VOLUME 14, ISSUE 1, JANUARY 2025

Abstract: Hydraulic properties of soil depends on their texture which represents the relative proportion of sand, silt and clay particles in the soil. An android application was developed to find the soil texture based on GPS location of the mobile user. The application find the texture based on the position of location specific dot on the colour coded map of soil texture of Haryana state. The app then shows texture based saturated hydraulic conductivity, moisture content at field capacity, moisture content at wilting point and available soil moisture and calculates the irrigation depth required for major crop based on root zone depth and pre defined moisture deficit in the soil. The application was developed on freely available MIT App Inventor web platform.

Keywords: Soil texture, hydraulic properties, crop water requirement, MIT App Inventor

Abstract: Traffic sign detection and identification not only support driver assistance technologies but also play a vital role in enhancing traffic management. These capabilities are essential for ensuring safe transportation and effective operation of self driving vehicles, aiding in real-time decision making for both human and autonomous drivers. Recognizing traffic signs in Bangladesh is especially difficult due to the country’s unique driving environment. This includes non-standard signage, diverse road conditions, and the frequent presence of pedestrians and livestock on roadways. By utilizing technologies like machine learning and computer vision, these systems can be tailored to local contexts, ultimately enhancing roadway safety in Bangladesh. This research proposes a smart assistant system that utilizes a dataset of 6,000 diverse traffic sign images from Bangladeshi road environments to improve road safety, especially in regions with potential driver compliance issues. The dataset encompasses 41 traffic sign categories and presents various real-world challenges, including faded color, weather conditions, blurring and vibration, occlusion, variable size of traffic sign and low light conditions. To enhance the dataset’s robustness, data augmentation techniques such as random rotations, shearing and zooming were applied. We trained the YOLOv10 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved significant results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification.

Keywords: TSR, Smart Traffic, Feature Extraction,Traffic Sign,YOLOv10,Computer Vision

Abstract: This paper presents work leveraging the recommendations of generative artificial intelligence (AI) tools such as large language models (LLMs) to create suitable AI models for automated image-based diagnosis of chronic kidney disease (CKD) within the context of a comprehensive AI-driven healthcare system. The LLMs suggested the synthesis of image-based AI solutions such as convolutional neural networks (CNNs) and these suggestions were followed meticulously to build AI models that were then trained on computed tomography (CT) image data representing the normal kidney state as well as the presence of cysts, stones and tumors and then tasked with the diagnosis of CKD based on the classification of the input CT images. Featuring reasonable performance metrics, the resulting AI models demonstrated the effectiveness of generative AI as a tool in the synthesis, training, testing and deployment of practical AI models within healthcare settings.

Keywords: Generative Artificial Intelligence (AI), Large Language Model (LLM), Convolutional Neural Network (CNN), TensorFlow, Healthcare System, Disease Diagnosis and Prediction, Chronic Kidney Disease (CKD).

Abstract: Recently, the object classification in digital images and videos has been addressed in various research works. Convolutional neural networks (CNN) are effective to process image data, while long-short term memory (LSTM) networks are effective to process sequence data. However, when these two technologies are combined, the result is a solution to challenging computer vision problems, such as video classification. The process of image classification involves passing an image to a classifier, which can be either a trained CNN or a classical classifier and obtaining class predictions. An LSTM is engineered specifically to operate with a data sequence, as it considers all the previous inputs when producing an output. LSTMs are a form of neural network known as a Recurrent Neural Network (RNN). In general, RNNs are not known to be effective in addressing the long-term dependencies in the input sequence due to a problem known as the vanishing gradient problem. LSTMs are created to circumvent the vanishing gradient, thereby enabling an LSTM cell to retain context for lengthy input sequences. This paper is intended to rapidly design a novel image classification method by using Conv2D and LSTM algorithms. The number of filters, the size of the filters, the activation function, and the padding mode are among the numerous parameters that the Conv2D function accepts. The HiSVidClassifer method applied time-distributed Conv2D layers, followed by MaxPooling2D and Dropout layers. The Flatten layer is used to flatten the feature extracted from the Conv2D layers, which are then transmitted to an LSTM layer for analysis to classify and detect the object. Our HiSVidClassifer method is trained and evaluated on UCF50 dataset. It achieved outstanding results with low loss equals to 0.1935, and good accuracy equals to 95.08%, compared to ConvLSTM method which obtained loss equal to 0.3773 and the accuracy equal to 87.70%.

Abstract: As the global demand for secure and efficient voting processes increases, including biometric technology such as face detection and identification into voting systems appears to be a possible response. This literature review explores the advancements in utilizing deep learning-oriented face recognition within smart voting systems. Begin-ning with a concise summary of traditional voting methods and their inherent vulnerabilities, this paper examines the current role of face detection technologies in enhancing voter authentication and preventing electoral fraud. For real-time voter verification, the paper looks at and assesses the performance of a number of deep learning models, includ-ing convolution-based neural networks (CNNs). Additionally, key challenges related to accuracy, data security, and ethical concerns are discussed. By carefully analysing both new and existing systems, this study explains how deep learning has the potential to revolutionize voting processes. In order to ensure scalability, equity, and security, it also points out areas that require further research and development.

Keywords: Face Recognition, Deep learning, Convolution neural network.

Abstract: Thyroid disorders affect metabolism, mood, and health, often going undetected until severe. This project develops a machine learning-based system for early detection and classification of thyroid conditions like hypothyroidism and hyperthyroidism using health data from CSV datasets. It also predicts recurrence risk and provides personalized health recommendations, including diet, meditation, and medication advice. Powered by Python, Flask, and SQLite, with XG Boost and Cat Boost algorithms, the system ensures high accuracy. By integrating robust preprocessing, exploratory analysis, and evaluation, it empowers users with actionable insights for proactive thyroid disorder management.

Keywords: Thyroid Disorder, Thyroid Classification, Thyroid Recurrence, Hypothyroidism, Hyperthyroidism, Personalized Advice, Health Recommendations.

Abstract: Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analysed vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.

Keywords: Drug discovery, convoluted neural networks, machine learning, pharmacological research, artificial intelligence.

