The car centric development has become the heart of urban planning , yet although a city is being designed for humans, the road network seldom provide individuals without automobiles any decent space. Numerous transportation problems have been caused by rapid urbanisation and rising economic growth. Private car usage and growth have increased at a never-before-seen rate, causing significant traffic congestion, high accident rates, air pollution, and greenhouse gas emissions. Initiatives like MRTS and BRTS in growing cities show that traffic problems in the city aren't always fixable. To combat the rising demand for traffic, the government has started transit-oriented development. A stronger vision of a future car free cities has to be taken into consideration since transit-oriented development alone cannot alleviate the strain on infrastructure. The research focuses on ways to create car-free cities within the context of developing countries and offers a strategic framework using characteristics derived from related topics.

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This project introduces an application using computer vision for Hand gesture recognition. A camera records a live video stream, from which a snapshot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least once. After that a test gesture is given to it and the system tries to recognize it. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures. Research was carried out on a number of algorithms that could best differentiate a hand gesture. It was found that the diagonal sum algorithm gave the highest accuracy rate. In the preprocessing phase, a self-developed algorithm removes the background of each training gesture. After that the image is converted into a binary image and the sums of all diagonal elements of the picture are taken. This sum helps us in differentiating and classifying different hand gestures. Previous systems have used data gloves or markers for input in the system. I have no such constraints for using the system. The user can give hand gestures in view of the camera naturally. A completely robust hand gesture recognition system is still under heavy research and development; the implemented system serves as an extendible foundation for future work.

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The Language Club for Beginners is an initiative aimed at reducing language barriers by providing a supportive and engaging learning environment. The club offers translation services to help individuals overcome language difficulties in their daily lives. Additionally, the club organizes quizzes and ranking systems to encourage and motivate language learners to improve their language skills. The quizzes are designed to be fun and interactive, providing learners with an opportunity to practice their language skills in a low-pressure setting. The ranking system allows learners to track their progress and compete with others in a friendly and supportive environment. The Language Club for Beginners is committed to creating a welcoming space for all language learners, regardless of their proficiency level. By providing practical support and fostering a community of learners, the club hopes to empower individuals to overcome language barriers and connect with others from diverse backgrounds.

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Engineered cementitious composites commonly known as bendable concrete is highly known due to its ultimate strain capacity between 3% to 5% as compared with 0.01% of normal concrete [1]. ECC is similar to High performance fiber reinforced cementitious composites with a difference of nonuse of coarse aggregates. ECC is known for its good compressive, flexural and tensile strength. Low carbon foot print due to usage of fly ash makes it ecofriendly and cost effective. Micro crack formation and self-healing behavior of ECC makes it popular in the construction industry. Due to its ductile behavior, this material has a wide use in the area which is prone to natural disaster. Different types of fibers can be used in ECC like Polypropylene fiber (PP), polyvinyl alcohol (PVA), natural fibers etc. Green light weight ECC can be developed with the use of high volume of industrial waste [2]. This paper deals with modeling the compressive strength of ECC using machine learning and ANN.154 Data sets were collected from different experimental works performed to determine the compressive strength of ECC. Data sets were normalized and artificial neural Network and Machine learning techniques like Random Forest, Linear Regression, LASSO Regression, Elastic Net, Ridge Regression, Support vector R are used to analyze the datasets and validation is done using experimental work. The results show a good correlation with the predicted and experimental compressive strength values.

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This Car Café App project will basically be an easy-to-use web application that will allow customers to easily purchase and order products for delivery. It is basically for providing a platform for registering users, categories, products, managing stocks and orders and an end-to- end system from order-to –delivery-to –payment services. This project presents a theoretical framework for Car Parts Store, it discussed about ordering Car Parts items from our store just like from vendors .After Ordering, the details are processed and a delivery person is assigned for carrying out the delivery available in that region. The Order goes through various stages like “PLACED, PACKING, READY, OUT_FOR_DELIVERY, DELIVERED & CANCELED”. This project discussed the tool and technology used in developing the proposed system (the system has a front end by REACT to display the content structure and a back end of database using MySQL and Spring Boot i.e. J2EE). A number of development methodologies were discussed and why one of the methodologies was chosen for this project. Methods used to gather the requirement specification was also discussed and how the researcher will use this as a guideline in developing the proposed system.

