Volume 12 | Issue 5

 Abstract

Visually impaired people are not comfortable reading and writing. Hence, an application is being developed to enable blind individuals to read printed text with a camera by simply tapping on the screen using a speech engine. Additionally, a talking calculator has been designed so that visually impaired people can utilize it via voice commands. Alongside these features, several applications have been incorporated to assist blind individuals in their everyday lives. The application also displays the user's current location and provides weather information for any city or location. With the help of an object detection system, blind individuals can easily identify objects through the camera and listen to their names[1]. Furthermore, they can transfer money using a phone number or account number through a voice-based payment system implemented in the project. The application requires minimal effort from the user to be used effectively during daily activities. With the rapid growth of wireless communications, there is an increasing need for voice recognition techniques. Voice applications based on voice interfaces, recognition, and dialogue management can help users focus on their current tasks without requiring extra effort from their hands or eyes. The application listens to commands and responds with voice prompts.

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 Keywords

OCR recognition, Calculator, location detector, Weather detector, text-to speech, Object detection, android

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 Abstract

This proposal advocates for the integration of Industrial Internet of Things (IoT) technologies in the industry to enhance production efficiency and sustainability. Leveraging smart monitoring through IoT-based equipment, the initiative focuses on optimizing energy usage, detecting early machine failures, and ensuring precise temperature control. Implementation involves energy meters for daily voltage regulation, thermal sensors for cost-effective failure detection, and temperature sensors for climate control. By reducing downtime, operational costs are minimized, environmental impact is lowered, and pharmaceutical production becomes more sustainable and economically viable. This innovative approach aligns with the industry's high demand, promoting enhanced product quality, safety, and overall efficiency. In simpler terms, our strategy utilizes smart monitoring to keep machines running smoothly, save costs, and maintain pharmaceutical production quality and safety

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 Keywords

Predictive Maintenance, Early detection, Reduce Downtime, Energy Optimization

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 Abstract

A novel and highly secure encryption methodology using a combination of AES and visual crypto. With the ever-increasing human dependency on The Internet for performing various activities such as banking, shopping or transferring money, there equally exists a need for safe and secure transactions. This need automatically translates to the requirement of increased network security and better and fast encryption algorithms. This paper addresses the above issue by introducing a novel methodology by utilizing the AES method of encryption and also further enhances the same with the help of visual cryptography. In this method the secret message is divided into two parts after which the message the first half of the message is encrypted using AES and the second share of the message is embedded in the image using LSB.

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ENCRYPTION USING AES AND VISUAL CRYPTOGRAPHY THROUGH LSB

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 Abstract

"Distance-Based Toll Way Automation: Using RFID & ANPR for Contactless & Queue-less Tolls" presents a model aimed at showcasing an innovative approach to modernizing highway toll collection. Through the integration of Radio Frequency Identification (RFID) and Automatic Number Plate Recognition (ANPR) technologies, our model eliminates the need for physical toll gates, offering a contactless and queue-less tolling experience. Dynamic pricing mechanisms are introduced to ensure fairness in toll charges, promoting efficient resource allocation and optimizing revenue generation. By prioritizing data privacy and security, our model provides a user-friendly interface for commuters, enhancing overall satisfaction and promoting trust in the tolling system. While implemented at a demonstration scale, this model serves as a proof of concept for the feasibility and effectiveness of distance-based toll way automation. It represents a significant step towards realizing a more accessible, efficient, and equitable tolling infrastructure for highways, contributing to improved traffic management and urban mobility.

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 Keywords

Toll way automation, RFID, ANPR ,Contactless toll collection, Queue-less tolls, Dynamic pricing, Highway infrastructure, Traffic management, Transportation technology, Tolling efficiency, Road safety, Urban mobility, Toll collection optimization

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 Abstract

Diabetic patients are prone to eye disease called Diabetic Retinopathy that affects blood vessels of the retina of diabetic patients. Diabetic retinopathy stands as a foremost cause of vision impairment globally. The earliest diabetes-related changes in the retina are often imperceptible and have minimum impact in the vision and thus approximately one third of the diabetic patients have DR but show no symptoms, leading to the progression of the disease untreated. The complexity of screening methodologies for diabetic eye diseases and the shortage of adequately trained personnel render the development of effective screening-oriented treatments a financially burdensome endeavor. Our proposed framework demonstrates proficiency in accurately localizing and categorizing disease lesions within retinal images thus facilitating automated detection and recognition of diabetic retinopathy, thus enabling early detection for efficient treatment with low cost and high accuracy

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 Keywords

Diabetic Eye Disease, Diabetic Retinopathy, Deep Learning, Retinal Disease.