Abstract: In a technologically advancing world, the evaluation of answers should happen rapidly and with greater accuracy. However, unlike objective answers, subjective answers make it difficult for an automated system to evaluate them accurately. This is because subjective answers are hard to evaluate using static content and finding a dynamic capability that caters to content, meaning, order and structure for subjective type answer evaluation is not so easy. This study represents an automated evaluation system for handwritten as well as textual answer sheets making use of ML and NLP for the evaluation. This survey is all about a system that converts the answers written on the answer sheets into their digital text data, then check whether answer of each question is correct or not. This study comprises of various “Machine Learning” algorithm to recognize and digitize text from handwritten forms. It also analyzes the answer of a student based on keyword matching, semantic similarity and correct grammar and according to that it assigns marks for their given answer using various “Machine Learning” techniques and algorithms. These systems help to minimize biased marking scheme and promotes fair grading. Also, ensuring consistent evaluation and less human work. An overview has been provided, which includes its evolution and effectiveness of various Machine Learning (ML) techniques to improve “Subjective answer evaluation systems”.

Keywords: Optical Character Recognition (OCR), Convolutional Neural Networks (CNN), Machine learning (ML), Natural Language Processing (NLP), Large Language Models (LLM), Subjective Answer Assessment.

Abstract: In this paper a proposal for e-business simple job seeker is introduced. It is aimed at developing an online e- Portal for the Placement Details for job seekers as Anyone- to- Anyone(A2A) model, a wider one of e-Business's models encompasses the entire business model of organizations (government, citizens, business, employees, customers, suppliers, partners, and value chain) such as Anyone- to- Anyone( A2A), Business-to- Consumer (B2C), Business-to- Employee (B2E) and Business-to-Business (B2B). Electronic business (e-business) is about enabling organizations to cohesively bring together their processes and the Internet technologies for cost effectiveness, efficiency and better relationships among partners. These partners could be business organizations, customers, suppliers, government departments or citizens, e-business's concepts and technologies can be applied in various areas of everyday life including, but not limited to, businesses, industry, government and education. However, its pervasive applications are found in commerce, government and services sectors of the economy. Index Terms: About four key words or phrases in alphabetical order, separated by commas.

Abstract: Optimum water use in agriculture is more crucial nowadays. This study explores the transformative potential of CropWAT software in enhancing water efficiency and increasing crop yields through precise irrigation scheduling. Our primary goal is to elevate farmers' awareness about the benefits of this advanced tool, particularly in the drought-prone Marathwada region of Maharashtra. We conducted a comparative analysis of two farms: On one land an abundant water supply and another less irrigation water with different types of agricultural soil, as fewer irrigation water sources in Marathwada, known for its challenging water scarcity issues. By considering local climatic, soil, and crop data in CropWAT, we were able to accurately predict the irrigation needs for each crop in the field. The farm in which the water-rich area avoided of over-irrigation, while the farm with less water was given the optimum depth of water then we achieved improvements in water use efficiency. In both scenarios, CropWAT's irrigation scheduling led to significant increases in crop yields. This study highlights the game-changing potential of CropWAT software as a critical tool for modern agriculture. By adopting precise irrigation management strategies, farmers in Marathwada and similar regions can not only conserve precious water resources but also enhance their productivity and contribute to sustainable farming practices. Our findings underscore the importance of embracing technological innovations to secure the future of agriculture in water-challenged environments.

Keywords: CropWAT Module, Meteorological Data, Reference Evapotranspiration, Crop Water Requirements, Irrigation Scheduling, Cropping Pattern, Water Supply Scheme

Abstract: Predictive modeling in drug safety utilizes artificial intelligence (AI) to proactively identify and mitigate adverse drug reactions (ADRs), significantly enhancing pharmacovigilance efforts. By analyzing massive datasets encompassing patient demographics, medical histories, drug interactions, genetic predispositions, and clinical trial data, AI algorithms can identify intricate patterns and predict potential risks with unprecedented accuracy. This surpasses the limitations of traditional methods, which often rely on retrospective analysis of adverse event reports. 1 Machine learning techniques, such as deep learning and natural language processing, play a pivotal role in extracting valuable insights from diverse data sources, including electronic health records (EHRs), social media, and scientific literature. Deep learning models, for example, can analyze complex medical images and identify subtle biomarkers associated with drug toxicity. Natural language processing enables the extraction of relevant information from unstructured text data, such as clinical notes and patient narratives, allowing for a more comprehensive understanding of ADRs. AI-powered predictive models can forecast the likelihood of specific ADRs in individual patients, identify high-risk populations, and even suggest optimal drug dosages and treatment regimens. 6 This personalized approach to drug safety enhances patient outcomes by minimizing the risk of serious adverse events. Furthermore, AI-driven pharmacovigilance systems can continuously monitor real-world drug usage data, detect emerging safety signals in near real-time, and facilitate rapid regulatory responses. By analyzing large-scale data streams, these systems can identify unexpected safety concerns that may have gone unnoticed by traditional surveillance methods. 8 This proactive approach empowers regulatory agencies to swiftly implement necessary safety measures, such as issuing warnings, modifying drug labels, or even withdrawing a drug from the market if necessary. The integration of AI into drug safety practices has the potential to revolutionize how medications are developed, monitored, and used. By leveraging the power of AI, the pharmaceutical industry can accelerate the development of safer and more effective medications, improve patient outcomes, and enhance the overall safety profile of the global drug supply.

Keywords: Adverse Drug Reactions (ADRs), Pharmacovigilance, Drug Safety, Artificial Intelligence (AI), Machine Learning (ML), Predictive Modeling, Data Science ,Precision Medicine.

Abstract: In order to carry real-time traffic like video, IP networks, especially intranets recently began to deal with different levels of priority for communication flows. Traffic prioritization is the key to meeting the demands of real-time traffic, which are much more stringent than mere data traffic, such as file transfers. Now that the question: “How should we specify different levels of priority?” is reasonably understood, the challenge is to answer this new one: “How should we charge for these different levels of priority by understanding Network Security?” This document discusses the introduction of Quality of Service (QoS) in the corporate network of a large industrial enterprise. Its main contributions are a charging model for different types of traffic with different levels of priority, a network simulation for verifying the impact of a QoS implementation and detailed understanding of network security nitty-gritties.

Abstract: Sentiment analysis involves determining the polarity of statements—classifying them as positive, neutral, negative, satisfied, or frustrated—based on opinions, feedback, and comments shared on social media or e-commerce platforms. It is a key area in text mining for analyzing and classifying user sentiments. This work focuses on identifying the sentiments of tweets and retweets on Reddit by searching for specific keywords and evaluating their polarity as positive or negative. Sentiment analysis is performed using feature selection for scoring words, with classification algorithms employed for training, testing, and sentiment evaluation. Performance metrics such as accuracy, precision, and time are compared across ANN, CNN, and LSTM models.

