Volume and Issue Articles
Back to ArchivesVolume 11, Issue 1
January-December 2024
AMBULANCE DETECTION SYSTEM USING RF MODULE
Abstract: The Ambulance Detection System with RF Modules is a pioneering technology designed to revolutionize emergency response in urban environments. Its primary goal is to swiftly identify and prioritize ambulances, thereby reducing response times crucial for saving lives. The system operates using RF communication modules, with ambulances transmitting continuous signals that are captured by strategically placed receivers in high-traffic areas. An advanced algorithm analyzes these signals, enabling precise and timely identification of approaching ambulances. Upon detection, the system integrates with traffic infrastructure to adjust signals at intersections, ensuring a clear path for ambulances through congested traffic. What sets this system apart is its real-time communication capabilities, allowing it to adapt to changing road conditions and optimize traffic flow for ambulances. This dynamic responsiveness not only minimizes delays but also increases the likelihood of timely medical intervention, potentially saving lives. Furthermore, by reducing the risk of accidents involving emergency vehicles, the system contributes to overall road safety. In conclusion, the Ambulance Detection System utilizing RF Modules represents a significant advancement in emergency response technology. Its incorporation of cutting-edge RF technology promises to revolutionize how ambulances navigate urban traffic, ultimately improving public safety and saving lives.
Object Detection Using YOLO: Challenges, Architectural and Applications
Abstract: Object identification, an essential aspect of computer vision, has made notable progress due to the emergence of deep learning methods. YOLO (You Only Look Once) is a real-time object identification system recognized for its fast performance and precision. This abstract thoroughly summarizes YOLO, emphasizing its structure, training procedure, and uses. The YOLO architecture utilizes a single convolutional neural network (CNN) to predict bounding boxes and class probabilities at the same time, allowing for quick and accurate object localization. Training YOLO requires improving a predetermined loss function that merges localization and classification mistakes. The model's structure has been revised several times, with each new version fixing flaws and improving efficiency. YOLO has been widely used in several fields, such as driverless cars, surveillance, and healthcare. Its real-time operation makes it ideal for situations that need quick decision-making. Nevertheless, difficulties like managing tiny objects and occlusions remain, leading to continuous research endeavors to enhance YOLO and broaden its functionalities. This article is an introductory guide for scholars and practitioners who want to learn about the concepts and uses of YOLO in object detection tasks.
A DETAILED REVIEW PAPER ON ONLINE VOTING SYSTEMS
Abstract: Online voting systems have emerged as a potential solution to modernize the voting process, making it more accessible, efficient, and secure. This review paper provides a comprehensive analysis of online voting systems, covering their evolution, architecture, advantages, challenges, security concerns, and future prospects. We explore different types of online voting systems, discuss their implementation strategies, and examine case studies from around the world. Furthermore, we assess the impact of online voting on democracy, voter turnout, and election integrity. The paper concludes with recommendations for policymakers and stakeholders to promote the adoption of secure and reliable online voting systems. .
A DETAILED REVIEW PAPER ON TOBACCO SELLING WEBSITES WITH
Abstract: Tobacco selling websites play a significant role in the distribution and accessibility of tobacco products. This review paper provides a comprehensive analysis of tobacco selling websites with a focus on the regulatory framework surrounding their licensing. We explore the evolution of online tobacco sales, the prevalence of such websites, and the challenges they pose in terms of underage access, regulatory compliance, and public health concerns. Additionally, we examine the licensing requirements imposed by various jurisdictions, the enforcement mechanisms in place, and the effectiveness of these measures in regulating online tobacco sales. Through a detailed review, this paper aims to shed light on the complexities of online tobacco selling, the importance of licensing, and the potential strategies for improving regulatory oversight in this domain.
