Volume 12 | Issue 4 |

 Abstract

Abstract:The study aims to assess awareness, knowledge, and attitudes regarding physiotherapy among pregnant women in Gujarat's Mehsana District. It includes 60 educated pregnant women aged 18-35, excluding those with major neurological issues. Results show most respondents were graduates (58.3%) and unemployed (76.7%). Only 33.3% had sufficient physiotherapy knowledge. Attitudes varied, with 38.6% positive, 49.8% unsure, and 11.7% negative. While 93% were aware of physiotherapy, only 21% knew about antenatal exercises. Understanding of physiotherapy's significance for antenatal care was low, with only 36.7% believing they understood it

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 Keywords

Physiotherapy, antenatal care, knowledge, awareness, attitude.

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Dantrolene sodium, Forced degradation, RP-HPLC, method validation.

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For the purpose of early diagnosis and treatment planning, medical imaging plays a critical role in the detection of pneumonia. While convolutional neural networks (CNNs) and many deep learning algorithms have shown impressive results in medical image processing applications, there are still major hurdles associated with the computational load of training large-scale CNN models on big datasets. In this paper, we use the distributed computing framework PySpark to offer a unique method for CNN models' distributed training for pneumonia diagnosis. By distributing the training process across several nodes in a Spark cluster and utilizing PySpark's parallel processing capabilities, we can effectively utilize computing resources and handle big datasets thanks to its scalability. Our approach entails preprocessing the data, developing the CNN model using deep learning frameworks like TensorFlow and training the CNN model across a distributed system using Pyspark. This paper contributes to the growing body of research on distributed deep learning and its applications in medical image analysis, providing insights and guidelines for leveraging PySpark for large-scale CNN training tasks.

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Convolution Neural Networks, Pneumonia, Distributed deep learning,TensorFlow, X-ray Image, PySpark

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This study examined the relationship between interpersonal intelligence and mental health among prospective teachers. A survey was conducted with a sample of 250 student teachers selected through simple random sampling from various teacher education institutions. The Interpersonal Intelligence Scale and Mental Health Inventory were used to measure the variables. Multiple regression analysis was performed to test the hypotheses based on gender, locality, pedagogy subject, age, and type of institution. The findings revealed a significant positive correlation between interpersonal intelligence and mental health. Gender, locality, and pedagogy subject were found to moderate the relationship, while age and institution type did not have a significant effect. The study highlights the importance of developing interpersonal skills to promote mental well-being among future teachers.

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Interpersonal Intelligence, Mental Health, Prospective Teachers.

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Compliance with Tax Deducted at Source (TDS) offers taxpayers a complex environment that includes both opportunities and challenges related to tax administration. The complexities of TDS compliance are explored in this abstract, along with the opportunities and challenges that taxpayers must overcome. Overcoming complicated tax legislation, carrying administrative responsibilities, guaranteeing correctness in deduction and remittance, and adjusting to regular regulatory changes are among the difficulties. On the other hand, there are opportunities for taxpayers within these issues, like encouraging tax compliance, giving governments a reliable source of funding, streamlining the tax collecting process, and improving financial transactions' transparency and accountability. Taxpayers may help create a more just and efficient tax system that promotes economic growth by addressing these issues and taking use of the opportunities provided by TDS compliance.

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Complexities, Regulatory changes, Government

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Smart Crop System is web application that integrates several machine learning algorithms with recommendation systems. It mainly includes two main modules Crop Data Analytics [CDA] and Crop Recommendation System [CDS]. It seeks to offer a range of solutions to the issues the agriculture sector faces. The web application provides a number of options, such as Crop seasonal analytics, crop detection, and disease detection. Recommendation solutions include techniques such as regression, whereas detection solutions involve image pre-processing stages including contour detection, Normalization, and CNN model. Using resources from OpenCV and Open AI, this system seeks to reduce the amount of time needed for crop diagnostics and crop recommendations for the upcoming seasons.

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CDA, CRS, OpenCV, Open AI, Normalization.

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The current study's findings show that many phytochemicals, including polyphenolic components such alkaloids, flavonoids, tannins, steroids, and phenols, resins, and carbohydrates, are present in the ethanolic extract of Trachysprmum ammi bark. Ethnobotanical and traditional uses of natural substances, particularly those derived from plants, have drawn a lot of attention recently as they have been scientifically proven to be safe for human use and to be beneficial. When looking for novel compounds to treat a variety of illnesses, this traditional method works the best. A thorough review of the literature on Trachyspermum ammi revealed that traditional healers and members of different ethnic groups frequently use it as a therapy for a variety of illnesses. Based on the results, we can conclude that the ethanolic extract of Trachyspermum ammi bark can be attributed to Anti-oxidant activity and Anti-ulcer activity. However, the isolation of active constituents from the ethanolic extract of Trachyspermum ammi bark species and its action responsible for its Anti-oxidant activity and Anti-ulcer activity effect can be useful for the treatment of the same in future.

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Trachyspermum ammi, Anti-oxidant activity and Anti-ulcer activity.

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The IoT (Internet of Things) based smart parking system is a modern solution designed to address the challenges of urban parking management. Leveraging the power of interconnected sensors, communication technologies, and data analytics, this system offers real-time monitoring and optimization of parking spaces, enhancing efficiency, convenience, and user experience. The IoT based smart parking system is a comprehensive solution aimed at revolutionizing urban parking management. Traditional parking systems often suffer from inefficiencies, such as limited visibility into parking space availability, long search times for drivers, and inefficient space utilization. To address these challenges, the smart parking system integrates various IoT technologies to provide real-time monitoring, analysis, and optimization of parking spaces. Overall, the IoT based smart parking system represents a paradigm shift in urban parking management, offering a scalable, efficient, and user-centric solution to address the challenges of modern parking infrastructure. By harnessing the power of IoT technologies, data analytics, and seamless connectivity, this system aims to create smarter, more sustainable cities for the future. At the core of the system are IoT sensors deployed in parking spaces, capable of detecting the presence or absence of vehicles. These sensors communicate wirelessly with a centralized control system, which aggregates the data and provides actionable insights. Through web or mobile applications, drivers can access real-time information about available parking spaces, navigate to the nearest available spot, and even reserve parking in advance.

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Parking Slot, Arduino UNO, NodeMCU, Blynk application, SPS - Smart Parking System

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The AR-based interior designing and shopping app represents a solution that utilizes augmented reality (AR) to revolutionize the world of interior design and home decor. This innovative mobile application provides users with a dynamic and immersive experience, empowering them to effortlessly visualize, customize, and purchase furniture and decor items with ease and confidence.

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3D visualizations, DIY decorators, AR, personalize

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Creative Commons Attribution 4.0 and The Open Definition

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- Steganography is the process of hiding secret information, be it text, images, or videos, within digital media in a manner imperceptible to human eyes. The rise of deep learning technology has marked a powerful advancement in various applications, including steganography, attracting significant attention and fostering extensive research. This paper presents a concise review of diverse steganography technologies, encompassing both traditional methods and the latest emerging deep learning techniques. Spatial domain technologies like Least Significant Bit (LSB), and transform domain technologies such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), are explored alongside deep learning technologies like Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN) in the context of steganography. The discussion extends to various datasets used in steganography research, including BossBase, ImageNet, and MNIST. This paper aims to provide valuable insights for fellow researchers by reviewing contemporary trends and offering potential directions for the future of steganography research.

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Steganography, GAN steganography, CNN steganography, Data hiding, Spatial-domain steganography, Transform-domain steganography.

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Creative Commons Attribution 4.0 and The Open Definition