Keywords: Fuzzy sentiment, tweet sentiment analysis, sentiment polarity

Abstract: Retrieval-augmented generation (RAG) is an emerging AI framework that enhances the capabilities of generative language models by integrating external retrieval mechanisms, enabling them to produce contextually relevant and factually grounded responses. This hybrid approach combines the precision of retrieval systems with the generative richness of advanced transformer models, reducing hallucinations and making RAG ideal for applications such as question-answering, knowledge management, and customer service automation. However, the unique architecture of RAG systems introduces critical security challenges, data privacy risks, model poisoning, inference attacks, and unauthorized access. This paper provides a comprehensive analysis of RAG systems, starting with a definition of their core architecture and a detailed exploration of the frameworks that constitute RAG, such as LangChain and Haystack[1]. We then identify common architectural patterns, such as pipeline and cascade architectures, and discuss the supporting systems that underpin RAG functionality, including vector stores and orchestration layers. Building upon this foundation, we analyze the security threats faced by RAG frameworks and offer practical recommendations to mitigate these risks. Key strategies include implementing data access controls, secure communication protocols, model integrity checks, and rigorous data labeling and training processes. By integrating security measures directly into the design and deployment of RAG systems, this paper outlines a secure framework that balances functionality and protection. The proposed framework provides actionable insights for developers and organizations aiming to deploy RAG applications in sensitive and dynamic environments while safeguarding data and ensuring compliance.

Keywords: Retrieval-augmented generation (RAG), AI Security, Secure Framework, Building Security in AI.

Abstract: The W-Safe project aims to enhance women's safety and empowerment by leveraging modern mobile technology. Designed as an Android-based application, the app integrates innovative features to provide real-time support during emergencies. Utilizing Android Studio, Java, and XML, the app is built to be both reliable and user-friendly, offering functionalities such as an SOS panic button, live GPS tracking, and access to critical helplines. These features enable immediate assistance and help send location details to emergency contacts. In addition to offering emergency support, the W-Safe app emphasizes empowerment through educational content. It provides valuable resources on women's rights, self-defense techniques, and legal safety tips, aiming to raise awareness and promote long-term prevention. The app also incorporates community-driven features to foster collaboration and enhance social awareness. By addressing the specific safety needs of women, W-Safe recognizes the limitations of traditional safety measures and capitalizes on the widespread use of smartphones. It promotes self-reliance while aiming to create safer environments for women from diverse backgrounds. The app is designed to be scalable, allowing for future updates that may include AI-based threat detection, wearable device integration, and predictive alerts, all enhancing its functionality and adaptability. In summary, the W-Safe project is a mobile technology-driven solution that not only provides immediate assistance but also contributes to long-term empowerment, safety awareness, and social progress. Its scalability ensures that it can evolve to meet the ever-changing landscape of societal challenges.

Keywords: Women's Safety, Real-Time Support, Empowerment, Mobile Technology.

Abstract: This scholarly treatise undertakes a rigorous critique of the phenomenon of bank fraud within the Indian banking sector, with an emphasis on the regulatory framework delineated for fraud mitigation. The investigation delves into the intricate causes of bank fraud, highlighting the pivotal roles of deficient internal controls, inadequate employee training, and the onerous demands placed upon banking personnel. It meticulously evaluates the adherence of bank employees to established fraud prevention protocols, particularly those stipulated by the Reserve Bank of India (RBI). Furthermore, the research provides a sophisticated analysis of bank employees' perceptions of the efficacy of these preventive measures and their cognizance of varied fraud typologies. The findings elucidate substantial shortcomings in the implementation of internal control mechanisms and identify critical contributory factors to bank fraud, including insufficient training, employee overload, competitive pressures, and low compliance with RBI guidelines. The research underscores the necessity for banks to proactively strengthen internal controls and augment employee training programs. This comprehensive assessment proffers substantive recommendations to refine extant fraud prevention strategies, thereby mitigating financial losses and reinforcing the integrity of the Indian banking industry.

Keywords: Banking Frauds, Fraud Detection, Risk Management, Reserve Bank of India.

Abstract: For the welfare of society and many working sectors, people keep working on improving the scope and features of technology to make tasks easier. Many of these profound technology helps law enforcements and corporates to protect their data privacy but in contrast, those are also being used to steal sensitive data and do various kinds of cybercrime. Cybercrimes involves usage of both coding and software tools to find vulnerabilities to crack a system but with the creation and advancement of Artificial Intelligence, such tasks become so much easier. AI also paved the way to make new updates and features in the cyber space. Foundation of Artificial Intelligence was laid with research in fields of brain, computation and electrical networks which made scientists to work on creating a computer that is capable of thinking on its own2. Geoffrey Hinton, the godfather of AI himself has that use of AI should be tread carefully14. Social media acts as a medium of large amount of photographic, video and audio content. These data can be used to create, edit and morph images, videos and audios with minimal effort. Propagation of a websites that has AI features are being used to morph images of person to past it in a pornographic content. Deepart.io is one of the websites that got banned because it allowed creation of morphed pornographic content1. Regulations and restrictive guidelines on social media for posting images are weak in some popular applications like telegram and twitter. It allows such morphed pornographic content to propagate in cyber space easily. It’s nearly impossible to find difference between a normal image and morphed image without use of technical softwares which is a primary cause for increasing victims of this cybercrime. Government organizations doesn’t provide enough awareness on this topic among people either. The simply and proper way to provide awareness and avoid these crimes is within the creation of the problem itself, which is the use of Artificial Intelligence itself.

Keywords: Deepfake, Artificial Intelligence, Pornography, Cyber Laws, Cybercrime, Victimization, Morphing.

Abstract: AI (Artificial Intelligence) is revolutionizing the education sector with game-changing solutions that enrich teaching and learning experiences. The leading edge of AI not only utilizes the third fathom but also analyzes counsels, distinguishes the strengths and weaknesses of students, and thus, a learning path designed towards his/her need will enable students to have a better chance of learning well for the subjects. The following piece is a deeper look at how AI is changing education. AI, for example, supports personalized learning experiences by tailoring lessons according to the individual requirements, preferences, and progression of students. AI Tutor Systems will analyze student performance and behavior.

Abstract: Pneumonia is a serious lung infection that afflicts millions worldwide, particularly children and the elderly. To be treated effectively, this requires prompt and accurate diagnosis. This project introduces a system that uses Convolutional Neural Networks (CNNs), a powerful deep learning tool, to make pneumonia detection easier and faster. Users can upload chest X-ray images, which the CNN model will analyze to identify signs of pneumonia with high accuracy and speed. By bringing the efficiency of advanced image analysis together with a user-friendly interface, this approach should bring diagnostic capabilities closer while improving issues such as data quality, privacy, and model reliability.

