Volume 13 | Issue 1

 Abstract

Artificial intelligence(AI) in financial education has the potential to revolutionize the field by improving learning through real-time, personalized, and adaptive feedback mechanisms.This study investigates Gen Z's awareness and perceptions of Artificial Intelligence(AI) tools in financial education.The study collected primary data using Google Forms for quantitative surveys,utilizing a convenience sample strategy.The three objectives are to analyze Gen Z's understanding of AI tools in financial education, learn about their perceived advantages and obstacles to using these tools, and offer suggestions for raising awareness and promoting AI usage in financial education.The findings show that there is a significant opportunity for more involvement even if Gen Z typically exhibits a moderate level of awareness of AI products.Personalized learning experiences and real-time feedback are two of the perceived benefits of AI tools, and these features match perfectly with Gen Z's propensity for interactive and adaptable teaching approaches.This reveals important adoption challenges, such as worries about data security and privacy and a lack of thorough understanding of how these tools work.The study makes a number of proposals to address these issues, including putting in place focused awareness-raising educational efforts,AI literacy into financial education curriculum, and putting in place strong data protection measures to foster confidence.By focusing on these topics, educational institutions can improve Gen Z's financial literacy by increasing the efficacy and acceptability of AI tools.This study adds to our understanding of how artificial intelligence(AI) can be used to further financial education and provides useful information for those involved in both technology and financial education.

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 Keywords

Artificial Intelligence, Financial Education , Gen Z, AI Tools Awareness , Financial Literacy

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In the recent years the demand is increasing day to day. It is found that large quantity of electricity in many countries is consumed in lighting the streets. Most of basic street lighting systems are switched ON/OFF at regular intervals of time. In this project the system is to develop a street light energy saving control system to reduce energy if no vehicles pass through certain roads. This paper suggests a low-cost Arduino microcontroller-based solar street lighting system that is energy-efficient. The primary goal is to provide energy-efficient solar streetlights for smart cities only, as well as for current lamps in rural and urban areas. The LDR luminaire, LDR driver, PV panel, charge controller, light sensor, motion sensor, and Arduino make up the system. The smart streetlight is adjusted according to the amount of traffic on the road and the time of day or night. This paper suggests energy efficient of automatic street light by using Arduino. The main objective is to design energy efficient automatic streetlight for energy conversation in present streetlights of rural area, urban area and completely for smart cities. The system LED, solar panel, charge controller, Battery, Arduino. The system is set to automatically turn OFF during the hours of daylight and only operate during the night.

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Smart LED street light; Solar; servo motor

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 Abstract

The process of the isolation of Eastern Areas can be traced back to 20th century with the creation of Excluded Area in the Naga Hills District under the province of Assam but before we proceed to the discussion of the contemporary issue, it is advertent to rewind the history for better understanding. Let's examine the chronology of the region as to understand the sentiments and demands of the people. The Nagas lived independently in their own territory prior to their contact with the British. Nagas came into contact with the British in 1832; the Naga territory underwent a political transformation post the battle of 1851 known as the battle of Kikruma. The British established their first headquarter after reoccupation of Samaguting in 1866 and subsequently sub-divisional headquarters were created at Wokha (1875), Kohima(1879) and at Mokokchung (1889). The Permanent headquarter after re-occupation of Kohima was established in 1881. The Naga Hill district was under British administration while Tuensang area remained as un-administered area until 1902 with the label of Tribal Area. In 1880, Assam frontier Tracts Regulation was implemented, which extended up to the Naga tribal areas. Through this regulation the first British expedition reached Tuensang area on 12th January 1889, via Kohima and Mokokchung. Although the Britishers were able to bring certain area under their control, but they could not fully enforce their administration throughout the Tuensang area. The area remained un-administered, some within the political control and certain area beyond the political control. In 1914, Tuensang area was clubbed under the North East Frontier Tract by the regulation of Assam Frontier Tract Regulation Act of 1880, the area remained as tribal area under section 311 of the Government of India Act 1935 by which no British laws and regulation were to be implemented in this area, and thus the area remained officially un-administered until 1948 though certain measure were taken by 1902 yet this historical negligence with the political corruption impacted the areas to the current times.

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 Keywords

ENPO, Frontier Nagaland, Frontier Territory, British Administration in Naga Hills.