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In this paper, Modal Analysis of FGM (functionally graded material) is carried out using First Order Shear Deformation Theory (FSDT) in ANSYS 18.0. Natural frequency and central deflection is determined using the finite element method (FEM). The mechanical properties are varying with thickness above and below the reference level which is centre of the plate. The simple power law is used to determine material mechanical properties with varying volume fraction of the constituents. Results are obtained for different boundary conditions applied. The results for different thickness ratio, aspect ratio, volume fraction is compared with different boundary conditions.

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Expert system have been applied to solve agriculture problems for some time. The complexities involved in plant disease diagnosis make this problem a prime candidate for the application of expert system. To Build this expert system we are going to use images pre-processing or computer vision technology in the domain of AI along with ML and CNN for accurate disease prediction which can help the plants further growth, we will also use aspect of deep learning and create a web application from where user input can be collected and tested against trained data set the Python Programming language will be use along with tensor flow and other ML tools as required.

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AR technology can also provide personalized learning experiences, allowing students to learn at their own pace and in a way that suits their individual needs. For example, AR can adapt to a student's level of understanding and provide additional information or support when needed. Furthermore, AR can facilitate collaborative learning, allowing students to work together on projects and share their knowledge and ideas in a more immersive and interactive way. This can help to foster a sense of community and engagement in the classroom. As AR technology continues to evolve, we can also expect to see more innovative uses in education, such as virtual field trips, gamification of learning, and training simulations for vocational subjects. Overall, AR technology has the potential to transform the way we approach education and provide students with more engaging, personalized, and interactive learning experiences. As educators continue to explore the possibilities of AR, we can expect to see more exciting and innovative applications of this technology in the classroom.

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To describe a range of conditions that affect the heart. To limit alcohol consumption, keep cholesterol level and triglyceride levels under control. To use proximity to make classifications or predictions about the grouping of an individual data point. To use KNN classification algorithm to predict heart disease. To evaluate the diagnostic accuracy of symptoms and signs in patients presenting to general practice with chest pain.

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Mobile health technologies are becoming increasingly popular in the management of chronic diseases. Chronic diseases are among the leading causes of death worldwide and their management often requires ongoing monitoring and support from healthcare providers. Mobile health technologies have the potential to improve disease management by enabling patients to track their symptoms, receive personalized care, and communicate with healthcare providers remotely. The purpose of this research paper is to explore the efficacy of mobile health technologies in managing chronic diseases. The paper provides an overview of mobile health technologies, their potential benefits and limitations, and their role in chronic disease management. It also examines the challenges and opportunities presented by the use of mobile health technologies in chronic disease management. The paper is organized into six sections. The introduction provides background information on chronic diseases and mobile health technologies, and outlines the thesis statement of the paper. Section II provides a detailed overview of chronic diseases and their management, as well as an introduction to mobile health technologies and their use in disease management. Section III discusses the benefits of mobile health technologies in chronic disease management, including increased patient engagement and empowerment, improved disease monitoring and self-management, and increased efficiency and cost-effectiveness in healthcare delivery.

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Object detection is an essential task in the field of computer vision, enabling machines to recognize and locate objects in images and videos. Real-time object detection is particularly crucial for applications where time is of the essence, such as autonomous driving, surveillance systems, and robotics. The ability to quickly and accurately detect objects can help improve safety, efficiency, and productivity in various domains. However, achieving robust real-time object detection remains a challenging task due to several factors, including occlusion, clutter, and variations in lighting conditions. Over the years, researchers have proposed various approaches to tackle the challenges of real-time object detection. Traditional object detection methods relied on handcrafted features and classifiers, such as the Histogram of Oriented Gradients (HOG) and the Support Vector Machine (SVM). These methods often required extensive feature engineering and lacked the flexibility to handle complex object variations and backgrounds. More recent approaches based on deep learning have shown remarkable success in object detection, leveraging the power of convolutional neural networks (CNNs) to learn discriminative features automatically. One of the most popular deep learning-based object detection frameworks is the Faster R-CNN (Region-based Convolutional Neural Network) proposed by Ren et al. in 2015. The Faster R-CNN architecture comprises two components: a Region Proposal Network (RPN) that generates candidate object proposals and a Region-based CNN (RCNN) that performs object classification and bounding box regression on the proposals. The RPN and RCNN share a common CNN backbone, enabling end-to-end training and efficient feature sharing. Faster R-CNN achieved state-of-the-art performance on several object detection benchmarks, demonstrating its effectiveness in real-world scenarios. Despite its success, Faster R-CNN still faces some limitations, particularly in terms of speed and accuracy. The RPN component requires significant computational resources to generate proposals, and the RCNN component can be slow in processing a large number of proposals. Additionally, Faster R-CNN struggles with small objects and dense clutter, leading to missed detections and false positives. Therefore, researchers have proposed several extensions and variations to Faster R-CNN to improve its performance in challenging scenarios. One such extension is the Single Shot Detector (SSD) proposed by Liu et al. in 2016. SSD is a one-stage object detection method that avoids the separate proposal generation stage of Faster R-CNN, enabling faster and more efficient detection. SSD divides the input image into a grid of default boxes of different aspect ratios and scales, each predicting the presence of a certain object class and its bounding box coordinates. The SSD architecture also includes multiple feature maps with varying resolutions, allowing it to detect objects at different scales and levels of abstraction. SSD achieved comparable accuracy to Faster R-CNN while being much faster and more efficient, making it an attractive option for real-time object detection. Another extension to Faster R-CNN is the You Only Look Once (YOLO) framework proposed by Redmon et al. in 2016. YOLO is also a one-stage object detection method that divides the input image into a grid of cells, each predicting object classes and bounding boxes for objects that lie within it. YOLO uses a single CNN to predict all objects' classes and coordinates simultaneously, making it even faster and more efficient than SSD. However, YOLO suffers from lower accuracy than Faster R-CNN and SSD, particularly in detecting small objects and objects in dense scenes. Recently, researchers have proposed several other real-time object detection methods that aim to balance speed and accuracy, such as the EfficientDet architecture proposed by Tan et al. in 2020. EfficientDet is a family of object detectors that use EfficientNet as a backbone network, which is an efficient CNN architecture that achieves state-of-the-art performance on image classification benchmarks. EfficientDet optimizes the network architecture, training.