Keywords: Pneumonia, Convolutional neural network, Data augmentation, Deep learning, Accuracy.

Abstract: Kidney stone detection is a critical application in medical imaging aimed at aiding early diagnosis and treatment. This project presents a graphical user interface (GUI) application for automated kidney stone detection using image processing and machine learning techniques. Developed in Python, the system leverages libraries such as OpenCV, Tensor Flow, and Tkinter to create an intuitive, user-friendly tool for image analysis and classification.This tool demonstrates potential in assisting healthcare professionals with kidney stone detection, reducing manual effort and improving diagnostic accuracy. Future enhancements may include integrating real-time detection capabilities and expanding the classification model to cover additional medical imaging modalities. This project implements a kidney stone detection system using a graphical user interface (GUI) built with Python's Tkinter.

Abstract: This project implements a blockchain-based pharmaceutical supply chain management system using Flask, MongoDB, and SendGrid. It ensures the secure and transparent tracking of drug shipments across suppliers, manufacturers, distributors, and customers. Each transaction is recorded as a block in the blockchain, providing tamper-proof data integrity. The system supports email notifications for order confirmations, a dashboard for blockchain visualization, and interactive routes for role-based actions.

Abstract: Early and accurate detection of breast cancer is essential for improving patient outcomes and tailoring treatment strategies. This study introduces a two-step machine learning framework for symptom-based breast cancer detection and carcinoma type identification. The initial step utilizes Random Forest (RF) to detect the presence of breast cancer based on extracted symptoms. If cancer is detected, the second step confirms the carcinoma type, specifically identifying ductal carcinoma, using Gray-Level Co-Occurrence Matrix (GLCM) for feature extraction and RF classification. The proposed system demonstrates enhanced accuracy and reliability, leveraging the strength of feature-based methods and ensemble learning techniques. This paper provides an in-depth analysis of methodologies, results, and related datasets, emphasizing the practicality and effectiveness of the system in clinical applications.

Keywords: Breast cancer detection, GLCM, Random Forest, Ductal carcinoma, Feature extraction, Machine learning, Symptom-based analysis, Classification.

Abstract: Rainfall forecasting plays an important role in increasing agricultural production and decreasing associated risks resulting from climate change. This is especially because traditional approaches to rainfall forecasting often do not adequately capture the complex and nonlinear features of climate change. Hence, this research examines the applicability of using machine learning algorithms, such as decision tree regressors, for the yearly and monthly rain events based on the historical climate and geographical information. Apart from incorporating agricultural analysis to advise potential appropriate crops in accordance with the expected amount of rainfall, type of soil, as well as its pH level, the proposed application also brings experimental results affording better tools for decision-making for farmers and other stakeholders in the agricultural field. This study illustrates the possibility of applying quantitative approaches to promote sustainable farming practices and, therefore, achieve food security in conditions when weather patterns become volatile.

Keywords: Rainfall Prediction, Machine Learning, Decision Tree Regressor, Agricultural Analysis, Crop Recommendation.

Abstract: The abstract for the project "E-Learning Platform for Data Structures and Algorithms (DSA)" outlines its purpose of creating a comprehensive and interactive platform to simplify DSA learning. It focuses on offering structured tutorials, assessments, and certifications to help learners grasp complex concepts efficiently. The platform emphasizes accessibility and scalability to cater to both academic and professional development. Assessments are unlocked only after the completion of corresponding video tutorials, ensuring a sequential learning process. Additionally, it includes a feedback mechanism to improve user experience and a dedicated query support system to address course or platform-related issues. These features collectively aim to enhance learner engagement, provide personalized learning pathways, and ensure skill validation while addressing challenges like scalable content delivery and robust infrastructure for a seamless learning experience

Keywords: Supportive learning environment, empowering students.

Keywords: gas detection, water detection, Internet of Things (IoT), machine learning.

Abstract: The Iot-based Iv-bag Monitoring System Automates The  Monitoring Of IV Fluid Levels And Vital Signs To Enhance Patient Care. It Uses Arduino, Load Cell, Heartbeat And Temperature Sensors, Dc Motor For Bed Control, Bluetooth For Voice Commands, And Node Mcu For Emergency Alerts. This System Reduces The Need For Constant Manual Supervision And Supports Timely Intervention By Caregivers.

Keywords: Smart Healthcare,Patient Safety System,Fluid Level Detection

Abstract: Photonic Crystal Fiber based Surface Plasmon Resonance (PCF-SPR) is a biosensing technology that combines PCF and SPR to detect refractive index (RI) changes with high sensitivity. This study investigates a novel dual-core PCF-SPR biosensor, designed to cover the RI range of 1.31 to 1.40, enhancing sensitivity and resolution for high-performance biosensing applications. It also focusing on key performance metrics, including wavelength sensitivity (WS), amplitude sensitivity (AS), and figure of merit (FOM), while minimizing confinement loss (CL). The methodology employs COMSOL Multiphysics using the Finite Element Method (FEM) for numerical analysis, investigating the effects of geometrical parameters on CL, AS, and WS. The results demonstrate a maximum WS of 8000 nm/RIU, an AS of -1279.38 1/RIU, a competitive resolution of 1.25 × 10⁻⁵ RIU, and an impressive FOM of 250 RIU⁻¹, which significantly outperform previous models. This biosensor presents an innovative and effective solution for biosensing applications, providing exceptional performance in the target RI range and demonstrating a strong potential for practical use in medical diagnostics and other biosensing fields.

Keywords: Photonic Crystal Fiber (PCF), Surface Plasmon Resonance (SPR), Biosensing, Finite Element Method (FEM), Confinement Loss (CL), Wavelength Sensitivity (WS), Figure of Merit (FOM).

Abstract: The e Appointment system is a mobile application designed to facilitate the scheduling and management of appointments for service providers such as doctors and consultants. This system enables users to book appointments via a web interface, eliminating the need for traditional methods such as phone calls. Service providers can manage their availability, confirm or cancel appointments, and maintain an organized schedule. The system offers real-time availability updates, email notifications, appointment reminders, and rescheduling or cancellation options for users. By digitizing the booking process, the e-appointment system improves efficiency, reduces administrative tasks, and enhances the overall user experience.