DETECTION OF UNAUTHORIZED OBSTRUCTION OF DRONE USING DEEP LEARNING
Abstract: Drone detection involves identifying the presence of unmanned aerial vehicles (UAVs) or drones in specific airspace. This technology has gained significance due to increased drone usage for various purposes, including civilian and military applications. As drone usage rises, concerns about potential risks like privacy infringement, malicious activities, and collisions with other aircraft are growing. Preventing unauthorized drone activities, such as espionage, smuggling, and terrorism, has become a critical security.Various methods, including radar, acoustic sensors, and video cameras, are employed in drone detection technology. These systems are integrated with software algorithms to accurately detect and track drones in real-time. This paper focuses on real-time drone detection using deep learning methods, specifically the VGG16 algorithm. Our experiments indicate that the VGG16 model offers improved accuracy and maintains a high detection speed.
DRIVER DROWSINESS DETECTION SYSTEM USING ML
Abstract: The abstract for "Driver Drowsiness Detection Using Machine Learning" summarizes a study focused on enhancing road safety through the application of machine learning. The research utilizes advanced algorithms to analyze physiological and behavioral signals, such as facial expressions and eye movements, to detect signs of driver drowsiness. By leveraging machine learning's pattern recognition capabilities, the system proactively alerts drivers or authorities, contributing to the prevention of potential accidents caused by driver fatigue. This innovative approach holds promise for significantly reducing road accidents and fatalities, aligning with the broader goal of creating intelligent systems prioritizing safety on the road
AI-DRIVEN FACIAL RECOGNITION: ENHANCING ATTENDANCE SYSTEMS WITH AIML FOR PRIVACY AND
Abstract: Facial recognition technology has witnessed significant progress in recent years, with a particular emphasis on improving accuracy, privacy, and efficiency. This research paper delves into the development of a facial recognition system that not only excels in recognizing faces in real-life scenarios but also prioritizes privacy concerns by differentiating between real faces and images. The system is designed to operate swiftly in various settings, including live videos and security cameras, catering to both large-scale and resource- constrained environments. By incorporating machine learning techniques, the system continuously evolves to handle new challenges effectively, making it a valuable tool for applications such as attendance tracking in educational institutions
PHISHING CONTROL SIMULATION
Abstract: "PCST" is a phishing framework designed to simulate real-world phishing attacks and assess an organization's susceptibility to such threats. The framework provides security professionals with a platform to create and execute phishing campaigns, thereby identifying vulnerabilities within their systems and educating employees about potential cybersecurity risks. PCST allows users to craft highly realistic phishing emails and web pages, mimicking the tactics used by malicious actors. Through its intuitive interface, users can customize various aspects of their campaigns, including email content, sender information, and landing pages. Additionally, PCST offers detailed analytics and reporting capabilities, enabling users to track campaign engagement and measure the effectiveness of their security awareness training programs. By leveraging PCST, organizations can proactively test their defenses against phishing attacks, identify weak points in their security posture, and implement targeted remediation strategies. Ultimately, PCST serves as a valuable tool in the ongoing effort to enhance cybersecurity resilience and protect sensitive data from unauthorized access and exploitation
RAINFALL PREDICTION SYSTEM USING
Abstract: Rainfall prediction is a crucial aspect of weather forecasting, with significant implications for various sectors. Machine learning techniques have been increasingly employed to enhance the accuracy of rainfall predictions. Studies have focused on identifying atmospheric features influencing rainfall intensity and utilizing various machine learning models such as Logistic Regression, Random Forest, and Multi-Layer Perceptron for rainfall density forecasting. Additionally, research has explored short-term rainfall prediction, extreme rainfall events detection, and the development of quantitative precipitation forecast correction techniques using machine learning. Novel machine learning models like TabNet have been proposed to improve rainfall prediction accuracy by combining satellite observations with machine learning methods. Furthermore, machine learning has been applied to predict rainfall- induced landslides and groundwater levels. Various studies have evaluated different machine learning methods for rainfall prediction, showcasing their effectiveness in enhancing predictive accuracy across different climates. Overall, machine learning has shown promise in improving rainfall prediction models, offering valuable insights for better preparedness and decision-making in response to changing weather patterns.