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Recently, the problem of signature forgery detection attracted significant attention due to various applications: banking, legal, and security . Existing methods require extensive volumes of data for training, making signature detection less accurate and convenient. This paper designs a novel methodology for signature forgery detection that requires one-shot learning.Furthermore, we introduce a novel similarity metric tailored for signature forgery detection, which captures the subtle differences between genuine and forged signatures. This metric facilitates the identification of forged signatures even in cases where the forgeries closely resemble genuine signatures.By training the siamese network on the genuine signature samples, we produced the synthetic forgery samples using sufficiently powerful data augmentation techniques which can allow the network to learn and easily differentiate between the genuine and the forgery signature samples. Our proposed method outperforms existing approaches and demonstrates a high potential for implementation in practice across various realms where the signature authentication needs for security and authenticity verfication.

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Signature forgery detection, One-shot learning, Siamese neural networks, Data augmentation, Similarity metric

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Utilizing cutting-edge technology solutions, the integration of Artificial Intelligence and Augmented Reality has significantly enhanced the traditional clothing shopping experience. Customers can virtually try on clothing and accessories, all from the comfort of their own homes. With specialized software and 3D modelling, customers can upload their images or avatars and virtually "try on" various outfits in real-time, achieving a higher degree of accuracy in sizing and fit predictions. This cutting-edge concept offers an immersive and highly interactive shopping experience, empowering customers to not only see how different clothing items fit and look on them but also providing accurate size recommendations through AI technology. Additionally, the integration of AR in virtual trial rooms enhances the virtual shopping experience by allowing customers to explore products in a more realistic and engaging way, ultimately enhancing their confidence in making online fashion purchases while reducing the need for physical store visits.(Abstract) Keywords-- Artificial Intelligence, 3D, machine learning, Augmented Reality

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Elevate the Online Shopping Experience using Augmented Reality (AR) and Artificial Intelligence (AI) for Enhanced Apparel Recommendations

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The face is an important part of the human body, it recognizes people in huge gatherings. The recognition of face has gained the attention of many researchers and has subsequently become the standard benchmark in the human recognition space. An attendance system using facial recognition is a type of biometric technology. It identifies and verifies the identity of a person from a digital image. Accurate attendance records are critical to class evaluation. However, manual attendance tracking can lead to errors, missed students, or duplicate records. A class image is taken and the RECOGNIZER python file is run. Attendance is done by cropping the faces in the image and it is comapared with the database faces.

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 Keywords

Python; OpenCV and Google API; Student attendance; Face recognition

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 Abstract

The proliferation of fake currency presents a significant and multifaceted challenge, posing a genuine threat to both the welfare of individuals and the stability of our national economy. While counterfeit detection systems are prevalent in banks and corporate environments, their accessibility to the general public and small enterprises remains limited, leaving them susceptible to counterfeit currency. advanced image processing techniques. This currency verification system has been fully developed using the Python language within the Jupyter Notebook environment.

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Fake currency, counterfeit detection, image processing, feature extraction, Bruteforce matcher

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 Abstract

This abstract presents a cutting-edge autonomous firefighting robot system designed to tackle the escalating challenges posed by fires worldwide. Integrating robotics, artificial intelligence, and advanced firefighting equipment, the system offers a versatile and effective solution for extinguishing fires while prioritizing the safety of both responders and civilians. Equipped with sensors for heat, smoke, and obstacle detection, the robot navigates complex environments with precision, swiftly locating and suppressing fires using high-pressure water cannons or foam dispensers. Powered by sophisticated algorithms for autonomous operation, the robot demonstrates remarkable adaptability and efficiency in dynamic firefighting scenarios. With built-in safety features and validated effectiveness through rigorous simulations and real-world experiments, this system represents a significant leap forward in firefighting technology, promising to enhance response capabilities and minimize risks in the face of escalating fire emergencies.