Abstract: Introduction: Oral cancer is one of the foremost dangerous cancers which happens within the oral depth. Abuse of tobacco and smoking cigarettes are the essential chance variables for creating oral cancer [2]. Oral cancer conclusion at an early arrange can spare the lives of numerous individuals with appropriate treatment.

Abstract: Basically, OCR technology is applied to convert the printed Kannada text into machine-readable format. It will make the text extractible from a scanned document and a photograph so that Kannada literature will become easily digitalized and accessed. Our system will recognize words and characters in numerous typefaces and layouts including multi-column forms through complex algorithms and machine learning. The base of implementation is the Tesseract OCR engine that is excellent as far as recognition accuracy in texts is concerned, and well suited to the Kannada script. Experimental results reflect that our approach maintains the integrity of the original text without reducing human efforts in data entry. This paper supports the cause of preserving Kannada material in the regional language and its dissemination through this work. It adds up to the ever-increasing requirement for digital resources in these languages.

Abstract: Web scraping is a powerful technique used to extract data from websites, and when combined with Python’s BeautifulSoup library, it becomes an efficient tool for data collection and analysis. BeautifulSoup simplifies the process of parsing HTML and XML documents, enabling users to navigate and extract the desired content with ease. In the context of sentiment analysis, web scraping plays a crucial role in gathering large volumes of text data from sources such as social media, review websites, and blogs. This data is then processed and analyzed to determine the sentiment—positive, negative, or neutral—using natural language processing (NLP) techniques. By leveraging Python’s BeautifulSoup, developers can automate data extraction, clean the collected data, and feed it into sentiment analysis models. This integration of web scraping and sentiment analysis provides valuable insights into public opinions, customer feedback, and market trends, making it a critical tool for businesses, researchers, and analysts in decision-making and strategy development.

Keywords: Webs craping, Python, BeautifulSoup, Data collection, HTML, XML

Abstract: Background of the problem: Personality may affect the academic life of the students and participation of physical related activities may reduce the stress and improve the personality factors

Abstract: In The Aero Cube Surveillance System (ACSS) is a Cube Sat-inspired aerial surveillance module for advanced monitoring and data collection. Designed to operate in hard-to-reach or hazardous areas, ACSS captures high- resolution images, and environmental data, transmitting critical information in real time to a ground station. The ACSS optimizes bandwidth and power, ensuring efficient, responsive surveillance. Its modular design allows for easy customization with various sensors and cameras, enhancing its versatility across applications. The ACSS delivers a reliable, adaptable solution for rapid-response and continuous monitoring needs.

Keywords: Aerial surveillance module, modular design, rapid-response monitoring.

Abstract: The "Long Range Camera System via Wireless Sensor Network for Remote Tethering" offers a remote-controlled photography solution that captures high-quality images from afar without direct interaction. Utilizing energy-efficient communication protocols, it ensures reliable, long-range connectivity. Key words: Wireless sensor networks (WSNs), Image-capturing technology, Remote-controlled camera, Advanced wireless communication and Real-time data processing.

Abstract: Social media is a platform where many young people are getting bullied. As social networking sites are increasing, cyber bullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model automatically detect social media bullying actions. The objective of our project work is to show the implementation of NLP and LSTM which detects bullied tweets, posts, etc. A machine learning model is proposed to detect and prevent bullying on Twitter. Two classifiers i.e. NLP (Natural Language Processing) are used for identifying the complete sentence in the comments and LSTM (Long Short-Term Memory) for identification.

Abstract: The proposed project presents a LabVIEW-based automated sorting system designed for conveyor lines in manufacturing industries, aimed at improving efficiency and accuracy in product sorting. The system utilizes two sensors: one to measure the size of objects and another to detect their position on the conveyor. Based on predefined size criteria, logical operations trigger an actuator to sort objects into designated categories. A graphical user interface (GUI) on the LabVIEW front panel provides real-time monitoring and control, including indicators for size, position, and system activity. This system significantly reduces manual effort, enhances sorting precision, and ensures seamless integration into industrial workflows. Potential applications include quality control, material handling, and packaging, making it a cost-effective solution for automated production lines.

Keywords: automated sorting system, LabVIEW, pneumatic actuators, sensor integration, workflow automation

Abstract: This research presents the design and verification of a low-noise, low-power amplifier (LNA) optimized for high-performance applications such as wireless communication, IoT devices, and medical sensors. The design is implemented using 45nm CMOS technology in Cadence Virtuoso to achieve an optimal balance between noise figure, power efficiency, gain, and bandwidth. The proposed LNA incorporates inductive degeneration, resistive feedback, and cascading topologies to minimize thermal noise and enhance gain. The design undergoes extensive DC, AC, noise, transient, and Monte Carlo simulations to validate robustness. Post-layout verifications, including Design Rule Check (DRC) and Layout vs. Schematic (LVS), ensure fabrication compliance. The results demonstrate a power consumption of 22.4mW, making this design suitable for energy-efficient high-frequency applications.

Keywords: Low noise and Low Power Amplifier, 45nm CMOS Technology

Abstract: Blind voters often face significant challenges in participating in elections independently and securely, primarily due to the lack of accessible voting mechanisms. This paper introduces an AI-driven voting system designed specifically for blind individuals, leveraging Natural Language Processing (NLP) and speech recognition technologies to create a secure, accessible, and independent voting experience. The system enables voters to authenticate, navigate, and cast their ballots through intuitive voice-guided interactions, ensuring privacy and accuracy. By eliminating reliance on physical assistance, the solution upholds the principles of democracy and inclusivity. Anticipated outcomes include a 30% increase in voter participation among the blind and enhanced user satisfaction due to its streamlined and user-friendly interface. This innovative approach signifies a crucial step toward making elections more inclusive and accessible for all citizens.

Abstract: The "Academic Performance Indicator" is a web-based system designed to ensure fair, transparent, and accurate evaluation of faculty performance in higher education. Developed using Python, Flask, HTML, CSS, JavaScript, and SQLite3, this platform allows faculty members to calculate their Academic Performance Indicator (API) scores based on a structured and standardized framework. The system includes two types of roles: users (faculty members) and an admin. Faculty members can create personalized profiles, answer questions categorized into three sections, and upload relevant proof documents (PDF, JPEG, or PNG). The platform calculates scores dynamically and provides immediate feedback on eligibility for salary increments or promotions. Additionally, users can view and manage their profiles and scores through an intuitive interface. The admin has the authority to oversee all user profiles, scores, and submitted proofs. They can also download user documents for verification and record-keeping purposes. This system promotes a streamlined and efficient evaluation process, reducing subjectivity and ensuring consistency in assessing academic performance. This project serves as a valuable tool for institutions aiming to implement transparent faculty evaluation practices, ultimately contributing to professional growth and institutional development.