ADVANCEMENTS IN CREDIT CARD FRAUD DETECTION: A COMPREHENSIVE REVIEW
Abstract: Credit card fraud remains a significant challenge for financial institutions and consumers alike. As fraudulent activities evolve, there is a growing need for advanced fraud detection techniques to safeguard transactions and mitigate financial losses. This paper provides a comprehensive review of recent advancements in credit card fraud detection methodologies, highlighting machine learning algorithms, data pre-processing techniques, and emerging technologies. Additionally, the paper discusses challenges in fraud detection, such as imbalanced datasets and adversarial attacks, and proposes future research directions to address these challenges
HOUSE PRICE PREDICTION: EXPLORING THE IMPACT OF MACROECONOMIC FEATURES BY USING ML
Abstract: Accurate prediction of house prices is crucial in the real estate industry, especially with the dynamic nature of the market and the impact of external factors like the COVID-19 pandemic. This research paper aims to explore the effectiveness of machine learning algorithms in predicting house prices, considering both house- specific features and macroeconomic variables to enhance predictive accuracy. The study utilizes datasets containing house-specific features and macroeconomic variables like interest rates, GDP, house price index, CPI, and unemployment rates. Tree-based algorithms such as random forest and XGBoost are employed to develop predictive models. The performance of the models is evaluated using metrics like mean absolute error and mean absolute percentage error to assess predictive accuracy. The results of this research are expected to provide insights into the effectiveness of machine learning algorithms in predicting house prices, highlighting the importance of incorporating macroeconomic variables for enhanced predictive accuracy. By comparing the performance of different algorithms and evaluating the impact of macroeconomic features, this study seeks to contribute to the advancement of house price prediction models, which are crucial for various stakeholders in the real estate market.
EDUXCELL EMPOWERING ONLINE LEARNING THROUGH INTERACTIVE COURSE
Abstract: This paper delves into India’s booming Ed-Tech sector, considering the perspective of creating an online education application to provide better remote education. We’ll explore what’s trending, what’s driving growth, and what challenges need addressing. From online platforms to mobile apps and virtual classrooms, we’ll analyze how these technologies impact teaching, learning outcomes, and student engagement. Factors like smartphone use, improved internet access, government support, and changing attitudes towards online learning are driving sector growth. However, challenges like the digital divide, teacher training gaps, data privacy concerns, and regulatory needs must be navigated carefully. By understanding these dynamics, we aim to provide insights that can guide the development of successful online education platforms and applications in India, ensuring better access to quality education remotely.
INTELLIGENT TRAFFIC CONTROL SYSTEM FOR AMBULANCEPRIORITY USING SOUND FREQUENCY
Abstract: Proposed method presents an innovative solution that combines artificial intelligence and sensors to address the negative effect of urban traffic congestion on emergency vehicle response times, particularly for ambulances. The objective is to create an advanced traffic control system capable of quickly identifying and giving priority to ambulances in heavily congested urban areas.The main novelty of this system is based on advanced sound frequency recognition technology, which has been carefully designed to identify and understand the specific acoustic patterns produced by ambulance sirens, even in the presence of other background noises. By utilizing complex signal processing techniques and machine learning models, the system can properly detect and separate ambulance signals in real-time situations, guaranteeing quick and efficient responsiveness. Once a signal is successfully detected, the system activates an intelligent traffic control mechanism that can dynamically manipulate traffic lights. This adaptive modulation facilitates the movement of the approaching ambulance by reducing traffic congestion and optimizing traffic flow in its proximity. Moreover, the technology effortlessly integrates with the current traffic infrastructure, effectively coordinating a safe and efficient path for the emergency vehicle amidst the congested traffic. Emphasizing the movement of ambulances in busy urban areas has great potential to significantly reduce response times, which could lead to life- saving results during urgent crises. The effective deployment of this intelligent traffic management system not only solves the pressing requirement for swift emergency vehicle passage but also establishes the foundation for future progress in traffic management and emergency response systems, promoting safer and more efficient urban environments.