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Firefighting robot, prototype, sensors, navigation, fire suppression

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This study applies machine learning (ML) techniques to predict Cricket World Cup winners, using historical data, team performances, and player stats. Comprehensive datasets from past tournaments are analyzed with algorithms like Random Forests and Logistic Regression, enhanced through cross-validation. Models are trained on diverse match scenarios and team performance data, aiming to forecast the champion accurately

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Component, formatting, style, styling, insert.

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Enhancing Library Chatbot Using Machine Learning with Read-Aloud Technology project aims to enhance user experiences and Uses streamline Framework as it's Front end and leveraging conversational AI technology. This Chatbot will serve as a virtual assistant, providing users with quick and convenient access to information about library resources, such as books, opening hours, and events. Additionally, it will assist in answering common library-related questions, guiding users through the library's physical layout, and recommending books based on their preferences. The Chatbot will offer 24/7 support. It will incorporate natural language processing capabilities to understand and respond to user queries effectively and has Read-aloud technology.

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Library Chatbot, Read aloud Technology, machine learning

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 Abstract

Establishing data for an Intrusion Detection System (IDS) typically entails configuring the actual working environment to explore potential attacks, a process that can be prohibitively costly. However, such software is crucial for safeguarding computer networks against unauthorized access, including from potential insiders. The task of training an intrusion detector involves developing a predictive model, often a classifier, capable of distinguishing between "bad" connections (intrusions or attacks) and "good" regular connections.To address the expense and complexity associated with real-world testing, this study focuses on predicting whether connections are under attack using the KDDCup99 dataset and various machine learning methods. The objective is to enhance packet connection predictions for better accuracy, particularly in identifying DOS, R2L, U2R, Probe, and overall attacks. This involves evaluating and comparing supervised classification algorithms to identify the most accurate predictive results. Additionally, the study assesses algorithm performance through classification reports, confusion matrices, and data prioritization.

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NETWORK BREACH PREDICTION

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"CaneGuideX" incorporates sophisticated equipment to assist the blind. It enhances safety and liberty for individuals exploring new situations by using smart obstacle detection algorithms to detect and evaluate their surrounds in real-time. It offers clear direction and thorough descriptions of barriers through voice aid, making navigation simple and effective. By providing thoughtful, proactive assistance, this ground-breaking technology transforms the way blind people use canes, allows them to move with assurance and independence. With its ability to extend the gap between those with blind people and the surroundings around them and promote greater diversity and autonomy in daily activities, CaneGuideX is a significant leap in accessibility technology.

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 Keywords

Obstacle Detection, YOLO, Deep Learning, Raspberry pi, Text-to-Speech(tts).

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 Abstract

The rising number of vehicles on roads has led to like increased demand for parking spaces, necessitating more efficient and super responsive parking systems. This abstract proposes a super cool smart car parking system utilizing fog computing technology to address so many latency issues inherent in those conventional systems! By employing a combination of many sensors, cameras, and edge devices, the proposed system gathers and processes parking-related data in really real-time, generating like really significant data volumes that require so efficient management! Fog computing extends cloud services to that network edge, reducing latency and congestion by processing data closer to its source. However, resource management remains a such as challenge in fog computing implementation, requiring effective allocation of computing resources across edge devices to really optimize throughput and reduce latency. This research contributes to the development of intelligent parking systems by proposing a fog computing-based approach that optimizes resource utilization and enables real-time processing for efficient parking management.

