VOLUME 13, ISSUE 3, MARCH 2025 | IJIREEICE

Design of GNRFET based Static Random Access Memory for IoTs in Aeronautical Applications

admin — Tue, 04 Mar 2025 11:33:30 +0000

Abstract: In this paper, the Static Noise Margin (SNM) and Power consumption of SRAM at different voltage supply and temperatures of Static Random Access Memory for IoTs utilizing Energy efficient GNRFET Technology is simulated using hspice. Further, the Simulation of various Waveforms of the GNRFET SRAM have been presented. SNM is present in SRAM cell which is effect the stability in read operation of the SRAM cells. SRAM cell stability analysis is a based on Static Noise Margin (SNM) investigation when in read mode.. The SRAM cell SNM during read operations analyzing various alternatives to improve cell stability.. The role of GNRFET improves its power efficiency and speed which play vital role in Aeronautical Engineering for various IoT applications. SNM is 6.7@1V, Average Power is 2.24@1V, SNM Is 2.43@45oC, and Average Power is 1.25@45oC.

Index Terms: GNR, GNRFET, Power Consumption, Cell Ratio, CMOS, Pull-up Ratio, SNM), Nano-electronic.

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AI-POWERED PET HEALTH CHATBOT: REVOLUTIONIZING VETERINARY CARE THROUGH INTELLIGENT SYMPTOM ANALYSIS AND TELEMEDICINE INTEGRATION

admin — Tue, 18 Mar 2025 06:03:32 +0000

Abstract: The Pet Health Chatbot is a pioneering AI-powered conversational platform designed to provide pet owners with personalized, expert-backed health advice and care recommendations. By inputting their pet’s disease or health issue, users are guided through a comprehensive, symptom-assessing questionnaire to determine the severity of the condition. For mild cases, the chatbot offers tailored remedies and care suggestions, while more severe cases are seamlessly directed towards appointment booking with nearby, partnered veterinary hospitals. Users can effortlessly select a hospital, provide essential pet details, and receive instant confirmation and receipt of their booking. By streamlining the veterinary care process, the chatbot reduces wait times, promotes efficient appointment booking, and enhances overall pet care. This transformative platform revolutionizes the way pet owner access veterinary expertise, providing unparalleled convenience, accessibility, and personalized guidance.

Keywords: AI-Powered Chatbot, Pet Healthcare, Veterinary Telemedicine Disease Prediction, Natural Language Processing (NLP)

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THE FUTURE OF HUMAN-MACHINE COLLABORATION IN WORKPLACES: A THEORETICAL APPROACH

admin — Tue, 18 Mar 2025 06:24:28 +0000

Abstract: Human and machine collaboration transforms workplaces with the possibilities of efficiency and accuracy through machinery, as well as human innovation and flexibility. This examines this new technological artistic intelligence, including human and human skills, the theoretical fundamentals of cooperation between people and machines, focusing on robotics. Therefore, this study analyzes the interactions of trust, adaptability and common goals in the integration of human and machine workflows. It also discusses development, innovation and impacts on organizational effectiveness.

Keywords: Human and machine collaboration, innovation, theoretical fundamentals.

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VIRTUAL MOUSE USING HAND GESTURE RECOGNITION

admin — Wed, 19 Mar 2025 11:02:18 +0000

Abstract: The creation of a virtual mouse implemented through hand gestures is a big leap in the field of human computer interaction (HCI), granting a touch free way for people to control other devices. In this paper, we present the design and implementation of a virtual mouse system capable of controlling cursors and accessing GUIs based on performing hand gestures. The system uses computer vision techniques specifically the hand tracking algorithms and uses predefined hand gestures and converts them into mouse movement, click and scroll actions. To use computers without physical input devices, hand gestures are accurately detected using such tools as OpenCV and machine learning models. It provides a robust alternative to traditional input methods, if the touch or physical interaction is impractical, due to its reaction time, robustness in a large number of lighting conditions and ease of use. But in fact this project focuses on gesture recognition and the key challenges such as real time processing, accuracy etc.

