Volume 12 | Issue 8

 Abstract

Railway safety remains a critical concern worldwide, prompting the need for innovative solutions to prevent accidents and protect passengers and crew. This project proposes an AI-driven railway safety system leveraging computer vision and sensor technologies to proactively detect hazards. The system aims to revolutionize safety measures by swiftly identifying potential threats on railway tracks, such as obstacles, collisions, and derailments. By integrating advanced computer vision capabilities with sensor data analysis, the system enables real-time monitoring and prompt alerting of railway authorities. Methodologies include comprehensive data collection, AI model development, and seamless integration into existing railway infrastructure. Evaluation metrics and impact assessments will gauge the system's effectiveness in reducing accidents and enhancing safety. The ultimate goal of the project is to mitigate risks, significantly improve passenger safety, and optimize operational efficiency for railway authorities through preemptive hazard detection and rapid response protocols

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PROACTIVE AI-POWERED RAILWAY SAFETY SYSTEM

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 Abstract

This project introduces a wireless night lamp designed in the shape of a crescent moon, combining aesthetics with functionality. Title: Wireless Power Transmission Night Lamp with Arduino and Motion Sensors. The objective of this project is to create an energy-efficient and convenient lighting solution for nighttime illumination in various indoor settings. The system employs wireless power transmission technology to eliminate the need for traditional power cords, enhancing user convenience and safety. The core components of the system include an Arduino microcontroller, a wireless power transmitter, a receiver module, and passive infrared (PIR) motion sensors. The Arduino microcontroller serves as the central control unit, orchestrating the operation of the system based on input from the motion sensors. The wireless power transmitter, based on resonant inductive coupling, wirelessly transfers power to the receiver module, which powers the night lamp. The incorporation of motion sensors enables the system to activate the night lamp automatically in response to detected motion within a predefined range. This feature enhances energy efficiency by ensuring that the lamp only illuminates when needed, thus conserving power during periods of inactivity. Additionally, the use of motion sensors enhances user convenience by providing hands-free operation, eliminating the need for manual activation. The implementation of the wireless power transmission night lamp system involves hardware design, including circuitry for power transmission and reception, as well as software development for Arduino programming to control the system's functionality. The system's design emphasizes simplicity, affordability, and reliability, making it suitable for deployment in various indoor environments such as bedrooms and hallways. Overall, this project demonstrates the feasibility and effectiveness of utilizing Arduino microcontrollers and motion sensors to create a wireless power transmission night lamp system. The system's energy efficient operation, convenience, and ease of deployment make it a promising solution for enhancing nighttime illumination in indoor settings while minimizing energy consumption and improving user experience. The project aims to seamlessly blend artistic design with practicality, offering a unique and visually pleasing wireless night lamp that contributes to a cozy and ambient atmosphere in any room. It can be controlled through motion sensor and Arduino controller.

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 Keywords

Portable, convenient, efficient, wireless, innovative

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Smart healthcare systems have changed the game for doctors and patients alike by allowing for continuous data monitoring and analysis to improve diagnosis, treatment, and care overall. But there are serious worries about data security and privacy when sensitive medical information is integrated into digital platforms. In order to guarantee the safety of real-time health information in smart healthcare settings, this abstract offer a thorough solution. To protect patient information from prying eyes, our suggested system makes use of cutting-edge encryption methods. To provide end-to-end security throughout the data lifecycle, advanced encryption algorithms like RSA (Rivest-Shamir-Adleman) and AES (Advanced Encryption Standard) are used to encrypt data while it is in transit and at rest, respectively. To further reduce the likelihood of key theft and ensure that only authorized individuals have safe access to data, secure key management methods have been put in place.