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SMART VEHICLE PARKING SYSTEM ON FOG COMPUTING FOR EFFECTIVE RESOURCE MANAGEMENT

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 Abstract

They are used to doing work that humans cannot perform. Hand gestures and voice are two of the most powerful communication techniques. Robotics can be used in many of these scenarios to minimize human error and to make work safer and easier. Defense, industrial robotics, vehicle part assembling industries in the civil side and medical field for surgery are the major fields that prefer hand gesture/voice recognition robots. Robot devices are tougher to control with the help of buttons and switches. It will get difficult and tedious to operate buttons and remote controls.Our project deals with the interface of robots through voice and gesture control. The purpose of this gesture recognition and voice recognition method is to capture human hand gestures, voice and perform applications and move in an individual path that meets the user's demands. This project aims to use these two methods to control a robotic car from a long distance without using any physical contact.

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Voice Controlled Autonomous Vehicle For Physically Challenges Civilians

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The current situation indicates that drunk driving is the primary cause of traffic accidents. Every manual effort aimed at curbing alcohol-related driving is undermined by law enforcement officials limited capabilities. Thus the requirements for an alcohol detection device that is not limited by time or space exists .This project describes the layout and focus of an Arduino UNO and ultrasonic sensor-based engine locking alcohol detector for automobiles. When the amount of alcohol in the alcohol detection sensor rises above a certain threshold, the equipment will continuously measure the alcohol content and cut off the vehicle's engine. The concept offers a practical way to reduce drink driving-related accidents.

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Arduino UNO, MQ3 Sensor, Buzzer, LED, DC Motor , Relay Switch

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 Abstract

SkillSync is the bridge that connects talent to opportunity, offering an open-source platform where skills are showcased, discovered, and perfectly matched with the ideal job roles. This project seeks to revolutionize the way we approach the workforce, providing a plethora of benefits, including enhanced efficiency, reduced costs, and an expansive network of skills that now have the chance to shine

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Component, formatting, style, styling, insert.

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In the face of increasingly complex cyber threats, the necessity for robust Network Intrusion Detection Systems (NIDS) has never been greater. Conventional rule-based systems often struggle to keep pace with evolving attack methodologies, necessitating the integration of machine learning (ML) techniques to bolster detection capabilities. This paper puts forward an innovative NIDS approach that leverages ML algorithms to effectively detect and mitigate network intrusions. Our proposed system utilizes supervised learning algorithms trained on labelled network traffic data to differentiate incoming traffic as normal or malicious. By harnessing extensive labelled data, our system can discern intricate patterns and anomalies indicative of malicious activities, thereby enhancing detection accuracy and reducing false positives. Additionally, the system incorporates detection methods for anomalies in network traffic to uncover previously unseen threats by detecting deviations from established baseline behaviour. Key features of our NIDS include real-time monitoring, scalability to accommodate large network infrastructures, and adaptability to dynamic environments. Through Ongoing adaptation through the incorporation of fresh data and refinement of detection algorithms, our system offers proactive defence against a wide spectrum of cyber threats, including known and zero-day attacks. In our evaluation, we demonstrate the effectiveness of our ML-based NIDS through comprehensive experimentation on diverse datasets, demonstrating its enhanced effectiveness in comparison to traditional rule-based approaches. Our results underscore significant enhancements in both detection rates and false positive mitigation, underscoring the potential of ML in bolstering network security defences against evolving cyber threats.

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NETWORK INTRUSION DETECTION SYSTEM USING ML

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 Abstract

The main cause of this article is to find the great version to predict market charges. while we recollect the many strategies and adjustments to recall, we discover that strategies which includes random forests and support vector machines are ineffective. In this newsletter, we are able to recommend and examine a extra powerful technique to more appropriately are expecting the movement of items. First, we don't forget enterprise rate information from the previous year. The data set is pre-processed and adjusted for accurate evaluation. because of this, our article also specializes in preliminary information of the authentic facts. Secondly, after finishing the initial information, we are able to look at using random forests and assist vector machines on statistics units and the effects they produce. similarly, this study examines using these estimates within the real global and the problems associated with the accuracy of these values. the object additionally introduces gadget mastering fashions to expect the lifespan of competitive products. The successful supplying of merchandise will become a superb fee for companies and provide real answers to the issues faced by means of investors.

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STOCK MARKET PRICE PREDICTION USING DECISION TREE AND MACHINE LEARNING ALGORITHMS

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