Keywords: Hand Gesture Recognition, Hand Tracking Technology, Gesture-Controlled Interface, Human-Computer Interaction (HCI), Real-Time Tracking, Computer Vision, Touchless Interaction, Gesture Recognition Algorithm

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NATURE’S REMEDY ONLINE STORE: AYURVEDIC PRODUCTS FOR WELLNESS

admin — Thu, 20 Mar 2025 07:41:23 +0000

Abstract: The escalating interest in alternative medical practices, especially Ayurveda, has underscored the necessity for a more streamlined mechanism to recommend Ayurvedic treatments for prevalent ailments. This initiative advocates for the creation of a machine learning-driven platform that proposes Ayurvedic pharmaceuticals predicated on symptomatic presentations and diseases. The objective of this system is to amalgamate traditional Ayurvedic principles with contemporary technological advancements to deliver precise and individualized treatment suggestions. The proposed system employs a machine learning model that has been trained on an extensive dataset encompassing Ayurvedic medicinal flora, their characteristics, and their applications for an array of diseases. Through the analysis of the symptoms submitted by users, the model discerns potential diseases and endorses suitable Ayurvedic pharmaceuticals based on their efficacy in addressing the specific condition. Furthermore, the system integrates patient-centric variables, including age, gender, and medical background, to enhance the personalization of the drug recommendations. The utilization of machine learning algorithms, including classification, clustering, and regression methodologies, empowers the system to forecast the most appropriate Ayurvedic pharmaceuticals for the treatment of a designated disease. The recommendation system is constructed upon a user-centric interface, facilitating users to input their symptoms and acquire instantaneous drug recommendations accompanied by comprehensive elucidations of their therapeutic attributes. This system endeavours to reconcile the divide between traditional Ayurvedic medicine and the requirements of contemporary healthcare by furnishing accurate, evidence-informed, and personalized Ayurvedic pharmaceutical recommendations.

Keywords: Ayurvedic medicine, Herbal remedies, Natural treatment, Holistic healing, Traditional medicine, Home remedies, Plant-based medicine, Herbal formulations.

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AI-POWERED VIRTUAL ASSISTANT FOR AN EDUCATION PLATFORM (ACADEMIQ)

admin — Thu, 20 Mar 2025 09:01:52 +0000

Abstract: AcademIQ is an AI-powered virtual assistant designed to enhance learning experiences for students and support educators by providing real-time responses to academic queries, generating educational content, offering voice-based explanations, and creating personalized learning pathways. Utilizing Natural Language Processing (NLP), AI-driven content generation, and speech synthesis, AcademIQ improves traditional education by making learning more interactive and accessible. The system includes modules for question answering, content generation, voice explanations, personalized learning, and interactive engagement, ensuring a seamless user experience. AcademIQ is evaluated based on accuracy, adaptability, and user engagement, with future improvements planned for video-based explanations, real-time tutoring, and blockchain-based credentialing. This research contributes to the development of AI-driven educational tools, making learning more efficient and personalized.

Keywords: AI in Education, Virtual Assistant, Machine Learning, Natural Language Processing, Personalized Learning, AI-Powered Tutoring, Smart Educational Systems, Adaptive Learning, AI Content Generation.

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PREFERENCE-BASED VEHICLE SELECTION INTERFACE IN ONLINE CAB BOOKING

admin — Thu, 20 Mar 2025 09:41:49 +0000

Abstract: The evolution of online cab booking services has significantly transformed urban transportation, yet many existing platforms lack the ability to provide personalized vehicle selection based on user preferences. This study introduces an advanced preference-based vehicle selection interface that enhances the online cab booking experience by allowing users to filter vehicles based on factors such as comfort, cost, eco-friendliness, seating capacity, and additional features like Wi-Fi and air conditioning. The proposed system integrates a dynamic filtering mechanism that enables users to make informed decisions by offering real-time vehicle availability, detailed specifications, and an option to select a preferred driver based on gender. The interface is designed to provide a seamless, secure, and efficient booking experience while prioritizing accessibility and sustainability. By leveraging technologies such as HTML5, CSS3, JavaScript for the front end, Node.js for the back end, and MySQL/MongoDB for database management, the platform ensures a robust and scalable system. This research highlights the necessity of customization in online cab booking to improve user satisfaction and address the limitations of existing platforms. Future enhancements may include artificial intelligence-driven recommendations, integration with real-time traffic data for better ride estimations, and further expansion of accessibility features to cater to a broader range of user needs. The preference-based selection model presented in this study has the potential to revolutionize online cab booking services by bridging the gap between user expectations and current industry standards.