Abstract: This project presents a cost-effective autonomous robot equipped for both firefighting and restaurant service, utilizing AI, ML, and camera integration. The robot's firefighting function detects and extinguishes fire through advanced sensors and a DC pump. In restaurant mode, it autonomously navigates, avoids obstacles, recognizes tables, and delivers orders, with data stored in a cloud database. AI and ML algorithms allow continuous learning, improving both fire response and service efficiency. This versatile robot enhances safety and operational efficiency by reducing human intervention across critical applications.

Keywords: AI, Machine Learning, autonomous path finding, obstacle avoidance, robotics, camera integration

Abstract: With the growing demand for efficient, contactless delivery systems in both urban and rural environments, this project introduces an advanced Autonomous Delivery Robot designed to overcome existing logistical challenges. The robot features enhanced navigation capabilities achieved through the integration of sonar, IMU, RFID, and wheel encoders, ensuring accurate localization and dynamic path planning. Advanced obstacle detection and avoidance mechanisms allow the robot to navigate complex environments reliably. A secure package delivery system, equipped with RFID-based unlocking and real-time notifications, guarantees safe and user-friendly operations. The robot’s power system, comprising a Li-ion battery with intelligent BMS and solar panel support, ensures sustainability, extended runtime, and reduced downtime. Real-time monitoring through a live camera feed and communication via an interactive OLED display further enhance its functionality. By combining efficiency, sustainability, and advanced technology, this work redefines autonomous delivery solutions, offering a practical and scalable approach to modern logistics.

Keywords: Autonomous delivery, Navigation, Obstacle avoidance, Secure delivery, Sustainable power, Real-time monitoring

Abstract: The Aqua Nebula Smart Water Dispenser is an innovative device that provides clean, safe drinking water using two eco-friendly sources: air and rain. It uses air-to-water technology to pull moisture from the air and turn it into water, while a rain sensor detects rainfall and collects rainwater automatically. This dual-source system ensures you always have water, even in dry areas or places with limited water access. The water is purified through multiple filters to remove dirt, bacteria, and harmful substances, and make it safe to drink. The system also adds minerals to improve the taste and health benefits of the water. The rain sensor helps save water by collecting rainwater whenever it rains, reducing the need for other water sources. This makes AquaNebula not only convenient but also environmentally friendly. Its modern design fits well in homes, offices, or communities, and it uses energy efficiently to minimize its impact on the environment. Whether you live in a city or a rural area, AquaNebula is a smart, sustainable solution for clean water. By using air and rain, it ensures you always have access to safe drinking water while helping to protect the planet. AquaNebula is a step toward solving water scarcity and building a greener future for everyone.

Keywords: Air-to-water Generator, Smart water dispenser, Rain sensor.

Abstract: This research paper presents the design and implementation of a 32-bit single-cycle RISC-V (RV32I) processor using Verilog HDL, targeting FPGA-based deployment for educational and embedded system applications. RISC-V, an open-standard instruction set architecture (ISA), provides an alternative to proprietary architectures by offering flexibility, scalability, and ease of implementation. The processor is structured around the fundamental stages of instruction execution, including instruction fetch, decode, execute, memory access, and write-back. To enhance performance, a five-stage pipelining approach is incorporated, reducing execution time per instruction while maintaining design simplicity. Key modules implemented in the processor architecture include the program counter, instruction memory, register file, arithmetic logic unit (ALU), data memory, pipeline registers, multiplexers, and a hazard detection unit to address data and control hazards. The entire design is synthesized and validated using FPGA platforms such as Xilinx Spartan-6 and Spartan-3E, with functional verification conducted through Xilinx ISE and Vivado simulation tools. The processor achieves a maximum operating frequency of 32 MHz, with an estimated power consumption of 7.9 mW, as analyzed using the Xilinx Power Analyzer. The implementation demonstrates the feasibility of a low-cost, fully synthesizable RISC-V processor, offering an efficient and scalable solution for embedded system applications. Furthermore, the development framework includes assembling tools and automated test suites to validate the processor’s functionality. This work contributes to the advancement of open-source processor design, providing insights into the hardware realization of RISC-V and establishing a foundation for future research into more complex architectures.

Keywords: RISC-V, Instruction Set Architecture , Verilog, Simulation.

Abstract: A standalone solar power system is a self-sufficient renewable energy solution that harnesses solar energy to generate electricity, operating independently of the grid. This system integrates solar panels, a charge controller, a battery bank, and an inverter, synergistically working together to provide a reliable and sustainable source of energy. During the day, solar panels convert sunlight into DC power, which is then regulated by the charge controller and stored in the battery bank. The stored energy is subsequently converted into AC power by the inverter, supplying electricity to homes, businesses, or remote areas, thereby mitigating reliance on fossil fuels and promoting energy autonomy.

Abstract: The increasing demand for renewable energy has led to the development of battery-based solar PV systems. This system integrates solar photovoltaic (PV) panels, a battery bank, and power conversion systems to provide a reliable and efficient renewable energy supply. The battery bank stores excess energy generated by the solar PV panels during the day, allowing for a stable power supply during periods of low solar irradiance or at night. The system is designed to operate in both grid-connected and off-grid modes, providing flexibility and resilience. With advanced power management and control systems, the battery-based solar PV system optimizes energy yield, reduces energy losses, and enhances overall system efficiency. `

Abstract: The proposed project presents the VGG16 deep learning model, a 16-layer convolutional neural network renowned for its simplicity and effectiveness, by leveraging its pre-trained foundation on the ImageNet dataset. By fine-tuning VGG16's layers, it adapts to various image processing tasks such as image classification, object detection, and image enhancement. Through rigorous experiments on benchmark datasets, the model's ability to generalize across different datasets is tested, demonstrating high accuracy in classifying images and performing well in tasks like object detection and segmentation. The project explores VGG16's capability to generate meaningful image representations, crucial for applications like image retrieval and content-based filtering, thereby showcasing its significant improvement in modern image analysis challenges.

