Volume 12 | Issue 11

 Abstract

As the sophistication of deep fake technology increases, so does the potential for misuse in various domains, including politics, entertainment, and social media. This paper presents a comprehensive survey of deep fake detection systems, focusing on the methodologies employed to identify manipulated audio-visual content. We categorize existing detection approaches into traditional and modern techniques, detailing their underlying algorithms, feature extraction methods, and performance metrics. Additionally, we explore the challenges faced in deep fake detection, such as the evolution of generative models and the need for real-time processing capabilities. The survey also addresses the ethical implications and societal impacts of deep fakes, highlighting the importance of developing robust detection mechanisms to mitigate misinformation. Finally, we identify future research directions, emphasizing the need for interdisciplinary approaches that combine advancements in machine learning, psychology, and legal frameworks to enhance detection accuracy and the field of deep fake detection.

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 Keywords

Generative Adversarial Networks (GANs), K-means Clustering, Face Swapping, Image Synthesis And Face Recognition, Convolutional neural networks ,Deep fake, Convolutional Neural Network(CNN), Fake Face Detection.

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Agriculture is the mainstay of the Indian economy. Almost 70% people depend on it & shares major part of the GDP. Diseases in crops mostly on the leaves affects on the reduction of both quality and quantity of agricultural products. Perception of human eye is not so much stronger so as to observe minute variation in the infected part of leaf. In this paper, we are providing software solution to automatically detect and classify plant leaf diseases. In this we are using image processing techniques to classify diseases & quickly diagnosis can be carried out as per disease. This approach will enhance productivity of crops. It includes several steps viz. image acquisition, image pre-processing, segmentation, features extraction and neural network based classification.

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Image Processing, K-means Clustering, Histogram Equalization, Contour Detection And Tracing, Gray Level Co-occurrence Matrix (GLCM), K-Nearest Neighbor

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A deep learning-based method called AyurLeaf was created to classify medicinal plants. Conventional methods for classifying and identifying medicinal plants have depended on human observation and skill, which can be laborious and prone to mistakes. The development of deep learning methods and artificial intelligence has made it feasible to create automated systems that able to correctly categorise therapeutic herbs according to their outward appearance.For millennia, many traditional medical methods have included the therapeutic use of medicinal plants. Bioactive substances found in these plants have therapeutic qualities and can be utilised to treat a range of illnesses. However, because there are many species and minor variations in their physical characteristics, it can be difficult to identify and classify these plants

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Desktop application, DB sqlite, python, CNN algorithm , Machine learning

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Real time monitoring of landslides is one of the challenging research areas nowadays within the field of geophysical research. The event of an actual field deployment of devices network primarily based landslide detection system. This system uses sensor nodes and communication for efficient delivery of real time data to the system for monitoring and provides warning and risk assessments to the inhabitants of the area. This network will be used for understanding the capability and usability of sensor network for critical and emergency application. In the context this project delves into the development of a sensor based system that utilizes multiple sensors for landslide detection as well. We will explore how these sensors enhance the monitoring surrounding environmental awareness in a coal mine, enabling it to relay data in dynamic and complex scenarios.

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Temperature Sensor, Pressure Sensor, Gas Sensor, Buzzer

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Abstract--In the domain of Internet of Things (IOT),a complete solution for optimising traffic signals to prioritise emergency vehicles is provided by the combination of Raspberry Pi, NodeMCU, transmitter, receiver, LED, and jumper cables.This abstract delves into the technical intricacies

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Keywords--IoT (Internet of Things),Traffic signal control,Emergency vehicle priority,Smart traffic management,Real-time traffic monitoring.