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Fitness is a metric that assesses a person's overall health. People are pursuing healthy lives in many ways, such as decent eating, frequent exercise, and adequate sleep, as more people emerge from poverty. With the rise of the Internet of Things and smart phones, fitness is becoming in-creasinglypopularthroughsmartwearable's.Electronicsareincreasinglybeing employed in clothes nowadays, making it smart and fashionable at the same time. Them a in goal of this project is to create as mart wear able fitness monitoring system that will aid athletes and regular individuals who need to keep track of their health when exercising, yoga, meditation, or jogging. This technique will assist them in keeping track of their health and increasing the effectiveness of their everyday workouts. As a result, the idea offers a system that may provide information on our fitness, such as the amount of calories burnt. Health care is a major concern in our people. In the rising technology, the Internet of Things (IoT) technology, captivate everyone's attention towards it for its potential to change the traditional health care system and to resolve the problem caused by the rise aging population and the continuing increase in chronic illness in the health care system. This paper mainly study about the conventional healthcare system which is used in past for providing healthcare services and the convergency of a new technology named IoT in the health care system to update the way of treating patients. This paper outlines how IoT has modified the traditional way of healthcare monitoring and make the services fast and efficient in a smarter way. In the end a research study has done on various IoT based healthcare monitoring system and farther more, a connection is made between these IoT based healthcare systems represent their goodness and weakness. Using ML we analysis health of person.

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The population shift from rural to urban areas is influenced by urbanisation, or "the gradual increase in the proportion of people living in urban areas," which also adds to the complexity of travel patterns. To investigate the functional properties of mixed traffic flows made up of different kinds of vehicles (cars, trucks, and bicycles), which are influenced by a variety of variables like side friction, the presence of non-motorized transportation, the curvature of the roads, variation in volume, density, and velocity, etc. The development of a nation's economy depends on an efficient transportation system. Due to a shortage of available urban road space, more vehicles are now on the road, which causes friction on the sides of the road. The main causes of roadside friction are parking manoeuvres, frequent bus stops, entrances and exits from main roads, etc. The characteristics of how vehicles move through traffic are influenced by these factors. Quantifying the effects of various types of side friction in mixed traffic can be challenging for traffic engineers. Determining the capacity of urban arterial roads is the aim of this study. The study and research are focused on the analysis of the four-lane arterial roads in Indore. To study speed reduction, curved stretch was chosen as a part of straight stretch. Here, it will be noted how the curved and straight portions differ in terms of speeds. The conventional straight-and-curve speed flow relation was employed. The data for four-lane divided roads is collected using a video graphic technique. It will take place from 7 AM to 9 PM. For the straight portion, the capacity was calculated based on the flow rate. The sport speed method is used to record video graphics analysis on a large screen, capture vehicle per hour and speed in kmph, as well as convert time means speed into space means speed to establish speed, flow relationships.

