Volume 11 | Issue 11 |

 Abstract

Osteoarthritis is a common condition affecting the knee joint that is characterized by the progressive and irreversible loss of articular cartilage. Patients' lives are greatly impacted by this degenerative disorder, so prompt and accurate diagnosis is essential. The primary diagnostic tool for osteoarthritis, traditional knee X-rays, frequently depend on the experience of seasoned doctors, which adds to the burden and increases the risk of making mistakes in judgment because slight variations in disease stages exist.This work uses artificial intelligence (AI) and machine learning to create a state-of-the-art medical diagnosis support system in response to these issues. The technique of transfer learning, which makes use of neural network models that have already been trained, is the foundation of the system. It minimizes reliance on large datasets and facilitates effective learning for the diagnosis of knee osteoarthritis.

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 Keywords

Inception V3, Knee Osteoarthritis, X-Ray Images

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Rice is one of the most widely cultivated food crops in the world. However, there is a significant probability that high amounts of salt, especially at the seedling stage, would have a detrimental effect on rice growth. To prevent a decrease in rice productivity, it is crucial to quickly discover and develop salinity-tolerant rice crop types, particularly at the seedling stage. Expertise from humans is required for the classification of visual signals and the conventional way of a standard assessment system for identifying rice crop salt stress. It's not only time-consuming, but it often leads to mistakes that lead to inaccurate classification. The research shows the need for a deep learning developed model over the conventional approach of measuring rice crop sensitivity to salt stress during the seedling stage to detect and classify salinity stress in rice seedlings using field images. Therefore, we employ pre-trained deep learning approaches, such as VGG 16, VGG 19 to develop the classification model. These techniques are developed in Jupyter Notebook using Python programming. The model can classify images of rice seedlings into scores of 1, 3, 5, 7, and 9, demonstrating the critical need for a computer-based classification system for salinity prediction that could be used as a tool for automating the classification process for rice development to aid researchers and farmers in the rice crop management system.

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 Keywords

Salinity, Deep Learning, Rice Seedling

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Multimorbidity is the coexistence of multiple chronic conditions, posing a significant challenge to healthcare systems worldwide. This article gives information of the intricate landscape of multimorbidity, emphasizing the importance of tailored strategies for effective management. Focusing on the it's prevalence in India, this article explores the multidimensional nature of multimorbidity, considering factors such as financial burden and healthcare utilization. The article highlights on key components of successful multimorbidity management, including comprehensive assessments, individualized treatment plans, regular medication reviews, patient-centered innovations, and patient education. These components are pivotal in addressing the diverse needs of patients living with multimorbidity, ensuring personalized and holistic care. Looking to the future, this article discusses emerging trends like Online Patient Education (OPE) programs, medication simplification, increased involvement of physician as well as clinical pharmacists, and patient-centered outcomes. These innovative approaches hold promise in improving the quality of life for individuals grappling with multimorbidity. In conclusion, this article underscores the urgency for healthcare systems to adopt patient-centric approaches in managing multimorbidity. By embracing personalized care, empowering patients, and by using new technological advancements, healthcare providers can navigate the challenges that posed by multimorbidity and improve quality of life of individuals living with this complex health condition.

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 Keywords

Multimorbidity, Chronic Conditions, Prevalence, Multiple treatment therapy, Strategies.

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This comprehensive survey study investigates retinal blood vessel separation techniques, gathering insights from ophthalmologists and retinal imaging specialists in India. The study addresses the significance of these techniques in the field of ophthalmology and retinal imaging, emphasizing their role in clinical diagnosis and research. Drawing from a diverse sample of professionals, the research examines the effectiveness of current separation methods, awareness of recent advancements, and the potential of artificial intelligence (AI) in improving these techniques. Additionally, it explores the challenges faced in retinal blood vessel segmentation and the primary imaging modalities used in practice. Ethical and privacy concerns related to AI-based segmentation are also examined. The study's findings offer valuable insights into the perceptions and practices of Indian professionals in the field, with implications for training, software development, ethics, and the integration of AI. This research contributes to the broader understanding of retinal imaging practices in India and provides recommendations for businesses and policymakers to enhance the quality of eye care in the country.

