Volume 12 | Issue 2 |

 Abstract

The amount of pollution caused by humans on the planet has increased dramatically since the industrial revolution. Many of the pollutants in the environment are visible, such as those in the air, water, and soil. Some people, particularly those who reside in big industrial cities, will be aware of air pollution. Since air quality is becoming one of the main factors affecting human health. Air pollution has become a major concern worldwide due to its detrimental effects on human health and the environment. Accurate prediction of air quality is crucial for implementing effective mitigation strategies and safeguarding public health. This study focuses on employing machine learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, for air quality prediction. The LSTM algorithm, a type of recurrent neural network, is known for its ability to capture temporal dependencies in sequential data. The methodology involves collecting historical air quality data, including pollutant concentrations, meteorological variables, and other relevant factors. These data are preprocessed and used to train the LSTM model, which learns the complex relationships between the input variables and the air quality outcomes. The trained model is then used to make predictions for future air quality conditions. The performance of the LSTM model is evaluated using various evaluation metrics, such as mean absolute error (MAE) and root mean square error (RMSE), to assess its accuracy in predicting air quality.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Long Short-Term Memory (LSTM) algorithm,Air Pollution,Quality Outcomes,Human Health,Environment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the contemporary world, the surge in the number of daily patients is evident, propelled by the swift evolution of lifestyles. The queues at hospitals and local doctor's residences are consequently experiencing a steep incline. For individuals with packed schedules, the significant waiting time to consult with a doctor becomes a considerable inconvenience. Some ailments demand prolonged periods for recovery, and heart disease, a widespread concern globally, claims lives on a daily basis, affecting both the young and the elderly. Addressing the escalating healthcare challenges of today and tomorrow necessitates a shift toward remote data collection by care providers, accurate diagnoses irrespective of distances, leveraging AI for data analysis to enhance both business and health outcomes, and more.In this transformative landscape, chatbots, also known as conversational interfaces, emerge as a novel means for individuals to engage with computer systems. The introduction of chatbots revolutionizes the user experience by allowing them to pose questions in a manner akin to conversing with a human. Notably, chatbots are rapidly gaining traction on computer chat platforms, harnessing artificial intelligence to comprehend human inputs effectively. This technological integration facilitates a more intuitive and user-friendly interaction, marking a pivotal advancement in healthcare and beyond. As the reliance on such innovative solutions grows, the intersection of AI, healthcare, and conversational interfaces holds the promise of reshaping how we approach and experience medical care in our increasingly dynamic world.

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 Keywords

Hospitals,Doctors,Artificial Intelligence,Chatbots..