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The management of resource allocation in the cloud is a critical issue that has received significant attention in recent years due to the increasing demand for cloud-based services. The efficient allocation of resources is crucial to meet the requirements of different applications and to optimize the utilization of available resources. This research paper explores the concept of optimal management of resource allocation in the cloud. The paper analyzes different approaches to resource allocation and discusses the advantages and limitations of each approach. The research also examines various factors that affect resource allocation in the cloud, including workload, resource availability, and resource utilization. The paper proposes a novel approach to resource allocation that is based on machine learning algorithms. The approach uses historical data to predict resource utilization and allocate resources accordingly. The research also investigates the impact of different factors on the performance of the proposed approach and compares it with other existing approaches. The findings of this research paper provide insights into the optimal management of resource allocation in the cloud. The proposed approach is shown to be effective in improving resource utilization and meeting the requirements of different applications. The research also highlights the importance of considering different factors that affect resource allocation in the cloud to achieve optimal performance.

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The use of solar PV arrays with boost converter to maximize output power at variable irradiance is an efficient way to generate solar power. Connecting the boost converter's output to a metal hydride battery and circuit breakers for switching based on load requirements can help ensure that the system provides power as needed while also protecting the battery from damage due to overloading. the Smart Solar Portable Power Supply System described has the potential to provide a reliable and sustainable source of power during emergencies and natural disasters. It's exciting to see innovations like this that can help save lives and improve outcomes in critical situations.

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Transmission of sensitive information over the internet can be intercepted or accessed by unauthorized parties. Communication over the internet must be secure to prevent intercepting or accessing sensitive information. Attempts are being made to protect confidential information by providing maximum security over the network. Using numerous prime integers for the RSA cryptosystem, which is difficult to crack, is one of these new techniques. The traditional RSA algorithm uses two prime numbers for generating keys. In this paper, our objective is to enhance the existing algorithm by using three prime numbers to generate the keys and to demonstrate the application of this algorithm created in the C programming language. We will also compare the time consumed to encode and decode the message by the traditional RSA algorithm and the enhanced RSA algorithm to get a clear idea of which algorithm is more efficient and reliable and not easily breakable by intruders.

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In order to extract valuable information from multilingual, unstructured (free form) CV materials, this research suggests two NLP models. The model indicates the pertinent document sections (personal information, education, employment, etc.), as well as the pertinent specific information. (Names, addresses, roles, skill competencies, etc.) at the lowest hierarchy level. Our strategy makes use of the transformer architecture and the BERT language model, which serves as the encoder part's multilingual implementation. The models perform well on common accuracy tests after being trained and evaluated on a sizable, manually annotated CV dataset. By displaying the model attention and its vector representations, it was possible to examine the suggested models' significant end-to-end training and interpretability characteristics.

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Vox is a modern, web-based, decentralized, and secure peer-to-peer based real-time communication application that has been built with Rust for high performance and minimal bandwidth consumption. The application has been built to allow users to communicate in a variety of different ways including rich text chat functionality which can be cached for offline access, WebRTC is a free, open-source packet-based protocol that can be installed on any browser to enable audio and video calls and concurrent screen sharing. WebSocket is a protocol that runs on the web which can be used for full duplex and is more suited for data sending due to its low latency. Vox is a revolutionary mobile messaging app that provides powerful encryption and privacy features. It also offers robust features for content sharing and communication. Vox has end-to-end encrypted messages that are optional to persist on a centralized network which means you can be sure that no one will be able to read your messages, view your photos, or know who you are talking with unless they have access to your device.

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Emotion-based advertising has developed as a potential method for developing advertisements that generate emotional responses from viewers while increasing brand recognition. Convolutional Neural Networks (CNNs) have demonstrated significant promise in recognising and analysing emotional content in advertisements. We provide a review of the literature on the use of CNNs in emotion-based advertising in this study, with an emphasis on facial expression recognition and emotional branding. We examine the success of CNNs in creating emotion-based advertising and promoting brand recognition, as well as their limitations and obstacles. According to the literature, CNNs have the potential to revolutionise emotion-based advertising by providing real-time analysis and customisation. However, further study is required to address the limits of current treatments and to investigate new paths for emotion Deep learning algorithms are used in advertising.

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The versatility of gesture detection systems' applications and their capacity for productive human-computer interaction have made them a hot topic in recent years. The most recent findings in hand gesture recognition algorithms are presented in this paper. The primary issue of hand gesture recognition is introduced along with the difficulty of gesture systems. Review the most current methods for recognizing gestures. Also included are comparisons between the most important gesture recognition steps, a summary of hand gesture techniques, and the findings of database searches. The system's benefits and drawbacks are addressed at the conclusion.

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