1,720,963 research outputs found
A Comprehensive Study on Object Detection Techniques in Unconstrained Environments
Get PDFObject detection is a crucial task in computer vision that aims to identify
and localize objects in images or videos. The recent advancements in deep
learning and Convolutional Neural Networks (CNNs) have significantly improved
the performance of object detection techniques. This paper presents a
comprehensive study of object detection techniques in unconstrained
environments, including various challenges, datasets, and state-of-the-art
approaches. Additionally, we present a comparative analysis of the methods and
highlight their strengths and weaknesses. Finally, we provide some future
research directions to further improve object detection in unconstrained
environments.Comment: 9 pages, 3 Figures, 2 Table
Securing Information on a Web Application System to Facilitate Online Blood Donation Booking
Get PDFBlood donation has saved many lives in the past. According to statistics presented by the American Red Cross, a patient is in need of a blood transfusion every two seconds. There are many benefits that arise from blood donation to both the donor and the blood recipients. With blood donation, cancer patients, people involved in accidents, or those battling diseases that require blood donation have access to enough blood to sustain their survival. There is a need to digitize the blood donation booking to facilitate blood donation across the United States, and ensure patients in need of blood, receive their donation from eligible donors on time. This report demonstrates the security measures implemented to secure patient and blood donor data on a blood donation booking web application
Electroencephalogram classification of brain states using deep learning approach
Get PDFThe oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing techniques, input type, and the depth of learning. This study summarises the current methodologies and performance results in EEG categorization using deep learning. A series of practical recommendations is provided in the hopes of encouraging or directing future research using EEG datasets to use deep learning
Designing autonomous drone for food delivery in Gazebo/Ros based environments
Get PDFThere has been a growing global trend towards convenience, speed, and ease in delivery services, and this has been further accelerated by the COVID pandemic. With the everincreasing demand for easily accessible deliveries and expanded delivery service coverage, it has become critical that innovations in this space be developed to further ensure the industry’s smooth operation. With the emergence of the COVID-19 pandemic, the inadequacies became more apparent, emphasizing the need to revolutionize and accelerate the trend in order to meet the increased demand. Drone delivery systems are of particular interest in this context because they can enable faster and more cost-effective delivery. This paper introduces a navigation system that simplifies the delivery of food parcels with independent drones. The system generates a path between the start and endpoints and controls the drone to follow this path based on its location obtained by planning the route through various sensors like LiDAR. The drone also avoids obstacles that come its way to achieve the intended goal, hence autonomously navigating its path. In the landing phase, marker information (ArUco Tag) uses a camera, and the drone software scale is integrated using an expanded Kalman filter algorithm to improve landing accuracy. The vector-based approach controls the drone to fly the desired path smoothly, minimizing vibrations or strong movements that could damage the transported package
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
PROPOSING A NEW MODEL: 'AGILE X' - AN UPGRADED AGILE METHODOLOGY
Get PDFAgile methodologies have revolutionized the way project management is handled, emphasizing iterative development and cooperation between client and vendor in a fast-paced world of software development. A comprehensive agile framework, which may be adapted to changing requirements, is nonetheless needed in view of the increasing complexity of projects. In order to overcome the limitations of existing Agile models, this Article shall introduce a novel method named 'Agile X.' This research supplies a framework that will improve project management, foster collaboration, and deliver superior project outcomes through the use of strong quantitative analysis and qualitative evidence
Comparison of Data Fluctuations that Lead to Cyber Security Attacks: A Difference between Surface, Deep and Dark Net
No full textThe term "darknet" refers to the address space on the internet that is not being used, and users do not anticipate that this area will interact with their machines. Darknet is a source of cyber intelligence. In order to develop network security, it is necessary to conduct studies of the many dangers that comprise the network. In this research, we offer brand new machine learning classifiers that go by the name stacking ensemble learning. Their purpose is to evaluate and categorize darknet traffic. This novel approach employs predictions created by three different base learning techniques in order to deal with the issues relating to darknet attacks. The software was validated using a dataset that had more than 141,000 records and was derived from the CIC-Darknet 2020 database. The findings of the experiment indicated that the classifiers used in the investigation were able to easily differentiate between benign and malignant traffic. The classifiers have the ability to efficiently recognize known as well as unknown threats with a high degree of precision and accuracy that is greater than 99% in the training and 97% in the testing phases, with increments ranging from 4 to 64% based on the algorithms that are currently in use. As a consequence of this, the suggested system will become more reliable and accurate as more data is collected. Additionally, in comparison to other AI algorithms already available, the suggested system has the lowest standard deviation
Machine learning approach to multicore data structures
Get PDFIn this study, a novel method for constructing self-aware data structures using online machine learning is proposed. This research introduced a novel category of data structures called Smart Data Structures, which continuously and automatically improve themselves to help simplify the complexity of manually modifying data structures for varied systems, applications, and workloads. This study also concluded that online machine learning is useful for autonomous data structure modification. For the online machine learning algorithm, I have proposed a reinforcement machine learning algorithm that benefits from the reward system and optimize the knobs accordingly. Online learning, in my opinion, offers a trustworthy and efficient framework for assessing intricate dynamic tradeoffs. Many of the possible difficulties that programmers may encounter in their daily work may be eliminated by using intelligent multicore data structures
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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