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 Keywords

REAL-TIME MEDICAL DATA SECURITY SOLUTION FOR SMART HEALTHCARE

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 Abstract

In today's digital communication landscape, efficient analysis of phone call conversations is imperative for various applications, including customer service enhancement and market research. This project proposes a novel approach leveraging Natural Language Processing (NLP) techniques to identify conversation threads in phone calls. The methodology encompasses preprocessing audio, converting it to text, and employing NLP algorithms such as summarization, topic modelling, and sentiment analysis for comprehensive analysis. Key components include implementing a robust speech-to-text conversion system using deep learning models fine-tuned on phone call data, followed by NLP analysis to parse and analyze transcribed text for pattern identification. Thread identification algorithms are developed based on semantic coherence and contextual cues, facilitating the segmentation of conversations into coherent threads. An intuitive user interface is designed to visualize and interact with identified conversation threads efficiently. The system's accuracy, scalability, and real-world applicability are evaluated rigorously across diverse datasets, with continual optimization to enhance performance. Evaluation metrics include precision, recall, and F1-score, providing insights into the system's effectiveness in identifying conversation topics and patterns.

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 Keywords

Natural Language Processing (NLP), speech-to-text, scalability, metrics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Student Document Management System based on Ethereum Blockchain project aims to revolutionize the traditional approach to academic record keeping by leveraging the decentralized and secure features of block chain technology. The system seeks to provide a tamper-proof and transparent platform for the storage, verification, and accessibility of student documents. Through the integration of smart contracts on the Ethereum block chain, the project aims to ensure data security, streamline the verification process, and reduce the risk of data loss. By prioritizing user authentication, efficient document upload and verification, and a user-friendly interface, the system intends to offer a comprehensive solution for educational institutions, students, and employers, contributing to the enhancement of overall data integrity and accessibility in the academic sector. The project also emphasizes rigorous testing, scalability considerations, and thorough documentation, anticipating a positive impact on educational practices and the evolution of secure document management systems

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 Keywords

block chain

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 Abstract

The project introduces an advanced safety jacket tailored explicitly for defense and rescue missions. It incorporates cutting-edge features to enhance safety, communication, and situational awareness during operations. Central to its functionality is the seamless integration with an AI assistant, providing real-time support to soldiers and establishing connectivity with their environment. Key components include a precision GPS sensor for accurate location tracking, ensuring effective coordination in emergencies. The AI assistant acts as a crucial bridge in communication, relaying vital information like location, heart rate and voice recordings to command centers and team members. Going beyond traditional distress signals, the AI assistant offers navigational support by interpreting voice commands, assisting soldiers in unfamiliar terrain, and dynamically adjusting routes based on live data. This comprehensive system not only improves the safety and efficiency of individual soldiers but also equips commanders with valuable insights from soldiers' status and environmental conditions, facilitating informed decision-making. In essence, this adaptable safety jacket represents a significant advancement in military gear, leveraging state-of-the-art technology to safeguard and enhance the success of military personnel in diverse operational landscapes

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 Keywords

Advanced safetyjacket, Defense and rescue missions, Cutting-edge features, Safety enhancement, Situational awareness, AI assistant integration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Smart Street Light Automation System with Fault Detection aims to revolutionize urban lighting infrastructure by introducing an integrated solution that combines adaptive control mechanisms and proactive maintenance capabilities. Leveraging advanced light sensors, microcontrollers, and communication modules, the system autonomously adjusts street light brightness levels in response to ambient lighting conditions, ensuring optimal visibility while minimizing energy consumption. Moreover, the incorporation of fault detection sensors, including temperature and current sensors, enables real-time monitoring of street light health, facilitating the early detection of anomalies such as overheating or electrical faults. By promptly identifying and reporting faults to a centralized monitoring station, the system enables expedited maintenance interventions, thus reducing downtime and enhancing overall system reliability. Through its innovative approach to street light management, this project aims to contribute to the development of smarter and more sustainable cities, where efficient lighting infrastructure plays a crucial role in enhancing safety, comfort, and energy efficiency for residents and visitors alike