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The rapid growth of urban areas in the Delhi NCR (National Capital Region) has resulted in worsening road traffic congestion, creating significant environmental and economic challenges. This study investigates the factors influencing traffic congestion, including population growth, government policies, pride in vehicle ownership, and adherence to traffic rules. Through a dual-stage survey and analysis using Structural Equation Modeling (SEM), we explore the relationships between these variables. Our findings suggest that improving compliance with traffic regulations can substantially mitigate congestion, while population growth, government policies, and vehicle ownership do not have significant direct impacts. Recommendations are provided for policymakers to integrate infrastructure development with strict traffic management practices to ensure sustainable urban mobility.

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Traffic congestion, Delhi NCR, public policy, structural equation modeling, public transportation, traffic rules

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 Abstract

Immunotherapy for Bladder Cancer: A Review of Current Landscape

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Bladder cancer, immunotherapy, checkpoint inhibitor, gene therapy, adoptive T-cell therapy.

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Scrub typhus, also known as bush typhus, is a reemerging zoonotic disease that remains largely underrecognized despite its significant global impact. With nearly 1 billion people at risk, one million annual cases, and an untreated fatality rate approaching 50%, this acute febrile illness demands urgent attention. Caused by Orientia tsutsugamushi, an obligate intracellular Gram-negative coccobacillus from the Rickettsiae family, scrub typhus poses a critical health challenge. This article delves into the homoeopathic perspective, classifying it under Hahnemannian acute sporadic diseases with a miasmatic diagnosis of psora-syphilitic. Additionally, it highlights the therapeutic potential of homoeopathy in managing and addressing this neglected yet deadly disease.

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Homoeopathy, Scrub typhus, Therapeutics.

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This project proposes a deep learning-based system for vehicle detection and counting, leveraging the YOLOv8 model for accurate vehicle detection. The model is trained on a diverse dataset to ensure robustness across various traffic conditions. DeepSORT tracking algorithm is integrated for efficient multi-object tracking, enabling continuous monitoring of vehicle movement. The system processes video feeds in real-time, detecting and counting vehicles as they pass through predefined regions of interest. A Tkinter-based graphical user interface (GUI) is designed for easy input of video files and visualizing the results. The interface also provides real-time updates on detected vehicles and counts. The model is optimized for high accuracy while maintaining performance efficiency. The proposed system is suitable for applications in intelligent transportation systems, including traffic monitoring and automated vehicle counting. The project demonstrates the potential of deep learning techniques in addressing real-world transportation challenges. Future improvements may include handling more complex scenarios and enhancing tracking stability.

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Vehicle detection, Traffic Signal, Deep Learning, YOLO, OpenCV

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The researchers study the effectiveness of inventory Management and Control at San Roque Supermarket Montalban Rizal. Inventory is one of the resources that are managed by business organizations and it was first recorded in 1601. The main objective of inventory management is to maintain inventory at appropriate level to avoid excessive or shortage of inventory because both the cases are undesirable for business. This study employed the descriptive method of research using the questionnaire is gathering the respondents assessment on the effects of Inventory management and control in San Roque Supermarket, Montalban Rizal. The researchers have at least 50 respondents. Findings the respondents encountered problems on the lack of automation, failure to accurately forecast demand, lack of training program, poor communication, poor decision making.

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Key words: Inventory Management, business, automation, communication, decision making

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 Abstract

RAJNITTK JANMAT NIRMAN ME SOCIAL MEDIA KA YOUDAN AUSAR AND CHUNAUTIYA

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RAJNITTK JANMAT NIRMAN ME SOCIAL MEDIA KA YOUDAN AUSAR AND CHUNAUTIYA

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This study investigates the impact of smartphone addiction on the academic performance of secondary-level students in Hooghly District, West Bengal, utilizing a combination of qualitative data collection methods. Semi-structured interviews with students, parents, and teachers, classroom and home observations, self-reported smartphone usage logs, and academic records were used to gather comprehensive insights into students' smartphone habits and their influence on academic outcomes. The analysis, guided by frameworks such as the Smartphone Addiction Scale (SAS) and the Displacement Hypothesis, revealed patterns of academic disruption, cognitive overload, and behavioural changes. Findings indicate that excessive smartphone use, particularly for entertainment and social media, negatively affects academic performance, while mindful use does not necessarily lead to adverse outcomes. The study highlights the need for educators, parents, and policymakers to implement strategies to mitigate the negative impact of smartphone addiction on academic performance.

