Rainfall is the main cause of flood disasters, and analyzing its features plays a crucial role in preventing flood disasters. How to extract rainfall process features and conduct rainfall similarity analysis is a chal...
详细信息
Transfer learning is the ability to transfer knowledge from one context to another. This paper investigates, for the first time, the possibility of transfer learning on Monte Carlo Tree Search (MCTS). We use distribut...
详细信息
The Internet of Things (IoT) offers vast potential to enhance the quality of life, but the excessive visual data generated during environmental monitoring presents significant challenges. Existing visual data minimiza...
详细信息
The existing cloud model unable to handle abundant amount of Internet of Things (IoT) services placed by the end users due to its far distant location from end user and centralized nature. The edge and fog computing a...
详细信息
Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide ...
详细信息
Depth perception affects 2D image brightness, color, texture, and motion. In addition to stereoscopic vision, depth range, displaying size, 3D visualization, naturalness, and visual comfort can reconstruct 3D depth in...
详细信息
Your thyroid is a little gland that is situated in your neck. It produces hormones that aid in the regulation of several physiological functions. Thyroid disorders come in a variety of forms, such as goiter (enlarged ...
详细信息
This research-to-practice full paper discusses experiences and lessons learned from our EPICS@BUTLER (engineering Projects in Community Service at Butler) program. The program, housed within the department of computer...
详细信息
ISBN:
(纸本)9798350336429
This research-to-practice full paper discusses experiences and lessons learned from our EPICS@BUTLER (engineering Projects in Community Service at Butler) program. The program, housed within the department of computerscience and softwareengineering, is part of a college of Liberal Arts and sciences and serves a diverse population of students with multi-disciplinary backgrounds. The EPICS curriculum is driven by a team-based service-learning pedagogical model. EPICS teams learn how to work together effectively while addressing the immediate IT needs of our non-profit partner clients, navigating their budgetary restrictions, and coping with any lack of existing IT infrastructure. During the 2020/2021 academic year, we launched an empirical study to review and assess EPICS@BUTLER. The study's main goal was to learn from the past 20 years of running the EPICS program by soliciting input from all parties involved. We aimed to improve and expand our service-learning model within an LAS context. More specifically, this study included surveying alumni and current undergraduate students in order to understand the successes and areas of potential improvement within our program. In addition, we conducted one-on-one interviews with our community non-profit partners as well as volunteer team mentors to assess the program's effectiveness and community impact. Based on the empirical data we gathered and analyzed, we discuss how the existing curriculum is effective at providing fulfilling experiences which help our alumni secure jobs after graduation. In addition, we found that the practice of allowing supervised teams to navigate their own EPICS projects helps them improve their professional maturity and interpersonal skills. In summary, this paper discusses an empirical study and aims to leverage the results gathered from our surveys and interviews in order to present a plan for continuous improvement and modernization of our on-going EPICS program. In closing, our paper descri
Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data ar...
详细信息
Due to the exponential increase in data volume, the widespread use of intelligent information systems has created significant obstacles and issues. High dimensionality and the existence of noisy and extraneous data are a few of the difficulties. These difficulties incur high computing costs and have a considerable effect on the accuracy and efficiency of machine learning (ML) methods. A key idea used to increase classification accuracy and lower computational costs is feature selection (FS). Finding the ideal collection of features that can accurately determine class labels by removing unnecessary data is the fundamental goal of FS. However, finding an effective FS strategy is a difficult task that has given rise to a number of algorithms built using biological systems based soft computing approaches. In order to solve the difficulties faced during the FS process;this work provides a novel hybrid optimization approach that combines statistical and soft-computing intelligence. On the first dataset of diabetes disease, the suggested approach was initially tested. The approach was later tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after yielding encouraging results on diabetes dataset. While finding the solution, typically, data cleaning happens at the pre-processing stage. Later on, in a series of trials, different FS methods were used separately and in hybridized fashion, such as fine-tuned statistical methods like lasso (L1 regularization) and chi-square, as well as binary Harmony search algorithm (HSA) which is based on soft computing algorithmic approach. The most efficient strategy was chosen based on the performance metric data. These FS methods pick informative features, which are then used as input for a variety of traditional ML classifiers. The chosen technique is shown along with the determined influential features and associated metric values. The success of the classifiers is then evaluated using performance metrics like accuracy, preci
This article proposes an open-space emergency guiding (OSEG) framework that explores deep learning techniques to predict individual densities for evacuation based on Internet of Things localization. The OSEG framework...
详细信息
暂无评论