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 Keywords

fault detection, urban lighting infrastructure, energy consumption, real-time monitoring, sustainable city, microcontrollers.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This Project presents an innovative vulnerabilities inherent in traditional data management systems. This project aims to bridge data security model for environmental this gap by proposing an innovative data security monitoring, integrating blockchain technology model that integrates blockchain technology with advanced cryptographic techniques. The advanced cryptographic techniques, specifically the model incorporates the Twofish algorithm for Twofish algorithm for encryption and the RSA encryption and the RSA algorithm for algorithm for decryption, along with the utilization decryption, ensuring a high level of data of smart contracts. Confidentiality and integrity by employing decentralized ledger, the system blockchain's One of the primary objectives is to enhance the enhances transparency and traceability in confidentiality and integrity of environmental data. Environmental data transactions. Smart With the exponential growth in data collection and contracts are integrated into the blockchain transmission in environmental monitoring systems, framework, automating processes and enforcing ensuring that sensitive information remains security protocols. The Twofish and RSA confidential and unaltered is paramount. By combination fortifies the protection of sensitive leveraging the Twofish algorithm for encryption, information, making it resistant to unauthorize the project aims to provide robust protection access and tampering. This comprehensive against unauthorized access and data breaches. Approach aims to address data security Twofish, known for its strong encryption challenges in environmental monitoring, capabilities, offers a formidable defense providing a robust and trustworthy solution. mechanism, thereby safeguarding sensitive. environmental data from malicious actors and cyber threats

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 Keywords

Rivest-Shamir-Adlemen (RSA), Algorithm, Neural Network, Machine Learning, Confidentiality, Smart contracts.

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 Abstract

The digital revolution has transformed the way we transact financial transactions. With smart phones and internet connectivity rising, mobile payment systems such as UPI have become increasingly popular. Developed by NPCI, UPI is an instant money transfer and payment system that allows users to make instant payments using their smartphones. However, the platform's widespread adoption and effective use come with a number of challenges. This paper systematically examines the challenges faced in the UPI domain. From security concerns to interoperability issues, user awareness issues, and technological limitations, the paper examines the best practices for optimizing UPI usage, focusing strongly on user education, strong security measures, smooth interoperability, and continuous technology development. This paper aims to provide stakeholders with valuable guidance on how to use UPI more efficiently and securely, leading to a better overall digital transaction experience.

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OPTIMIZING DIGITAL TRANSACTIONS: A LOOK AT CHALLENGES AND BEST PRACTICES FOR UNIFIED PAYMENT INTERFACES

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Satellite imagery plays a vital role in various fields, including agriculture, urban planning, disaster management, and environmental monitoring. Efficient and accurate classification of satellite images is essential for extracting valuable information and making informed decisions. In this study, we propose the use of artificial intelligence techniques for satellite image classification. A comprehensive dataset of labelled satellite images is collected, representing different land cover types or objects of interest. The dataset is pre-processed to enhance the image quality, remove noise, and normalize the data. Data augmentation techniques such as rotation, scaling, and flipping are applied to increase the dataset size and improve the model's generalization ability. Future research directions may include exploring advanced deep learning architectures, such as attention mechanisms or graph neural networks, to further improve the classification performance. Additionally, the integration of multi-sensor satellite data and temporal analysis can enhance the capabilities of the classification models for dynamic monitoring and change detection applications

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SATELLITE IMAGES CLASSIFICATION BY USING ARTIFICIAL INTELLIGENCE TECHNIQUES

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Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating accurate and timely diagnosis for effective management and prevention. The integration of artificial intelligence (AI) techniques holds promise in enhancing the precision and efficiency of CVD diagnosis. This abstract outlines the framework and significance of employing AI in CVD diagnosis for a comprehensive project. The proposed project aims to develop and implement an AI-driven system for the diagnosis of cardiovascular diseases. Leveraging machine learning algorithms, particularly deep learning models, the system will analyze diverse patient data, including medical history, vital signs, imaging results, and genetic markers. Through the integration of these heterogeneous data sources, the AI model will learn complex patterns and relationships indicative of CVD presence, progression, and risk factors. Furthermore, the project will emphasize interpretability and transparency, providing clinicians with insights into the decision-making process of the AI model. The deployment of the AI-driven CVD diagnosis system in clinical settings has the potential to revolutionize cardiovascular healthcare delivery.