Keywords: Caltech 101 dataset, Convolutional Neural Networks, Deep Learning, Visual Geometry Group

Abstract: This project introduces an intelligent system that integrates custom-trained large language models (LLMs), RFID-based mode switching, and cloud-based APIs to enable natural, context-aware human-machine interaction on resource-constrained devices. The system operates in three modes—Student Mode, General Mode, and Visitors Mode—each tailored to specific user needs, such as educational support, everyday tasks, and quick information retrieval. RFID technology allows seamless mode switching, while cloud APIs handle resource-intensive tasks like speech-to-text (STT) and text-to-speech (TTS), ensuring real-time responsiveness on low-power hardware like microprocessors. Applications span education, IoT, healthcare, and customer support, enabling voice-activated smart devices, interactive kiosks, and accessibility tools. By combining affordability, scalability, and advanced AI capabilities, this project bridges the gap between cutting-edge technology and practical, real-world solutions, making AI-driven systems more accessible and impactful across industries.

Keywords: LLM, RFID, cloud APIs, STT, TTS, IoT, education, healthcare, accessibility, low-power hardware.

Abstract: This Independent solar powered PV system is a reliable and sustainable energy solution designed for remote or rural areas with limited or no grid connectivity. The system integrates solar panels, battery storage, and an inverter to generate, store, and distribute electricity. Solar panels convert sunlight into DC electricity, which is regulated by a charge controller and stored in batteries. The stored energy is converted into AC power by an inverter, making it usable for household appliances, schools, healthcare facilities, and other critical infrastructure.This system is ideal for powering rural homes, schools, healthcare facilities, and community centers. Its modular design and scalable architecture enable easy customization to meet specific energy requirements. Key benefits include energy independence, reduced energy costs, environmental benefits, and increased energy access.

Abstract: The proposed Railway Management System integrates automation technologies to enhance railway safety, efficiency, and management through the use of Arduino microcontrollers ,H-Bridge, DC motors, IR sensors and Zigbee.This system comprises two main modules:-The Station Module and the Train Module, each fulfilling distinct roles in managing train operations and station activities.The Station Module utilizes IR sensors to monitor platform availability,that will displayed in the LCD,ensuring optimal train scheduling and management. The Train Module controls train movement and speed via DC motors managed by H-Bridge drivers. Wireless communication between the Station and Train Modules is facilitated by Zigbee technology, allowing for seamless data transmission of platform status, train positions, crack alerts and Fire detection.Furthermore, the system incorporates an automatic gate control mechanism powered by DC motors, which operate based on inputs from IR sensors detecting the train’s proximity to level crossings.

Keywords: Arduino UNO, IR Sensor, LCD Display, Zigbee, DC Motor, H-bridge.

Abstract: The NODEMCU is a popular open-source IoT platform that enables developers to create innovative projects with ease. Based on the ESP8266 Wi-Fi SoC, NODEMCU provides a powerful and flexible framework for building IoT applications. This paper introduces NODEMCU and its integration with the Arduino platform, highlighting its key features, benefits, and potential applications. The Internet of Things (IoT) has revolutionized the way we live and work, enabling seamless communication between devices and the internet. NODEMCU is a popular IoT platform that provides a simple and intuitive way to develop IoT applications. With its integration with the Arduino platform, NODEMCU is accessible to a vast community.

Keywords: NodeMCU, Arduino, ESP8266, Wi-Fi, Microcontroller

Abstract: The Solar Energy-Based Home Automation System Using Mobile Bluetooth integrates renewable energy with advanced smart home technologies to enhance comfort, security, and energy efficiency. This system leverages solar energy as its primary power source and utilizes a Bluetooth-enabled mobile application for monitoring and controlling household appliances. It automates various functions, including lighting, ventilation, and home security, thereby reducing energy wastage and enhancing convenience. A significant feature of this system is the LPG detection capability, which triggers the automatic opening of windows using a servo motor and sends alerts to the user’s mobile device via Bluetooth for immediate response. The integration of an Arduino Uno microcontroller, HC-05 Bluetooth module, LCD display, LPG sensor, relay modules, and solar panels ensures reliable performance and cost-effectiveness.

Abstract: The *ACEBOTT ESP32 4WD Smart Robot Car Kit for Arduino* is a versatile and powerful platform designed for the development, learning, and prototyping of robotics systems. This project integrates the ESP32 microcontroller, known for its advanced processing power and integrated Wi-Fi and Bluetooth capabilities, with a four-wheel drive (4WD) chassis and various sensors, enabling both autonomous and remote-controlled navigation. The kit is compatible with the Arduino IDE, making it accessible to a wide range of users, from hobbyists to students and researchers.

Abstract: AI has become deeply ingrained in the everyday life. The matter in question does not only touch upon the mobile phones that almost everyone carries within easy reach. Today, voice assistants and smart speakers are mainly used to turn on music, turn off the lights or forecast the weather.AI chatbots are getting smarter. The use of new tech-nologies and the development of neural networks makes it possible to chat or answer questions, write a script, a scien-tific work, or program code. One of the key differences from previous GPTs is that the new version is trained to contin-ue the text and answer questions. The answers that the bot gives surprise users around the world. Yes, there are still questions about these answers and their validity, and everyone is sure that technology needs to be improved. For a technology to become revolutionary, it must find a better, new, breakthrough application. Although no, such an appli-cation has already been invented. Farcana decided to combine the functionality of the GPT chatbot and a voice assis-tant Index Terms - AI, mobile phones, voice assistants, smart speakers, neural networks, script writing, scientific work, validity of answers, technology improvement, revolutionary application, Farcana, GPT chatbot, voice assistant

Keywords: 1 2 X ESP32 DEV BOAR, MAX98357 I2S Class D Amplifier ,1 X INMP441 MEMS MICROPHONE, MINI MICRO PHONE, 1X IR PROXIMITY SENSOR.

Abstract: Traditional monitoring systems in coal mines are difficult to install, hazardous, and difficult to power. Because of the complexity of the mining environment and the wide range of operations performed in coal mines, it is vital to monitor and maintain the parameters in the background to increase the efficiency and safety of mineworkers. As a result, traditional monitoring methods cannot be relied on to ensure coal workers' safety. This research represents a ZigBee-based wireless monitoring system using a smart helmet. The presented wireless monitoring system is capable of detecting and transmitting critical parameters in coal mines such as methane gas, high temperature, humidity, and fire. In an emergency, this monitoring system transmits distress signals. A buzzer will sound if emergency conditions are detected, and the monitored variables will be displayed on the user interface machine.

Keywords: Coal mine Safety, Monitoring system, IoT, ZigBee, Smart helmet.