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Smartphone addiction, Academic performance, Secondary students, West Bengal, Cognitive overload, Behavioural impact, Screen time

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This study investigates the prevalence of cestode parasites in various vertebrates. These parasites are widely distributed among fish, amphibians, reptiles, birds, and mammals. While cestode infections have been extensively studied worldwide, research on their occurrence in Indian vertebrates remains limited. Understanding their impact is crucial, as they can cause severe infections, leading to significant health concerns and ecological imbalances. Among the affected species are Mastacembelus armatus, Wallago attu, and Clarias batrachus, nocturnal fish that inhabit rivers, coastal marshes, and sandy or rocky riverbeds. Like many other aquatic organisms, these fish are highly susceptible to cestode infections, which can negatively affect their health, growth, and survival. The presence of these parasites in vertebrates is particularly concerning because many of these hosts--including fish, birds, and mammals--are essential food sources for humans and hold substantial economic importance. The study's findings indicate that the helminth infestation index was lower in Wallago attu and Clarias batrachus compared to Mastacembelus armatus. The highest rates of parasitic infestations were recorded during the summer months in freshwater fish. Additionally, the study highlights the detrimental impact of anthropogenic activities since 2022, which have led to a decline in fish populations due to habitat destruction and pollution caused by illegal construction and other human activities. When infected hosts are consumed without proper cooking or preparation, cestode parasites can be transmitted to humans, leading to various health complications. Furthermore, cestode infections contribute to increased mortality rates among host species, reducing their nutritional and commercial value. The findings suggest that environmental factors and feeding habits play a crucial role in influencing the seasonal variation of parasitic infections, either directly or indirectly. Understanding the prevalence and effects of cestode infections is essential for biodiversity conservation, ensuring food safety, and minimizing economic losses in affected species.

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 Keywords

cestode, prevalence, Mean intensity, abundance, Beed.

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Pindborg tumor, also known as Calcifying Epithelial Odontogenic Tumor (CEOT), is a rare odontogenic neoplasm first identified by J.J. Pindborg. This tumor is exclusively epithelial in origin and accounts for less than 1% of all oral tumors. CEOT typically presents as a painless swelling, often associated with unerupted teeth, with both intraosseous and extraosseous variants. Radiographically, it may exhibit a distinctive "honeycomb" or "driven snow" appearance due to calcifications. Histologically, the presence of amyloid-like material and Liesegang rings serves as a key diagnostic feature. Although generally benign, the tumor may exhibit aggressive behavior in rare cases, especially in its clear cell variant. This review aims to provide a comprehensive overview of the tumor's epidemiology, clinical features, radiographic characteristics, histologic patterns, and management strategies to aid in accurate diagnosis and treatment planning.

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Pindborg tumor, amyloid, odontogenic epithelium.

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Bharatnatyam, Kathak, classical dancer, Aerobic Capacity, Vo2 max

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Autism, also referred to as Autism Spectrum Disorder (ASD), is a developmental condition that affects how individuals perceive and interact with the world. It is characterized by challenges in social interaction, communication, and a tendency for repetitive behaviors or narrowly focused interests. However, it is a spectrum, meaning that the traits and abilities of individuals with autism can vary widely. Autism Spectrum Disorder (ASD) and speech delay often go hand in hand, though not all individuals with autism experience significant delays in language development. Speech delays can vary widely depending on the individual and their specific profile on the autism spectrum. Speech delay is a common challenge among children with Autism Spectrum Disorder (ASD). This delay can manifest as limited vocabulary, difficulty forming sentences, or an inability to use spoken language to communicate. Addressing speech delay is crucial, as it significantly impacts a child's ability to express needs, build relationships, and engage in educational activities. There are several effective AI tools and technologies that have been developed to assist individuals with Autism Spectrum Disorder (ASD) and speech delays. These tools use machine learning, speech recognition, and other AI techniques to support communication, learning, and social interaction

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Autism Spectrum Disorder (ASD) and speech delay

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Ameloblastoma is a group of odontogenic tumors of the jaws that arise from ectomesenchyme or epithelial remnants associated with tooth development. The majority of them speculate that the tumor's growth and development may be connected to the intricate process known as the epithelium-mesenchymal transition (EMT), which allows cells to dynamically change from having a mesodermal shape to a morphological one. The etiopathogenesis of these tumors is still incompletely understood. The most common site of the lesion is the mandibular premolar area, which accounts for 32.6% of cases. The lesion's histological subtype and the existence of mural invasion are two important variables to take into account when evaluating the histopathological features of atypical lesions. This article gives a complete review of ameloblastoma, pathophysiology, clinical features and its management.