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The goal of the Improvisational Capability program is to introduce the fundamentals of improvisation mainly with Entrepreneur to promote innovative thinking and teamwork, enhance performance abilities, and boost team effectiveness. As an alternative to conventional coaching methods, the curriculum can be applied in a different field. The first part of each session is a review of the previous module, followed by a discussion that provides a more thorough explanation of how improvisation functions, its benefits, and risks, as well as how we may utilize it most efficiently. We will use the idea of freelancing in IT services in our business. There are many similarities between freelancers and businesses in the IT industry. When it comes to finding and keeping employees, creating a business culture, both groups have experience with managing projects and people similar difficulties. However, there are several important differences in how they approach business planning that might help you decide if your company will benefit more from a freelancer or corporation model. Some people find success working as a freelancer, while others find it difficult. Finding clients, keeping them, and getting the appropriate remuneration are the key obstacles to generating money as a freelancer. Additionally, self-employment calls for ongoing attempts to generate income through the investment in systems and infrastructure for ongoing success. Especially if you operate from home or other remote locations, being a freelancer frequently requires full-time dedication. To succeed as a freelancer in the IT business, you need to be persistent and patient when it comes to finding clients and making payment deadlines. There are numerous web services available if you're seeking for freelance work, and they can all help you quickly get your ideal position. The Naive Bayes machine learning algorithm, which is based on the Bayes theorem, is utilized for various classification functions. Gaussian Naive Bayes is the name given to the Naive Bayes generalization. Although there are numerous functions used to estimate data distribution, the Gaussian or normal distribution is the most straightforward to employ.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Naive Bayes, Self-Employement,IT Business, Improvisational Capability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The realm of license plate detection methods, grounded in OpenCV, stands as a well-explored domain within computer vision, boasting applications in diverse fields such as traffic management, vehicle surveillance, and law enforcement. This project introduces an innovative license plate detection methodology rooted in OpenCV, leveraging a spectrum of computer vision techniques to adeptly extract and recognize characters embedded within license plates. The systematic approach of this proposed system unfolds through multiple phases. Initially, the input image of a vehicle undergoes meticulous preprocessing steps, encompassing grayscale conversion, contrast adjustment, and adaptive thresholding. Subsequently, contours emerge from the thresholded image, and potential license plate characters are sieved based on criteria like size and aspect ratio. Precision in grouping these potential characters is achieved through the implementation of a contour arrangement algorithm, ensuring the accurate formation of a license plate region. Post-extraction of this region, further preprocessing is applied to enhance character visibility. Individual character segmentation within the license plate region is accomplished using contour detection. Finally, the optical character recognition (OCR) prowess of Tesseract is harnessed to recognize the segmented characters and extract alphanumeric information from the license plate. The system's development unveils promising results in license plate recognition, affirming the efficacy of the applied computer vision techniques. Nonetheless, it is imperative to acknowledge that the system's performance is contingent on factors such as input image quality, character segmentation accuracy, and the OCR engine's performance, introducing a dimension of variability that necessitates attention and potential refinement.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Opencv, License Plate Recognition,Segmentation,OCR.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The relevance of security concerns has increased with the ongoing advancement of computer technology and the increasing reliance of humans on network technology. To prevent attacks and security flaws, user authentication is essential. There are several forms of authentication, including facial recognition, voice recognition, SMS one-time passcodes, and fingerprint scanning. One of the key uses for image processing in still photos is face recognition. Making an automated system that can recognize faces as well as a person is a real task. This paper's primary goals are to examine the value of CNN, describe the many datasets used in face recognition systems, and assess the various CNN models. The deep learning CNN may be applied to facial recognition to boost authentication security. Here we are collecting the dataset of different faces. Once after preprocessing it we train the data with the CNN algorithm. After training, we will test the results using the OpenCV and also can upload the image for recognition of faces.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

OpenCV, CNN Algorithm,Facial Recognition,Deep Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The system is designed to work with live video feeds from cameras installed in strategic locations. It employs object detection algorithms to identify and track vehicles in real-time, allowing for accurate traffic analysis. The system incorporates speed violation detection by defining speed limit lines and calculating the speed of vehicles passing through those lines. Violation instances are flagged, and images or videos of the violations are captured for further analysis or evidence purposes. The project also includes a user-friendly interface that provides real-time traffic statistics, including the total number of vehicles, traffic congestion levels, and detected violations. Additionally, the system offers configurable settings for road-specific parameters, such as speed limits and the number of allowed vehicles. The proposed system aims to enhance traffic management and improve road safety by providing timely and accurate information to authorities. It can aid in monitoring traffic patterns, identifying congested areas, and enforcing speed limits. The system has the potential to reduce accidents, enhance traffic flow, and contribute to efficient transportation management.Overall, the project showcases the effective utilization of computer vision and deep learning algorithms to develop a comprehensive traffic monitoring and violation detection system that can significantly impact road safety and traffic management.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Traffic Management,Traffic Flow,Deep Learning Algorithms,Traffic Monitoring,Computer Vision.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