Abstract: Thyroid cancer is the deadliest cancer worldwide. It has been shown that early detection using low-dose computer tomography (LDCT) scans can reduce deaths caused by this disease. We aim to present a general framework for the detection of thyroid cancer in chest LDCT images. Our method consists of a nodule detector followed by a cancer predictor method. Our candidate extraction approach is expected to produce higher accuracy on the images of each subject and is also expected to increase precision for all recall values using convolutional neural networks. Our model insists over 3D CNN than other methods as they include different planes in detecting the nodules. In addition, our false positive reduction stage aims to successfully classify the candidates and is expected to increase precision. Our cancer predictor's ROC AUC curve is expected to determine how well our model can classify the nodules from the non-nodules based on the features and their properties.

Keywords: Pulmonary Nodule detection, Thyroid Cancer, Machine Learning, Deep Learning, KNN, Image processing, ANN, Random Forest, Logistic Regression.

Abstract: Cybercrime has become a major challenge in India, as digital platforms continue to evolve. With increasing incidents of financial fraud, hacking, identity theft, online bullying, and more, the role of state police forces has become critical in addressing cybercrime. This research paper examines the role of six Indian state police forces—Madhya Pradesh, Maharashtra, Delhi, Tamil Nadu, Karnataka, and Punjab—in mitigating cybercrime. Through a case study approach, the paper highlights specific incidents in each state, focusing on how police forces responded to cases of cybercrime, their strategies, technological advancements, collaborations with other agencies, and the challenges they faced. The research sheds light on both successes and areas for improvement in the fight against cybercrime in India.

Keywords: Cybercrime, State-Specific Approaches, Police, Indian Penal Act.

Abstract: Brain tumors are among the most challenging health issues because of their complexity and the critical need for early and accurate diagnosis. Magnetic resonance imaging provides excellent spatial resolution and soft tissue contrast, making it an indispensable tool for identifying abnormalities in the brain. It is highly used for tumor detection in the brain, but MRI scan analysis is time-consuming and prone to human errors because the appearance of a tumor may vary. This paper discusses multiclass classification for brain tumors using CNNs. The work proposes a method with a CNN model implemented in PyTorch to classify the MRI images into four categories: glioma, meningioma, pituitary, and no tumors. The experimental research was conducted using a dataset with varying tumor sizes, locations, shapes, and intensity of the images. For the rigorous evaluation of the model, the dataset of MRI scans was split into the training set and validation set. Techniques like dropout for regularization and data augmentation were used for optimizing CNN architecture to overcome overfitting. Experimental results show that the proposed model has a classification accuracy of 91.4%, which is more accurate than baseline methods. This indicates that it can be efficiently used for brain tumor diagnosis. Results obtained here highlight the potential of deep learning in clinical applications, where the technique provides enhanced diagnostic accuracy and reliability.

Keywords: Convolutional neural networks (CNNs), VGG, Brain Tumors, MRI, Image Classification, Medical Imaging.

Abstract: Deepfake technology has grown significantly in recent years, posing serious challenges in digital security and misinformation. This research focuses on detecting deepfake audio using machine learning techniques by extracting key audio features such as Mel-Frequency Cepstral Coefficients (MFCCs), mel spectrograms, chroma features, zero-crossing rates, spectral centroid, and spectral flatness. A Flask-based web application is developed for real-time deepfake detection, allowing users to upload files and receive instant classification results. Our methodology involves data preprocessing, feature extraction, and similarity-based classification. The system demonstrates high accuracy in distinguishing real from fake audio, providing a valuable tool doe media forensics and digital security applications.

Keywords: Deepfake detection, Audio forensics, Feature extraction, Spectral analysis, Digital Security.

Abstract: Sophisticated filtering and morphological procedures form the backbone of a comprehensive image processing methodology aimed at kidney stone diagnosis. The process initiates with the conversion of color images to grayscale, followed by the application of Adaptive Histogram Equalization (AHE) to enhance image contrast. To eliminate noise while preserving edges and ensuring the sharpness of critical features, a bilateral filter is employed. Otsu's adaptive thresholding technique then facilitates the differentiation of distinct stone sections. Further refinement of segmentation is achieved by filling gaps in the binary image and removing small objects. The real image is masked using the generated binary mask, and contrast is subsequently improved. The image is then reconverted to grayscale, high-intensity areas are highlighted, and the region of interest is selected. These systematic processing steps significantly enhance the precision and reliability of kidney stone detection. This methodology offers a novel combination of techniques, including bilateral filtering, advanced morphological procedures, and AHE, providing significant insights and improving precision in the field of medical imaging related to kidney stone diagnosis..

Keywords: Kidney stone detection · Ultra sound image · Otsu’s Thresholding · Bilateral Filtering.

Abstract: This dissertation explores the impact of easy financing options on the purchasing decisions of consumers in India, particularly in relation to smartphone sales, emphasizing the critical relationship between accessibility to financial services and consumer behavior in the technology market. The research employs quantitative analysis of sales figures, available financing options, and consumer demographics to reveal that enhanced financing accessibility significantly correlates with increased smartphone sales. Key findings indicate that flexible payment plans and low-interest loans are substantial drivers of consumer purchasing activity, suggesting that financial institutions play a pivotal role in shaping market dynamics. The significance of these findings extends beyond consumer electronics, offering insights applicable to the healthcare sector, where similar financing models could be employed to facilitate access to medical devices and services. By demonstrating that easy financing options can effectively stimulate demand, this study highlights the potential for integrating financial strategies into broader market development initiatives aimed at enhancing technology adoption in healthcare. Ultimately, the implications of this research suggest that policymakers and industry stakeholders should consider the integration of financing solutions as a strategic approach to stimulate not only consumer spending in technology but also improve access to essential health technologies, thus fostering an environment that promotes innovation and equitable healthcare delivery.

Abstract: In this research paper, energy efficient GNRFET operational amplifier for Pneumatic applications is designed and simulated at 45nm technology node. DC voltage gain, average power, unity gain bandwidth and output resistance have been computed using HSPICE. Recent work on GNRFET circuit simulations has shown that GNRFETs may have potential in low power applications. It has better DC Gain, low output resistance and extremely less average power as compared ot its CMOS counterpart. Further, the simulation studies have revealed that the performance of the proposed low voltage folded cascode Op Amp can be improved optimized for particle application. The proposed circuit is useful for Aeronautical Engineering, biomedical and other low power applications. In the proposed circuit, the DC Gain is 24.8% higher, Output Resistance is 26.92% lower as compared to Bulk Operational amplifier. Index terms: GNR, GNRFET, Op Amp, Simulation, DC Gain, Power Consumption, Output Resistance, Bandwidth, Phase Margin.