Real-time object detection using deep learning has emerged as a burgeoning field of study due to its potential for a wide range of applications, including autonomous driving, robotics, and surveillance systems. The primary goal of this method is to identify interesting objects in real-world situations quickly and accurately. By utilizing convolutional brain organizations (CNNs) for highlight extraction and article identification, advanced learning-based strategies have demonstrated exceptional outcomes in this area. CNNs are trained on large-scale image datasets to learn discriminative features that capture object appearance and context effectively. The features extracted by the CNN are then used to detect objects using a detection algorithm. The Region-based Convolutional Neural Network (R-CNN) framework is one popular approach, which first proposes a set of candidate regions and then applies a CNN to each region to extract features for classification and localization. Faster CNN architectures such as Single Shot Detector (SSD) and You Only Look Once (YOLO), as well as hardware acceleration strategies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs), have been proposed as ways to improve real-time performance. These methods allow for high frame rates and real-time object detection, making them suitable for a wide range of real-world applications. 

https://zenodo.org/record/7992750/files/IJISRT23MAY1379.pdf

Real Time Object Detection Using Deep Learning

Face detection applications using digital photos are critical in the face recognition process. This application is used in biometric recognition systems, search systems, and security systems. Artificial intelligence and machine learning are combined in computer vision. Using computer techniques, it can extract information from images and videos. Many prior studies used various methods and programming languages to create face detection applications. The most crucial aspect of computer vision is object detection. Locating the face is the primary step in computer vision to detect the face part in the input image. For the Java programming language, the Open-Source Computer Vision Library (OpenCV) is a free open-source library for object detection. The Haar cascade classifier is one of the object detection techniques. By counting the number of pictures in a square form on an image, this technique can easily convert an object. The use of face detection in digital photos using the Haar Cascade Classifier and image transformation into grey / grayscale images using the OpenCV library are discussed in this paper. This methodology provides the better investigation accuracy of the outcomes in input photos. 

Detection and Recognition of Face Using Deep Learning

The aim of this research study is to present a new YouTube downloader software tool that leverages the power of both artificial intelligence and the Python programming language. This innovative solution combines cutting-edge AI algorithms with the versatility of Python to offer a highly efficient and automated solution for downloading videos from YouTube. The software utilizes AI algorithms to analyze the YouTube video and determine the best possible download format and resolution. The user-friendly interface, built using Python, allows even novice users to easily search, select, and download videos from YouTube. The tool also supports batch downloading, enabling users to download multiple videos at once. Furthermore, the software employs AI-powered optimization techniques to enhance the speed and quality of the downloaded videos. The results of our experiments show that this software offers a faster and more efficient solution for downloading YouTube videos compared to traditional YouTube downloader tools. In conclusion, the developed YouTube downloader using Python and AI is a valuable tool for individuals and businesses who require a fast and reliable way to download and save YouTube videos for offline viewing. 

Development of an Automated Tool to download Youtube Audio/Video using Artificial Intelligence Techniques

Pill color, pill size and shape are few important characteristics for automatic pill detection. However, the environmental factors may cause an effect such that a change is produced in the above factors. Often medication errors occur that may cause complications to patients and all these are caused due to damage in labels and mismatches in medicine intake, etc. In this report, a trained system is proposed using Keras and Tensor Flow mainly, for easy and quick identification of varieties of pills. The detected pill (object detection) connects to the pill database wherein the pill name is detected. After the process of detection, the pre-trained dataset is used to identify the pill. Moreover, the dataset would also include the use cases and required detailed information of the respective pill. The project involves collecting datasets for automated medicine detecting technology. Effectiveness of the proposed method can be verified in the experimental results. 

Detection and Identification of Pills using Machine Learning Models

Employees that desire to work for Amazon will find Amazon employee access to be quite useful. Any individual on this duty who begins evolved work at a firm should obtain the access necessary to carry out their responsibilities. When a worker requests access, the manager will select that employee's request at random and provide access; this may take longer time. To provide access without human intervention, created a task utilizing the Catboost, Decision Tree, and Random Forest algorithms. This, too, takes time. Due of the performance constraint, then used ensemble algorithms such as random forest and gradient boosting. Furthermore, apply a linear classifier and obtain good results by using one-hot encoding, which converts categorical data into binary values. Finally, combine the top three predictions from Decision tree, Catboosting, and Random Forest.

Amazon Employee Access System using Machine Learning Algorithms

In recent years, individuals have been concerned about the extensive and pervasive use of data mining for fraud detection in credit card models based on data mining. Because our challenge resembles a classification problem, traditional data mining strategies are inapplicable. As a result, metaheuristic methodologies such as genetic algorithms are used to create an alternate strategy. The genetic algorithm is utilized in this examination to offer a charge card misrepresentation discovery framework. Genetic algorithm is a sort of calculation that intends to improve at taking care of issues over the long haul. These are algorithms that evolve over time. When a card is cloned, stolen, or lost and seized, it signifies that fraudster have done something odd with it, and it is generally utilized until the obtainable bound is exhausted. As a result, somewhat than focusing on quantity of properly categorized communications, strategy that reduces overall allowed limit on fraud-prone cards takes precedence. Using a genetic algorithm, it seeks to reduce false alarms. So, fraud detection is the process of identifying false alerts in data and reporting the outcomes over time.

Scam Recognition in Visa/Credit Card Using Genetic Algorithm

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Scam Recognition in Visa/Credit Card Using Genetic Algorithm