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Libraries have always been viewed as services that provide information, encourage reading, and support the idea of learning as a lifelong habit. In India, libraries have undergone significant transformations over the years, adapting to changes in technology, demographics, and user expectations. This paper aims to explore the new and emerging trends shaping the future of libraries in India. Based on the analysis of the primary care practices that are currently in use, problems that are prevalent, and opportunities that are apparent in the practices, we outline the critical areas of development such as digitalization, outreach to patrons, provision of innovative services, and the impact of the libraries on education and research support. Thus, it is acceptable to state that trends emerging in the position of libraries, as from the point of view of users, necessitate more investment in the training of library professionals and decision-makers of different levels so that bibliotheca is regularly updated. However, to address the issues of society as a whole and every library user specifically, the services and requirements of the libraries must be met.

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Library, Technology, Trends, Digitization

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Bhartiya Rajniti Me Lalkrishna Aadvani Ka Yogdhan : Ek adhyan

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Bhartiya Rajniti Me Lalkrishna Aadvani Ka Yogdhan : Ek adhyan

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This paper provides an in-depth examination of the banking industry, tracing its evolution from traditional deposit and lending services to a wide range of financial solutions. Banks now prioritize customer satisfaction and efficient services at reasonable costs. The paper consists of several key components, including an introduction to the past, present, and future of banks, as well as the challenges facing the industry. The paper profiles The Jalgaon People's Cooperative Bank Ltd., highlighting its services and financial status, alongside comparative analyses of JalgaonJantaSahakari Bank and VidyaSahakari Bank. The research methodology outlines the study's objectives, scope, sources, and limitations. The paper then presents data interpretation and findings, utilizing tables, charts, and graphical representations. The study aims to compare Jalgaon People's Cooperative Bank Ltd. with other cooperative banks, evaluate their financial performance and profitability, and identify the most effective and efficient bank. Based on the findings, the paper offers proposals and suggestions for cooperative banks to maintain their regional presence and tackle future challenges. Ultimately, the paper underscores the importance of leveraging technology and innovative services to enhance customer satisfaction, improve operational efficiency, and remain competitive in the market.

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1. Banking industry 2. Financial evolution 3. Customer satisfaction 4. Operational efficiency 5. Financial solutions 6. Cooperative banks 7. Jalgaon People's Cooperative Bank Ltd. 8. Comparative analysis 9. Financial performance 10. Profitability

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Abstract-- Utilizing Python for the analysis and visualization of electroencephalogram (EEG) data offers significant insights into brain activity patterns. The methodology encompasses preprocessing raw EEG signals, performing spectral analysis using Fast Fourier Transform (FFT), and employing advanced data visualization techniques. Key Python libraries such as NumPy, Pandas, and Matplotlib are employed for data manipulation and visualization. The visualization includes spectrograms, power spectral density plots, and topographic maps, facilitating the understanding of EEG frequency content and spatial distribution. Additionally, machine learning algorithms are considered for feature extraction and classification to identify cognitive states. The outcomes aim to develop robust Python-based scripts for EEG data processing, aiding researchers and clinicians in analyzing brain activity patterns and potentially identifying cognitive states or anomalies. Contributions to neuroscience are made by providing accessible tools for EEG data analysis and visualization using Python.

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Keywords-- Electroencephalogram (EEG), Python, Data Analysis, Spectral Analysis, Visualization, Machine Learning, Brain Activity Patterns

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The dairy sector is a key contributor to India's economy, providing employment, supplementary income, and self-employment opportunities, especially for small and marginal farmers. India is the largest producer and consumer of milk, with production reaching 230.6 million tons in 2023. While Uttar Pradesh leads in milk production, Karnataka ranks ninth, contributing 5.34% to the total. Despite the sector's potential, it faces challenges such as unorganized production, low productivity, inadequate feed, and poor veterinary care. Most dairy farmers operate on a small scale using traditional practices, with limited access to modern technology, credit, and insurance. Only 10% of milk is processed by organized dairy plants, while the majority is handled by local traders. Climate change, inefficient supply chains, and animal diseases further impact dairy farming. Additionally, poor adoption of innovations like high-yield fodder varieties and cold storage facilities limits growth. Improving productivity and profitability requires better infrastructure, financial support, and access to training programs. Promoting climate-smart practices and expanding value-added products can boost growth and sustainability. Addressing these challenges will unlock the sector's potential, benefiting rural livelihoods and ensuring India continues to meet the rising demand for milk and dairy products