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 Keywords

Integration of Artificial Intelligence (AI), AI-driven CVD diagnosis system

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 Abstract

The demand for authentic medicinal plants is on the rise, necessitating robust methods to ensure their integrity throughout the supply chain. In this paper, we present a novel approach leveraging machinelearning, specifically YOLOv8, for the precise identification of medicinal plants. Our methodology involves the curation of a diverse dataset comprising 30 distinct species of medicinal plants, which is used for rigorous training and testing of the model. The developed web application seamlessly integrates HTML, CSS, Bootstrap, JavaScript, React.JS, and Flask, offering a user-friendly interface for plant identification. Through comprehensive evaluation, our model demonstrates commendable performance metrics, contributing significantly to the authentication and preservation of medicinal plant integrity in the supply chain. This research not only addresses existing challenges but also paves the way for future advancements in leveraging machine learning for plant identification and supply chain management

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MEDICINAL PLANT IDENTIFICATION USING DEEP LEARNING

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 Abstract

The Animal Protection System with Vibration Fencing and Solar Power Integration addresses the challenge of safeguarding animals from potential harm caused by traditional electric fencing on farms. This innovative system employs ultrasonic sensors to detect animal proximity and utilizes a vibrator motor to produce fence vibrations, effectively deterring animals without causing injury. The system is equipped with dual power sources of energy and a conventional power supply to ensure continuous functionality. In solar energy, the vibrator motor is powered sustainably, promoting environmental efficiency. The sensors trigger the system, activating the appropriate power source and the vibrator motor. As a result, animals receive a non-harmful stimulus, prompting them to retreat from the fence. This eco-friendly and human approach offers a viable alternative to conventional electric fencing, contributing to animal welfare and crop protection in agricultural settings

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 Keywords

non-harmful, crop protection, Dual power sources, Agricultural Innovation, Ultrasonic sensor.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In an era of unprecedented global connectivity, effective communication across diverse linguistic landscapes is pivotal. This project, representing a sophisticated translation application, emerges as a beacon, fostering cross-cultural understanding by seamlessly bridging language barrier. A novel translation application that integrates cutting-edge technologies to provide comprehensive translation services across text, audio, and sign language. Utilizing computer vision for accurate sign language detection, the application employs the Hugging Face mBART model for sophisticated language processing. It leverages Google Cloud Services to ensure robust and scalable translations, supporting numerous languages with high accuracy. The user interface, built with Python Streamlit, offers an intuitive and interactive experience, making it accessible to users with varied technical backgrounds. This integrated solution aims to facilitate seamless communication across different modalities, enhancing accessibility and fostering global connections.

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Creative Commons Attribution 4.0 and The Open Definition

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Sign language translation, multi-language translation, computer vision, Hugging Face mBART model

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 Abstract

The Woven Design File Transfer Utility integrates LPC2148 and Node MCU for seamless file transfer between embedded systems and IoT networks. It ensures efficient, secure data transmission with real-time monitoring and control. Robust error handling safeguards data integrity, while scalability and flexibility cater to diverse applications. Compatibility and interoperability enable seamless integration, optimizing performance. User-friendly interfaces empower effortless configuration and management. Meticulous documentation demonstrates reliability across domains, unlocking potential in industrial automation, and beyond

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 Keywords

LPC2148 , NodeMCU,-DWIN HMI

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An This drone-based surveillance system proposes a groundbreaking solution to challenges in flood management by introducing proactive monitoring and real-time data dissemination. Equipped with high-resolution cameras and GPS capabilities, specialized drones continuously monitor flood affected areas, providing rescue teams with vital information on precise flood levels and the exact GPS locations of individuals in distress. This innovation empowers rescue teams to make informed, data-driven decisions, optimizing responses based on the severity of the situation. This solution distinguishes itself through its dynamic adaptability. In high-flood scenarios, the system recommends deploying boats for evacuation, while in low-flood situations, alternative rescue methods are employed. The ability to dynamically adjust the number of rescue team members based on real-time population data minimizes response time, reducing the risk of casualties among both flood victims and rescue teams. This comprehensive and proactive approach transforms the traditional reactive model, enhancing overall disaster management effectiveness and striving to diminish fatalities during flood emergencies.

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 Keywords

Autonomous Aerial Surveillance , Flood Rescue ,Technology Integration. Disaster Management, Real-time Monitoring , Climate Resilience.