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ameloblastoma, aggressive, odontogenic tumor

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Ramoji Film City (RFC) has emerged as a pivotal institution in shaping both Hyderabad's film and tourism industries. Established in 1996, RFC has grown into one of the largest integrated film studio complexes in the world, offering cutting-edge facilities for film production while simultaneously becoming a major tourist destination. This comprehensive study explores the dual impact of RFC on Hyderabad's economy, culture, and global positioning. By providing state-of-the-art infrastructure and attracting international filmmakers, RFC has contributed significantly to the growth of the local and national film industries, particularly in the Telugu film industry (Tollywood), while also facilitating Bollywood and international productions. Furthermore, RFC's integration of film production with tourism has played a crucial role in enhancing Hyderabad's status as a prominent global tourism hub. This study combines the secondary data, to assess RFC's influence on film tourism, local economic development, and community engagement. The findings highlight RFC's role in job creation, cultural preservation, and fostering a global identity for Hyderabad, while also identifying areas for improvement in sustainability practices and competition from emerging film cities. This research provides valuable insights into the symbiotic relationship between film production and tourism, offering a model for similar institutions globally.

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Ramoji Film City, Film, Tourism, Economic Impact, Social & Cultural Impact

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Customer churn prediction is crucial for e-commerce companies looking to improve retention and optimize business strategies. In this study, we aim to build a robust predictive model to identify customers at risk of churn, using a variety of customer features such as demographics, behavior metrics, order history, and satisfaction scores. The analysis begins with an exploratory data analysis (EDA), using Python libraries like pandas, numpy, matplotlib, and seaborn for data manipulation, visualization, and understanding patterns in the dataset. Missing values are visualized and addressed using missing no, ensuring data quality before model development. Several machine learning models are trained to predict customer churn, including Support Vector Classifier (SVC), Logistic Regression, Random Forest Classifier, and XGBoost, implemented with libraries such as scikit-learn and xgboost. Data preprocessing steps, such as scaling features with Standard Scaler and encoding categorical variables using LabelEncoder, are performed to prepare the dataset for model training. Model evaluation is conducted using performance metrics like accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix, offering detailed insights into the effectiveness of each model. In addition, hyperparameter tuning is achieved using GridSearchCV to optimize model performance. Cross-validation is applied with cross validate to ensure generalizability and robustness of the models. The study identifies key factors influencing churn, including marital status, order categories, and platform engagement, and provides actionable insights for the company to implement targeted retention strategies. By leveraging machine learning and data analytics, this research helps the e-commerce company proactively manage churn, enhance customer satisfaction, and optimize product offerings tailored to customer preferences.

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Customer Churn Prediction, Customer Segmentation, Exploratory Data Analysis (EDA), Predictive Modeling, Voting Classifier, Machine Learning, Feature Scaling.

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The increasing interest and adoption of cryptocurrency in India signal a transformative shift in the nation's financial ecosystem. Cryptocurrencies offer innovative solutions to challenges such as financial exclusion, currency inefficiencies, and limited access to global markets. This research paper explores the fundamental principles of cryptocurrency, its mechanisms, and its economic implications within the Indian context. Using case studies and data from India's burgeoning crypto market, the study investigates how cryptocurrencies can drive financial inclusion, support economic growth, and integrate the Indian economy into global financial systems. While acknowledging the potential advantages, the paper also examines challenges including regulatory uncertainties, technological barriers, and socio-economic risks, and proposes strategies for fostering sustainable growth of cryptocurrency in India.

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Cryptocurrency, Blockchain, Financial Inclusion, Economic Growth, India, Digital Economy, Regulation

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Creative Commons Attribution 4.0 and The Open Definition

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Direct Orange, Cotton Stalks,Adsorption Isotherms, Sorption mechanism.

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The widespread adoption of digital payment applications has transformed financial transactions, with Google Pay becoming one of the leading platforms in this domain. This study explores customer satisfaction with the usage of the Google Pay application, focusing on factors such as ease of use, transaction efficiency, security, rewards, and customer service. Using a survey-based approach, data was collected from a diverse group of users to understand their experiences and perceptions. The analysis highlights the key factors influencing customer satisfaction, including the intuitive interface, reliability of transactions, and the security measures offered by the platform. Positive experiences, such as seamless payment processes and attractive cashback offers, are significant contributors to user satisfaction, while challenges such as technical glitches and occasional delays in customer support were noted.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Google Pay, Customer satisfaction, Security, Transactions

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Creative Commons Attribution 4.0 and The Open Definition