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Dairy sector, Milk production, Problems and prospects, India, Karnataka

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This review article explores the impact of granulation time and drying temperature on compression parameters and quality attributes in pharmaceutical manufacturing. Granulation and drying are critical unit operations that significantly influence the physical properties of granules, which, in turn, affect tablet characteristics such as hardness, disintegration time, and dissolution rates. The interdependence between granulation time and drying temperature is emphasized, highlighting how each parameter influences granule size, moisture content, and compressibility. Key findings indicate that insufficient granulation time can lead to poorly agglomerated granules, while excessive granulation may result in overly dense granules requiring longer drying times. Similarly, inadequate drying temperatures can cause excessive moisture retention, affecting granule flowability and compressibility, whereas high drying temperatures can lead to brittleness. The article underscores the necessity of optimizing Critical Process Parameters (CPPs) to ensure consistent Critical Quality Attributes (CQAs) and regulatory compliance. Additionally, advancements in technologies such as continuous manufacturing and Process Analytical Technology (PAT) are discussed, emphasizing their role in real-time monitoring and control of granulation and drying processes. This review serves as a comprehensive guide for researchers and industry professionals seeking to enhance product quality through the optimization of granulation and drying conditions.

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Granulation Time, Drying Temperature, Compression Parameters, CPP, CQA, PAT

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Abstract: The Kasyapasamhita, an ancient Ayurvedic text attributed to Sage Kasyapa, is a foundational work in pediatric health care, emphasizing holistic approaches to child health and development. This treasure trove offers insights into various aspects of neonatal, infant, and adolescent care, highlighting preventive and curative methods for common childhood ailments, nutrition, growth, and immunity. Through discussions on breastfeeding, weaning practices, and traditional herbal remedies, the Kasyapasamhita underscores the importance of early life interventions for lifelong health. Its teachings deeply rooted in Ayurveda, present a complementary perspective to modern pediatric practices, making it a valuable resource for integrative health care. This paper explores the historical, clinical, and practical aspects of the Kasyapasamhita, examining its relevance in contemporary child health.

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Keywords: Kasyapasamhita, child health, Ayurveda, pediatric care, neonatal care, infant nutrition, herbal remedies, holistic health.

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Cancer is a medical condition characterized by the rapid and uncontrolled proliferation of infectious cells throughout various sections of the body. It may commence at any site in the body and could either be malignant or benign. Cancerous cells have a capacity to move to distant locations in the body via metastasis. Immunotherapy is a proficient style of treatment that employs the body's own immune system capabilities to attack diseases. Immunotherapy may modify or boost the immune system's ability to recognize eradicate infectious cells. This article will discuss how immunotherapy could potentially be employed to treat and alleviate various types of cancers. This article will address an array of cancers, notably melanoma, breast cancer, and leukemia.

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cancer immunotherapy, checkpoint-point inhibitors, melanoma, Leukemia CAR T-cell therapy, HER2-positive breast cancer, Triple-negative breast cancer

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The present paper is based on research study undertaken to find out Emotional Intelligence, Creativity, Learning and Thinking Styles of Students. An experiment was conducted on 465 students of B.A.-IIIrd year on the basis of random sampling technique. In this study, both teachers and students tend to exploit their preferred styles, which may or may not match. Students whose styles are different and do not correspond with the teacher's style are labelled as 'slow' or 'dull'. Therefore, it is important for teachers to know the student's preferred styles so that the teacher can capitalize the opportunities for student's learning. This study will definitely help the teachers, administrators, counsellors and parents to understand and assess the preferred styles of students for developing their intelligence and creativity in the field of their profession and in the field of their preferred styles in academic area of their choice.