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 Keywords

Retinal Blood Vessels, Separation Techniques, Ophthalmologists, Retinal Imaging Specialists, India.

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 Abstract

Commerce drives economic growth, development and prosperity, promotes regional and international interdependence, fosters cultural exchange, creates jobs, improves people's standard of living by giving them access to a wider variety of goods and services, and encourages innovation and competition for better products.With the right technology, companies can streamline operations, make better decisions, deliver a better customer experience, and maximize returns. The role of technology in business is to enable companies to operate more efficiently, effectively, and competitively.

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E-Commerce, E-Tech, B2B,B2C,modules of e-commerce, USA regulations and commerce policies

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The increasing digitization of societal processes has paved the way for the exploration of innovative technologies to enhance the integrity and security of critical systems, particularly in the realm of electoral processes. This paper introduces an Online E-Voting System utilizing Blockchain technology, aiming to address the challenges associated with traditional voting systems such as fraud, tampering, and lack of transparency. The proposed system leverages the decentralized and immutable nature of blockchain to establish a secure, transparent, and auditable platform for conducting electronic voting.

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Blockchain, consensus, e-voting, government, industry, security, transparency.

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 Abstract

Paper uploaded

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Innovation, Business models, emerging technologies

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This review paper explores the current applications of Artificial Intelligence (AI) in conjunction with Remote Sensing (RS) and Geographic Information System (GIS) technology for river water management. It also discusses the potential future applications of AI in improving the accuracy of water quality predictions. While the literature on AI in the water domain is extensive, the ethical aspects of AI applications in water management receive limited attention. This paper emphasizes the need to consider ethical considerations alongside technological advancements. The integration of AI-GIS technology has shown promising results in predicting water quality parameters with greater accuracy.

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 Keywords

RS : Remote Sensing GIS : Geographical Information System AI : Artificial Intelligence GIS : Geographic Information System ANI : Artificial Narrow Intelligence AGI : Artificial General Intelligence ASI : Artificial Super Intelligence IoT : Internet of Things DSS : Decision Support System ESRI : Environmental Systems Research

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PROSPECTIVE STUDY OF USAGE OF ANTIBIOTICS AT A TERTIARY CARE TEACHING HOSPITAL

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PROSPECTIVE STUDY OF USAGE OF ANTIBIOTICS AT A TERTIARY CARE TEACHING HOSPITAL

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ABSTRACT Urban planning and geospatial technology play crucial roles in achieving sustainable development and sustainable development goals by addressing the complex challenges faced by urban areas. This paper highlights the significant contributions of urban planning and geospatial technology in promoting sustainable urban development. It explores how urban planning facilitates the integration of social, economic, and environmental factors to create liveable and resilient cities. Geospatial technology, with its ability to capture, analyse, and visualize spatial data, provides decision-makers with valuable insights for informed decision-making. By leveraging accurate and up-to-date spatial data, urban planners can prioritize sustainability aspects such as land use, transportation, energy efficiency, and climate change adaptation. This study showcases the application of geospatial technology in sustainable land use planning. Furthermore, it examines the monitoring and evaluation capabilities of geospatial technology for ongoing assessment and adjustments towards sustainable urban environments. Data have been majorly collected from secondary sources. Comprehensive review of existing literature has been conducted to gather information on the applications, benefits, and challenges of geospatial technology in urban planning for sustainable development. The findings emphasize the importance of integrating urban planning and geospatial technology as a holistic approach to achieve sustainable development, ensuring the well-being of urban residents while minimizing environmental impacts. The paper concludes by offering recommendations for policymakers, planners, and practitioners to effectively utilize urban planning and geospatial technology for the advancement of sustainable cities and communities.

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 Keywords

Keywords: Urban Planning, Sustainable Urban Development, Geospatial Technology, Sustainable Development Goals, Land Use Planning

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