderate Intellectual Disability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The intersection of satellite imagery analysis and transmission line monitoring offers a transformative approach to vegetation management in power infrastructure. This study employs the YOLOv8 algorithm to detect and analyze trees encroaching upon transmission lines, leveraging the high-resolution capabilities of satellite imagery. By automating the identification process, this methodology enhances the efficiency and accuracy of vegetation monitoring, enabling proactive maintenance to mitigate potential risks to the transmission network. Through the integration of advanced technologies, such as machine learning and remote sensing, this research aims to optimize infrastructure resilience while ensuring uninterrupted power transmission. Furthermore, this abstracted approach not only streamlines vegetation management processes but also contributes to the overall reliability and safety of transmission infrastructure. By harnessing the power of satellite imagery and machine learning algorithms, utility companies can effectively identify and address vegetation encroachments in a timely manner, minimizing the risk of outages and enhancing system resilience. The proactive nature of this methodology enables utilities to prioritize maintenance efforts, allocate resources efficiently and ultimately improve the reliability of power transmission networks in the face of dynamic environmental challenges.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Transmission line, python, agriculture, machine learning, yolov8, HTML

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) stands as one of India's most significant social welfare initiatives, aiming to enhance rural livelihoods and prevent distress migration. However, the effectiveness of MGRREGA in curbing rural-to-urban migration remains a subject of debate and needs to be studied systematically. This paper tries to analyse the effectiveness of MGNREGA in controlling rural migration based on the review of the literature. It is found that MGNREGA has contributed positively to mitigating rural distress and reducing migration in certain contexts. There are some challenges in realizing its full potential.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

MGNREGA, rural migration, livelihoods, rural development, India

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Autism Spectrum Disorder (ASD) is a complex neuro developmental condition characterized by challenges in social interaction, communication, and repetitive behaviours. Early detection and intervention are crucial for improving outcomes and quality of life for individuals with ASD. In recent years, deep learning techniques have shown promising results in various medical applications, including disease prediction. Here, we have proposed a deep learning-based approach utilizing Alex Net to focus on feature selection and extraction techniques for the effective detection of ASD using Magnetic Resonance Imaging (MRI)data. At first, the MRI datasets are taken from kaggle.com are preprocessed and segmented. Then, an Alex Net architecture is designed and trained to effectively capture intricate patterns and relationships within the data for accurate prediction of ASD. The proposed deep learning model demonstrates superior performance compared to traditional machine learning methods and baseline models in ASD prediction. Through extensive experimentation and evaluation, the model achieves high accuracy in identifying individuals at risk of ASD. Future research directions may focus on refining the model architecture, incorporating multimodal data sources, and deploying the system in clinical settings to validate its efficacy and real-world utility.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Alex Net, Autism Spectrum Disorder (ASD) , Deep Learning, , Magnetic Resonance Imaging (MRI)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study was conducted to investigate learning first aid for burn management through video-based instruction among children with mild intellectual disability. The sample consists of children with mild intellectual disability age ranging from 11-14 years. Sample was drawn from "Skills and Ability School", Mulund West, Mumbai. Size of the sample consists 10 subjects. They were randomly assigned into experimental and control groups. Each group consists of five subjects. The students in the experimental group were taught first aid for burn management through Video Based Instruction and control group were taught through conventional method (using flash card and worksheets). Intervention was carried out for 15 sessions. The results indicate that there is a significant improvement in learning First aid for Burn Management. In addition, the rate of learning among the students in experimental group was higher than the control group. From the results it is evident that the children who were taught through Video have achieved higher score than the children who were taught through conventional method. Video had created interest, eagerness and motivation in children with mild Intellectual Disability.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

First Aid, Video-based Instruction, Intellectual Disability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lebanon is currently experiencing a multifaceted crisis that encompasses economic collapse, political instability, and social upheaval, posing significant challenges for the country's population and its future. This paper examines the root causes and impacts of the ongoing crisis, including the severe depreciation of the Lebanese pound, hyperinflation, and the breakdown of the banking sector. Additionally, the study explores Lebanon's sectarian political system, which has been marked by corruption and inefficiency, further hindering effective governance and reform. The paper also considers the humanitarian crisis stemming from widespread poverty, food insecurity, and lack of access to essential services. Moreover, the research assesses the impact of regional conflicts, particularly the Israel-Palestine conflict, on Lebanon's security and stability. By providing an in-depth analysis of the complex interplay between these issues, this paper aims to offer a comprehensive understanding of Lebanon's crisis and its implications for regional and international stakeholders. Possible pathways for recovery and potential areas for international assistance are also discussed, emphasizing the need for sustainable solutions to address the underlying causes of Lebanon's distress

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Humanitarian Law, Social Inrest, Economic Distress, Political Distress, Lebanon Crisis, Historical Implications

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Since collapse might harm public safety and the economy, building and construction structures must be maintained. Critical structures need periodic inspections to fix issues. Visual examination is tough and knowledge-intensive yet recommended. Better, more reliable monitoring is needed. The study uses high-frequency monitoring to discover structural problems low-frequency methods overlook. PZT patches detect and create signals. These patches can detect small structural behaviour changes that indicate problems. Check structural data against prior data to find research gaps. In this study, high-frequency EMI methods identify minute structural differences. We tested our methods in a scaled reinforced concrete lab. Computer models described observable phenomena for experimentation. Computational models anticipate damage and fracture propagation. Compared to empirical testing, predictive testing saves time and money. Computational modelling can assess structural typologies' environmental and fracture propagation mechanical responses. This study implies computational tools improve structural monitoring and construction engineering.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

: Structural health monitoring, Smart structures, Sensor technology, Numerical Simulation, Automated inspection.

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Creative Commons Attribution 4.0 and The Open Definition