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Emotional Intelligence, Creativity, Learning & Thinking Styles

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Railway safety is, in fact of paramount importance owing to the high accident rate often caused by some form of unexpected obstacle on a railway track, such as animals, vehicles, debris, and other foreign objects. Moreover, the current trackside obstacle detection systems are characterized by very high costs for maintenance, low distance coverage, and susceptibility to environmental wear and damage. The article discusses a novel IoT-based obstacle detection system for real-time detection and classification of obstacles utilizing LiDAR sensors and AI-enabled cameras mounted on the front of moving trains. It essentially shifts the mechanism of obstacle detection from the extensive trackside infrastructure to the train itself, thereby obliterating the latter. It thus identifies correct high-resolution, 3D obstacle images at a distance, ensuring differentiation between critical and non-critical objects, because the system combines LiDAR's ability to create high-resolution 3D maps with AI's object classification capabilities. Real-time computation within the edge device allows for sending alert messages immediately. It has better long-term monitoring integrated with cloud. Such innovative approach gives great benefits in enhancing railway safety systems by offering scalability, adaptability, and cost-efficiency. In addition to accuracy metrics, the system's performance will be evaluated using various environmental tests with very promising results in terms of obstacle detection accuracy, system reliability, and efficiency during operation.

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Obstacle Detection, Arduino UNO, LIDAR, AI Enabled Camera

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Abnormal uterine bleeding (AUB) is one of the most common menstrual disorders seen in the reproductive age group of women. AUB is refers the uterine bleeding outside the normal volume, duration, regularity or frequency. The clinical features of Raktapradara have close resemblance with Abnormal Uterine Bleeding. Raktapradara is a condition in which excessive or prolonged flow of blood during menstrual cycle or inter menstrual cycle as per Ayurveda. Ayurveda with its holistic approach can provide a comprehensive and effective treatment for the disease Raktapradara. The formulation 'of rakta stambaka churna was In the present clinical study with single group, 30 patients were enrolled and completed the study. All the patients were subjected to oral adminstration of rakta stambaka drugs for 5 days. The efficacy of treatment is assessed by adopting scoring methods for the Subjective and objective parameters and the results are analyzed statistically by paired "t" test. The results of the study indicate that the 'p'value is highly significant to extremely significant in all parameters and gave good results without any complications.

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Abnormal Uterine Bleeding, Raktapradara

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The risk perception component is a critical part of the formation of public responses to health crises, including compliance with protective measures and trust in healthcare systems. This review summarizes findings from 25 risk perception studies across SARS, MERS, and COVID-19 infectious disease outbreaks. Key topics covered emphasize the development and validation of risk perception scales, socioeconomic and cultural influences, healthcare workers' viewpoints, and strategies for risk communication. Most of the studies recommend culturally adapted communication, institutional support for healthcare workers, and trustworthy public health messages. However, reliance on self-reported data, the lack of longitudinal insights, and poor underrepresentation for diverse demographic groups are limitations that suggest a yearning for more robust and representative research. This review contributes to a better understanding of risk perception with recommendations to improve public health responses in future epidemics.

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Risk Perception, Public Health Communication, COVID-19, Socioeconomic Status, Healthcare Workers' Psychological Impact

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Experimental study to assess effectiveness of simulation and its role in improving Neonatal resuscitation competencies among nursing officers in selected Index hospital Indore in the year.

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simulation, Neonatal resuscitation, competencies.

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The project focuses on accurately forecasting the trajectories and intensities of tropical cyclones. With climate change increasing the frequency and strength of these storms, precise predictions are crucial for minimizing the potential loss of life and property. This study integrates historical meteorological data with advanced machine learning algorithms to analyze patterns and predict cyclone paths and intensities. Key features like pressure, wind speed, and temperature are used as inputs, enhancing model accuracy. By providing early warnings, the project aims to support disaster preparedness efforts, reducing the social and economic impacts of tropical cyclones

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- Deep Learning, LSTM (Long Short-Term Memory), CNN (Convolutional Neural Networks), Time Series Analysis, Sequential Data Processing, Predictive Modeling, Feature Extraction, Data Augmentation, Cyclone Intensity Prediction.

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