One of the most common problems faced by people suffering from common ailments or may be even major ones is the lack of immediate first aid consultation or a centralized service to a clinical database. Due to this lack of the knowledge of the standard operating procedure in such cases, these ailments might aggravate. This results in either physical or mental tension for the person suffering from such ailments. In some cases, the patient suffers from intense mental stress as they try to figure out the reason for their condition.The proposed system tries to eliminate their need to figure out their disease by giving them access to a centralized clinical repository in a much interactive way, just like in a virtual assistant, hence Virtual Health Assistant(VHA).The user gets asked several questions, each one contextually aware of the previous one. The user selects the ailments or their condition and thus a conclusion is reached.This project aims to develop a web service that can present information regarding the health issues and ailments & their history. At the end a precise prescription is generated. What this project can't ensure is the accuracy of the health condition that the service arrived at, and thus in such cases a physician must be contacted. These features thus eliminate the need to search for symptoms online.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Virtual Health Assistant,Centralized Service,Health Issues,Mental Stress.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The use of deepfake techniques in the area of converting images into live video has attracted a lot of attention recently. The term "deepfake," which combines the terms "deep learning" and "fake," describes the process of creating artificial content that is convincing and realistic, usually with the use of Generative Adversarial Networks (GANs). Based on a single input image, this method enables the synthesis of a video sequence that mimics the appearance of a target individual. This project shows how to make a movie of a person's facial driver using a first-order motion model.The algorithm can predict the movements of the head and face during driving after being trained on a dataset of driving videos and facial photos. The finished video is realistic and suitable for a range of objectives, including developing virtual reality experiences or instructing autonomous vehicle training programs. The imageio and matplotlib libraries are used in the project's Python implementation.The First Order Motion Model (FOMM) library is used to implement the first-order motion model. Using the first-order motion model, new techniques for face tracking and animation can be created. Video games and other applications could benefit from the increased realism provided by this technology.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

First Order Motion Model,Generative Adversarial Networks,Facial Driver,Vehicle Training Program.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

There is a vast increase in broadband access due to which this new generation netizens are spawned. In today's situation consumers mainly use the network as a interactive medium for multimedia entertainment and communication purpose. It includes interactive network applications such as teleconferencing, network gaming and online trading which are gaining popularity. We propose a latency equalization service (LEQ), which equalizes the perceived latency for all clients participating in an interactive network application. LEQ is used in variety of applications like gaming, video streaming and real-time communication systems. To effectively implement the proposed LEQ service, network support is essential. LEQ is a process used in data communication networks to ensure that all devices on the network experiences the same delay when transmitting and receiving the data. The LEQ architecture uses a few routers in the network as hubs to redirect packets of interactive applications along paths with similar end-to-end delay. We first formulate the hub selection problem, prove its NP-hardness, and provide a greedy algorithm to solve it.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Latency Equalization Service (LEQ),NP-Hardness,End-to-End Delay,Greedy Algorithm.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The problem of fraudulent mobile applications has grown significantly in importance as a result of the quick development of mobile technology and the rising popularity of mobile applications. These malicious apps not only endanger user's devices but also steal personal information. To safeguard users from potential harm, it is crucial to track down and identify fraudulent mobile applications. With the help of sentiment analysis and the Naive Bayes classifier, SVM etc.., this project is developed for identifying fraudulent applications based on user reviews. The goal is to create a framework that uses data mining and sentiment analysis to analyse user reviews and find review-based evidence of fraud. This projectseek to evaluate the authenticity and dependability of mobile applications before users download them by utilizing sentiment analysis. The suggested method involves gathering user reviews from the Google Play store and classifying them as positive or negative using sentiment analysis. Based on the opinions expressed in the reviews, the Naive Bayes classifier, SVM etc.., is used to categorize applications as either legitimate or fraudulent. By giving users a tool to make educated decisions about the applications they download, this strategy empowers users. Users will be able to recognize fraudulent applications and steer clear of any risks involved with downloading them by putting this framework into place. While giving users a trustworthy way to distinguish between fraudulent and legitimate applications, the system will help to ensure the security and integrity of the mobile application market.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition