Volume 13 | Issue 5 |

 Abstract

Radiology technologists are key contributors to modern healthcare, providing vital support in the diagnostic process through advanced imaging techniques. This paper explores their role in ensuring accurate diagnoses and promoting patient-centered care. Data were collected through literature review and observational analysis in clinical settings. Results indicate that well-trained radiologic technologists significantly improve diagnostic accuracy, patient safety, and overall workflow efficiency.

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 Keywords

1. Radiology 2. Radiation safety 3. Patient 4. Technologist 5. Patient care 6. Diagnostic

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Financial decision-making has become increasingly complex due to the vast amounts of information available, which often leads to challenges in extracting actionable insights. This paper explores the role of Artificial Intelligence (AI) in enhancing financial decision-making processes by leveraging technologies such as Natural Language Processing (NLP), machine learning, and deep learning models to analyse unstructured data, uncover patterns, and deliver timely, data-driven predictions. Applications like portfolio optimization, sentiment analysis, and earnings forecasting demonstrate AI's ability to improve decision accuracy, efficiency, and risk management. However, the study also highlights the limitations of AI, including its dependence on data quality, vulnerability to noise, and struggles to adapt during regime changes, emphasizing the critical role of human expertise in validating machine-driven insights and offering forward-looking judgment. By advocating a collaborative "man + machine" approach, the paper underscores the synergy between AI's computational power and human intuition, enabling organizations to balance automation with rational oversight. This integration not only enhances decision-making processes but also strengthens businesses' ability to navigate uncertainties, discover growth opportunities, and maintain long-term financial stability in a competitive and dynamic economic landscape.

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Artificial Intelligence, Financial Decision-Making, Machine Learning, Neural Networks, Decision Support Systems, Ethical AI.

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Abstract The paper analyses the various stages of the Dalit movement in Karnataka According to the author, though the Dalit movement in Karnataka is as old as the Veerashaiva movement the Dalit concern received very little attention in the context of the Non-Brahmin movement in the princely state of Mysore. The real Dalit movement in Karnataka started with the Bhusa Uproar of B. Basavalingappa. In the process the rise of the Dalit Sangharsh Samiti proved to be a non-parallel event in the history of the Dalit movement in Karnataka but this movement suffered from too many lacunas. The movement in Karnataka did not base its activities on a definite ideological stance the lack of ideological commitment in turn did not allow it to emerge as a 'self-help movement'. As the Dalits continued to fight for cursor? Benefits they lacked themselves into the captivity of concessions like reservations. Besides, they took their 'Dalit world' too much for granted. Which ultimately kept them bound by chains of submission to exploitative Hinduism? Strangely, in the process. The Dalits succumbed to the same evils against which they had been lighting. Also the lack of social cohesion and unity left the various Dalit organizations politically distorted.

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Key words: Dalit, Movement: DSS, Caste, Karnataka, Committee, Social, Bhusa uproar etc.

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Stock market trend analysis is a complex and challenging problem because of its highly volatile and nonlinear nature. Traditional models sometime fail to capture intricate patterns present in financial time series data. In this paper, we present a deep learning approach by implementing Long Short-Term Memory (LSTM) networks to estimate future fluctuations in stock value of Adani Group companies listed on the National stock exchange. This study uses a Kaggle sourced dataset including features like company names, current prices, stock dates, stock volume and stock open close. The data were preprocessed and normalized to improve model convergence. An LSTM model was trained to forecast stock prices for the upcoming financial year, and its predictions were integrated into a user-friendly web application. The platform provides real-time visualization of stock trends, including line charts and company information, enabling users to interactively explore historical and predicted prices. Experimental results demonstrate that the LSTM model captures underlying stock trends effectively, providing accurate predictions despite market volatility. This work highlights the ability of deep learning models linked with interactive web applications in enhancing financial decision-making

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LSTM (long short term memory) , deep learning , machine learning ,recurrent neural network

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Mariama Bâ, one of the prolific women novelists of the African Literature, emerges as a harbinger and torchbearer for women’s identity by fighting against social injustices, particularly those faced by women in the predominantly Muslim and male-dominated society of Senegal. Focused on female emancipation, Bâ challenges entrenched patriarchal values and urges women to seize control of their destinies. In Mariama Bâ’s two novels, So Long a Letter and Scarlet Song. Bâ’s focus on the ills of polygamy is analysed. Her novels highlight the progression of the African educated women. So Long a Letter attempts to examine how an educated, middle-aged and abandoned wife achieves self-realisation and emerges as a confident woman fully in control of herself. It also shows how women are able to accept life on their own terms and live independent lives. This paper explores Mariama Bâ's mission to use narrativization as a weapon for gender empowerment, shedding light on the multifaceted challenges faced by women in post-independence Senegal and emphasizing the transformative journey towards gender equality.

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Narrativization, Female emancipation, patriarchal challenges, societal values, gender dynamics, transformative journey.

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: In the modern era, fire hazards pose significant threats to human lives and infrastructure. Traditional firefighting methods often expose individuals to dangerous conditions, necessitating intelligent automation to enhance safety and efficiency. RoboRescue is an innovative robotic system designed to detect and extinguish fires using Arduino-based control mechanisms. Leveraging advanced sensors such as flame detectors, temperature sensors, and gas sensors, RoboRescue autonomously identifies fire outbreaks and assesses environmental risks. The system incorporates real-time processing and decision-making algorithms, enabling swift responses and precision in firefighting operations. Equipped with mobility features, the robot can navigate hazardous environments, avoiding obstacles while delivering targeted fire suppression using water or fire-retardant chemicals. The integration of IoT capabilities ensures remote monitoring and control, allowing firefighters to manage operations from a safe distance. This research highlights the potential of intelligent robotics in revolutionizing fire prevention and mitigation strategies, reducing human intervention in life-threatening scenarios, and paving the way for safer and more effective emergency response systems.

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firehazard,IoT,flame detectors,temperture sensor

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Inventory control and management at Automobile industry play a crucial role in ensuring efficient supply chain operations, cost optimization, and seamless production processes. The company employs advanced inventory management techniques such as EOQ, ABC analysis and VED analysis automated tracking systems, and data analytics to maintain optimal stock levels while minimizing holding costs and reducing wastage. By integrating modern ERP solutions and real-time monitoring, it ensures accurate demand forecasting, timely procurement, and effective warehouse management. These practices not only help in avoiding stockouts and overstock situations but also enhance the overall efficiency of production and distribution processes.

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Inventory management, Tracking, decision making.

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ABSTRACT The field of public administration has seen a dramatic transition from the traditional Weberian model, which focused on Western philosophies, to the neoliberal model, which is based on a universal theory supported by the World Bank. Yet the field is particularly context-driven and dependent on the society in which it is practiced, and none of these models are good for it. The subject was first taught in India as a sub-discipline of political science and was available in only a few institutions before the country's independence. But after independence it was Appleby who established it as a separate course of study in a number of universities. The discipline gained a lot of attention when it was added as a compulsory item in the Indian Civil Services UPSC syllabus. However, the limited scope of the study and excessive concentration on technical details prevented it from attracting the best minds. An examination of the research inquiry in this area reveals a fundamental weakness: it is a goal-oriented activity that ignores the socio-economic and political environment in which it is situated. In order to combat administrative corruption and preserve the spirit of ethics in administration, the recommendations of the Administrative Reforms Commission resulted in the establishment of several organizations and the adoption of certain legislation. Maintaining public administration's methodological diversity, context-specific approach and public character is therefore essential to the survival of the field as a practice in the face of an ever-changing global environment.

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Public Administration, Western methodology, Political Science, Governance, Re-conceptualization, Ethics, Neo-liberal perspective.

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: Oral cancer remains a critical health issue globally, with more than 360,000 new cases diagnosed annually. Despite advances in medical treatments, survival rates are significantly hampered by late-stage diagnoses. Early detection is paramount, as it can greatly enhance patient survival and reduce treatment costs. This study introduces an innovative framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the detection of oral cancer using multimodal data fusion. CNNs are employed to extract spatial features from clinical images, while LSTMs analyze the temporal dependencies present in medical imaging data, particularly in the case of longitudinal scans or patient records over time. By fusing spatial and temporal information, the model can detect early-stage lesions, subtle changes, and abnormalities often overlooked by traditional diagnostic methods. To assess the effectiveness of this framework, we conducted extensive experiments on a diverse oral cancer dataset, demonstrating the model's exceptional sensitivity, specificity, and area under the curve (AUC). These results emphasize the robustness and generalization capability of the model across various patient demographics and imaging conditions. The proposed deep learning model offers significant promise for clinical implementation, providing healthcare professionals with a powerful tool for early screening, diagnosis, and improving patient care outcomes. The successful application of CNNs and LSTMs in oral cancer detection underscores the transformative role of deep ABSTRACT: Oral cancer remains a critical health issue globally, with more than 360,000 new cases diagnosed annually. Despite advances in medical treatments, survival rates are significantly hampered by late-stage diagnoses. Early detection is paramount, as it can greatly enhance patient survival and reduce treatment costs. This study introduces an innovative framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks for the detection of oral cancer using multimodal data fusion. CNNs are employed to extract spatial features from clinical images, while LSTMs analyze the temporal dependencies present in medical imaging data, particularly in the case of longitudinal scans or patient records over time. By fusing spatial and temporal information, the model can detect early-stage lesions, subtle changes, and abnormalities often overlooked by traditional diagnostic methods. To assess the effectiveness of this framework, we conducted extensive experiments on a diverse oral cancer dataset, demonstrating the model's exceptional sensitivity, specificity, and area under the curve (AUC). These results emphasize the robustness and generalization capability of the model across various patient demographics and imaging conditions. The proposed deep learning model offers significant promise for clinical implementation, providing healthcare professionals with a powerful tool for early screening, diagnosis, and improving patient care outcomes. The successful application of CNNs and LSTMs in oral cancer detection underscores the transformative role of deep learning in advancing medical image analysis and healthcare diagnostics. Keywords: Oral Cancer Detection, CNN, LSTM, Multimodal Data Fusion, Deep Learning, Medical Imaging, Early Diagnosis.

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Oral Cancer Detection, CNN, LSTM, Multimodal Data Fusion, Deep Learning, Medical Imaging, Early Diagnosis.

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Employee engagement plays a crucial role in determining the success and effectiveness of organizations, especially in dynamic and labor-intensive industries like construction. Successful organizations know that employee performance and employee engagement are crucial. This study explores the impact of employee engagement on organizational effectiveness within construction companies. Employee engagement is basically a level of commitment and involvement that an employees has towards their organization and its values. It examines key engagement factors such as job satisfaction, motivation, leadership, workplace culture, and communication, assessing their influence on productivity, project performance, and employee retention. The research highlights the correlation between highly engaged employees and improved efficiency. Using qualitative and quantitative analysis, the study provides insights into best practices for fostering engagement in the construction sector. The findings suggest that investing in employee engagement strategies leads to enhanced organizational performance, reduced turnover, and increased profitability. The study concludes with recommendations for construction firms to improve engagement levels, thereby driving overall effectiveness and sustainable growth.

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Employee engagement, job satisfaction, organizational performance,

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