A REVIEW PAPER ON CHATBOTS AND THEIR IMPLEMENTATION
Abstract: Chatbots are extensively used in many applications, especially in systems that provide intelligent consumer support. Several systems use Chatbots to streamline assistance by reading customer inquiries and delivering prompt and precise responses. This article outlines the creation of a prototype chatbot intended to aid college students in certain courses like AI ML, data science within the educational field.The main focus has been on creating a specific architecture and model to manage communication and efficiently provide precise replies to the learner. A system has been developed to recognize questions and provide answers to students by using natural language processing methods and domain ontologies. Following the execution of the proposed model, an experimental campaign was carried out to showcase its enforceability and efficiency.
ADVANCEMENTS AND CHALLENGES IN MODERN DATA PIPELINE MANAGEMENT
Abstract: Data analytics pipelines are essential frameworks that consist of interrelated processes, beginning with data creation and ending with receipt and analysis. Pipelines enhance workflows by automating manual operations, maintaining smooth data flow, and enabling selection, extraction, transformation, validation, and visualisation activities. They demonstrate high efficiency by reducing errors and removing bottlenecks, enabling the simultaneous processing of several data streams. Data analytics pipelines are versatile, capable of handling batch and real-time processing and supporting various data inputs and destinations, such as visualisation and machine learning tools. Vigilant monitoring is required for the iterative process to ensure performance, validation, and fault identification, primarily due to obstacles such as data corruption and delay. As data sources increase and needs get more complicated, building, monitoring, and maintaining these pipelines becomes labour-intensive, frequently requiring human supervision. Data analytics pipelines are crucial for exploiting data in organisations, facilitating informed decision-making, and preserving a competitive advantage in today’s data-driven world. The proposed work addresses this challenge by developing a comprehensive end-to-end data pipeline. This unified solution integrates data processing, storage, and analysis to enable organisations to derive actionable insights from diverse data sources with exceptional efficiency and agility.
FLIGHT TICKET PRICE PREDICTION
Abstract: This research paper presents a study on predicting flight ticket prices using machine learning techniques. The aim of this study is to develop a model that can accurately forecast the prices of airline tickets, which is crucial for both consumers and airlines. The study utilizes a dataset of historical flight prices along with relevant features such as departure date, airline, and route information. Various machine learning algorithms, including regression and time series forecasting models, are employed to train and evaluate the predictive models. The performance of the models is assessed using metrics such as root mean square error (RMSE) and mean absolute error (MAE). The results indicate that the developed models can effectively predict flight ticket prices with reasonable accuracy, providing valuable insights for travellers and airline companies.
EMPOWERING SMALL RESTAURANTS WITH A REAL-TIME ONLINE ORDERING
Abstract: The online food ordering system discussed in this paper is designed to meet a specific market need by providing small restaurants with an affordable solution to offer their customers online ordering without the high cost and time investment typically associated with custom software development. This highly customizable system allows restaurant staff to easily manage the site content, particularly the menu, through an intuitive graphical interface. The website, which is the only part seen by customers, is dynamically designed to reflect the current state of the system, so any changes made are visible in real-time. Registered users visiting the website can easily navigate the menu, add food items to their order, and choose delivery options with just a few clicks, greatly streamlining the ordering process. Inside the restaurant, orders are promptly retrieved and displayed in an easily readable format for efficient processing. This paper aims to provide detailed descriptions of the system's design and implementation, including a comprehensive overview of available features and plans for future development. Additionally, it includes user manuals and troubleshooting tips for all three components of the system, providing readers with a clear understanding of the expected use cases.