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 Abstract

There is a sustainable worldwide effect, both in terms of disease and death, that is caused by pneumonia, which is a disorder that is easily affects the children and old age people. In recent days, pneumonia has easily affect the people because of pollution, increase in population and unhygienic conditions of living. It is a respiratory infection caused by bacteria and virus that affects the lungs. Many developed and developing nations also affected by pneumonia. Pneumonia can be identified by three different ways like CT scans, US scans and X-ray images. Due to the advancement in image recognition and deep learning technologies, computed vision devices predict infected and uninfected lung images with more accuracy .To make it success in rural areas, we employ a light weighted model to run through low-cost resource constraint devices and achieved remarkable training accuracy of 98.75%. Accuracy analyzed with three different learning rates and also compared with VGG-16, Mask-RCNN, and DenseNet121.

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 Keywords

Pneumonia, Respiratory infection, Light weight CNN, X-Ray, VGG-16, Mask-RCNN, DenseNet121.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In urban settings, efficient public transportation systems are essential for ensuring smooth commuting experiences for residents. Central to this is the management of bus passes, which traditionally involves cumbersome processes such as manual application submissions and in-person renewals, often leading to long queues and administrative bottlenecks. To address these challenges, this project proposes the development of an Virtual Bus Pass Management System (VBPMS) designed to streamline the application and renewal processes, thereby enhancing user convenience and administrative efficiency. The VBPMS will offer a user-friendly online platform accessible via web browsers and mobile devices, allowing commuters to apply for new bus passes or renew existing ones from the comfort of their homes or offices. Through intuitive interfaces and step-by-step guidance, users will be able to input their personal details, upload required documents, and select pass options tailored to their specific needs. Overall, the Online Bus Pass Management System aims to revolutionize the way bus passes are administered, shifting from traditional paper-based methods to a seamless online platform that enhances accessibility, transparency, and efficiency for both commuters and administrative personnel. By eliminating the need for queuing and paperwork, the system promises to significantly enhance the commuter experience while reducing administrative burdens, ultimately contributing to the advancement of sustainable and user-centric public transportation systems.

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VIRTUAL BUS PASS MANAGEMENT SYSTEM

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 Abstract

The aim of this project helps to develop an application to find the type of attack that occurred on the system and to detect the intruders by using Intrusion Detection System. Previously various machine learning (ML) techniques are applied on the IDS and tried to improve the results on the detection of intruders and to increase the accuracy of the IDS. This paper has proposed an approach to develop efficient IDS by using the principal component analysis (PCA) and the random forest classification algorithm. Where the PCA will help to organise the dataset by reducing the dimensionality of the dataset and the random forest will help in classification. Results obtained states that the proposed approach works more efficiently in terms of accuracy as compared to other techniques like SVM, Naive Bayes, and Decision Tree. The IDS acts as a network level defence to secure a system. IDS mainly used for security purpose to find the threats or malicious activities and also for identifying the type of attack on the system.

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 Keywords

IDS-intrusion detection system, Dimensionality, Datasets, Intruders, PCA-principal component analysis, Accuracy, RFA-random forest approach, Attack, Detection, classification.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Our project revolves around the development of an advanced flood warning system aimed at significantly enhancing disaster response efforts. At its core, the system harnesses the power of machine learning to predict and mitigate the impact of potential flooding events. A pivotal aspect of our approach is the creation of a user-friendly interface accessible to the general public. Through this interface, individuals can access crucial information regarding the likelihood of flooding in their respective regions. By utilizing historical data on river flow and visualizing rainfall patterns at the sub-division level, users gain valuable insights into the potential risks they face. The methodology employed in our project places a strong emphasis on the utilization of machine learning algorithms. These algorithms analyze vast datasets to forecast future outcomes related to flooding with remarkable accuracy. Additionally, we prioritize the speed and timeliness of our predictive models, ensuring that users receive timely alerts and warnings well in advance of potential flood events. By proactively alerting communities to the possibility of flooding, our system aims to minimize the loss of life and property that often accompanies such disasters. Drawing on lessons learned from past incidents, such as the devastating floods that struck Tamil Nadu , we are committed to leveraging cutting-edge technology to create a safer and more resilient future for at-risk communities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Flood prediction, machine learning , forecasting floods

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Creative Commons Attribution 4.0 and The Open Definition