Keywords: Online Cab Booking, Vehicle Selection, User Preferences, Personalized Filters, Ride Customization, Real-time Availability, Transportation Efficiency, Eco-friendly Vehicles, Accessibility, AI-driven Recommendations

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SENSOR BASED ANOMALY DETECTION SYSTEM FOR SMART JUNCTIONS

admin — Thu, 20 Mar 2025 14:50:51 +0000

Abstract: The project “Sensor based anomaly Detection System for Smart Junctions” has been developed using C#.Net as front end and SQL server as backend. The project helps to identify the Burglar movement activities and process from CCTV video footage. In a large video surveillance setup, there can easily be thousands of cameras. Constantly monitoring all this video for suspicious behavior is a very resource-consuming task. In some setups, it is however mission critical that this is done. It could for instance be needed in order for a guard to be able to take action on an incident with as little delay as possible. To limit the resources needed to do this monitoring, it would be helpful if the video surveillance system itself, by analyzing the video, could somehow generate a warning if something suspicious or at least abnormal is happening. For identifying the anomaly, the user needs to monitor the video regularly. In such application the proposed anomaly detection model can be applied to effectively identify and creates alerts about the anomaly. The main purpose of this project Monitoring scheme of anomaly will helps to improve the efficiency of the alert mechanism and also identifying the suspected entry. And its provide Automatic Alert system. This can be implemented to any kind of video monitoring application such as road safety, anolomoly movement monitoring applications. The system need some initial training in order to understand what is normal and what is not. This desktop application can implement high level monitoring places.

Keywords: Road safety, Anomaly, Video Surveillance, Alert System, Sensors, Cameras, Smart Junctions.

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A COMPARATIVE ANALYSIS OF JACCARD AND COSINE SIMILARITY FOR PLAGIARISM DETECTION

admin — Thu, 20 Mar 2025 15:10:38 +0000

Abstract: Plagiarism, the unauthorized use or imitation of another’s work without proper acknowledgment, poses a significant challenge in academia, research, and professional content creation, amplified by the widespread sharing of digital information. Reliable plagiarism detection systems are essential to ensure originality and maintain integrity. This paper investigates two widely used algorithms—Jaccard and Cosine similarity—for their effectiveness in detecting textual similarities. Jaccard similarity excels in identifying exact or near-exact overlaps but struggles with rephrased content, whereas Cosine similarity captures deeper semantic similarities, including paraphrasing, but is computationally more demanding. Preprocessing techniques, such as tokenization, stop word removal, and stemming, are employed to optimize the algorithms’ performance. The research evaluates their strengths, limitations, and computational efficiency through a detailed comparative analysis, offering insights into their suitability for specific applications. The findings emphasize the importance of balancing detection accuracy with computational demands, guiding the selection of appropriate methods for plagiarism detection in various contexts.

Keywords: Plagiarism Detection, Cosine Similarity, Jaccard Similarity, Text Similarity, Text Preprocessing

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DETECTING PRODUCT DEMAND OVER TIME USING MACHINE LEARNING

admin — Thu, 20 Mar 2025 16:10:17 +0000

Abstract: Demand forecasting is essential for supply chain management and inventory control, but complex patterns and big datasets are difficult for standard statistical approaches to handle. In order to improve prediction accuracy, this study suggests a machine learning-based strategy that makes use of models like Random Forest Regressor, Gradient Boosting Regressor, Support Vector Regression, and Neural Networks. Preprocessing of the dataset includes train-test separation, scaling, label encoding, and data cleaning. Model performance is evaluated using evaluation measures like accuracy, precision, and recall, while demand patterns are revealed using visualisations like heatmaps and histograms. Neural networks and ensemble learning are combined to enhance predictions even further.This method offers a scalable and dependable demand forecasting solution by bridging the gap between traditional approaches and contemporary machine learning techniques. Businesses may improve inventory management, lessen stock imbalances, and boost profitability through improved resource utilisation and demand prediction by successfully identifying important influencing elements such product classifications and warehouse locations.

Keywords: Motivation of Machine learning Support Vector Regression, Lasso Regression, Random Forest, Gradient Boosting.

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