Traffic Sign Detection Using YOLOv8 Algorithm
Abstract: . YOLO is a smart technology used for quickly spotting objects in images or videos. It's popular because it's fast and accurate. Over the years, it's been used to detect all sorts of things like traffic signs, people, cars and many other real world objects.Our goal is to study how YOLO works specifically for spotting Traffic Signs. We want to look at five main things: what it's used for, the data it uses, how well it performs, what kind of computers it needs, and the problems it faces.We have opted for the advanced YOLO algorithm i.e YOLOv8 in our project. Compared to other object detection algorithms like Faster R-CNN, SSD, YOLOv8 demonstrates superior accuracy and efficiency in detecting objects.The YOLOv8 model is trained on the dataset obtained from the Roboflow website, specifically formatted as YOLOv5 PyTorch, then the trained model detects traffic signs which are classified into 21 different classes.Also the model is also capable of detecting classified traffic signs in real-time by making predictions over the live camera feed.
GRC (ISO27000 FRAMEWORK BASED APPSEC AND NETWORK SECURITY
Abstract: The abstract provides a concise overview of GRC (Governance, Risk, and Compliance) practices within the context of ISO 27000 framework, focusing on application security (Appsec) and network security. It highlights the integration of ISO 27000 standards in ensuring robust security measures, safeguarding against threats, and maintaining compliance within organizational networks.
IOT BASED NOTICE BOARD USING ESP32
Abstract: An extensive investigation, the development and use of an Internet of Things notice board based on ESP32 is given in this research report. The digital notice board's design made use of ESP32. The cornerstone of our effort is IoT. Notifications can be posted on a website, and information can be shown on an electronic notice board. Customers who have registered can receive notifications on their Android phone, and changes to the message can be done remotely using our website. One common tool for displaying critical information is a notice board. Colleges and other organizations frequently put announcements on their bulletin boards. The core idea behind an Internet of Things (IoT) visual display powered by an ESP32 is the main topic of this study. Ensuring that data is updated and output is displaced on every internet-connected device is the proposed goal of this project. In our daily activities, notice boards are quite important. In a paperless age, a digital notice board can greatly simplify information distribution by replacing a traditional, analog notice board. The major goal is to develop a workable solution that allows for the gathering of data in real-time, remote-control functionality, secure environmental parameters, and a wireless notice board that shows the messages supplied by the webserver [1]. The components of the system are a breadboard, power supply unit, ESP32, dot matrix display, and jumper wires. Because of the system's intuitive design, users may operate it and send messages from anywhere at any time.
EXPLORATION OF LOW-POWER TECHNIQUES FOR ENERGY-EFFICIENT VLSI DESIGN IN INTERNET OF THINGS (IOT)
Abstract: The proliferation of Internet of Things (IoT) devices necessitates energy-efficient VLSI designs to extend battery life and ensure sustainable operation. This study delves into various low-power techniques for achieving energy efficiency in VLSI designs tailored for IoT applications. Through extensive simulations and evaluations, employing industry-standard tools and benchmarks, the efficacy of voltage scaling, clock gating, power gating, and architectural optimizations is assessed. Results indicate substantial energy savings, validated through graphs and technical analysis, without compromising system performance, rendering these techniques promising for IoT device implementation. The proliferation of Internet of Things (IoT) devices necessitates energy-efficient VLSI designs to extend battery life and ensure sustainable operation. This study delves into various low-power techniques for achieving energy efficiency in VLSI designs tailored for IoT applications. Through extensive simulations and evaluations, employing industry-standard tools and benchmarks, the efficacy of voltage scaling, clock gating, power gating, and architectural optimizations is assessed. Results indicate substantial energy savings, validated through graphs and technical analysis, without compromising system performance, rendering these techniques promising for IoT device implementation. Additionally, the abstract extends to emphasize the critical role of energy efficiency in IoT deployment, highlighting the impact of low-power VLSI design techniques in addressing environmental concerns and enabling the growth of IoT ecosystems. Furthermore, it underscores the practical significance of the findings, providing insights into the implementation challenges and trade-offs associated with each low-power technique. The abstract concludes by pointing to future research avenues, including the exploration of advanced low-power methodologies and their integration with emerging IoT technologies, to meet evolving energy efficiency demands and foster sustainable IoT innovation.