8 research outputs found
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
Smart buildings are increasingly using Internet of Things (IoT)-based
wireless sensing systems to reduce their energy consumption and environmental
impact. As a result of their compact size and ability to sense, measure, and
compute all electrical properties, Internet of Things devices have become
increasingly important in our society. A major contribution of this study is
the development of a comprehensive IoT-based framework for smart city energy
management, incorporating multiple components of IoT architecture and
framework. An IoT framework for intelligent energy management applications that
employ intelligent analysis is an essential system component that collects and
stores information. Additionally, it serves as a platform for the development
of applications by other companies. Furthermore, we have studied intelligent
energy management solutions based on intelligent mechanisms. The depletion of
energy resources and the increase in energy demand have led to an increase in
energy consumption and building maintenance. The data collected is used to
monitor, control, and enhance the efficiency of the system
Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images
In this study, the main objective is to develop an algorithm capable of
identifying and delineating tumor regions in breast ultrasound (BUS) and
mammographic images. The technique employs two advanced deep learning
architectures, namely U-Net and pretrained SAM, for tumor segmentation. The
U-Net model is specifically designed for medical image segmentation and
leverages its deep convolutional neural network framework to extract meaningful
features from input images. On the other hand, the pretrained SAM architecture
incorporates a mechanism to capture spatial dependencies and generate
segmentation results. Evaluation is conducted on a diverse dataset containing
annotated tumor regions in BUS and mammographic images, covering both benign
and malignant tumors. This dataset enables a comprehensive assessment of the
algorithm's performance across different tumor types. Results demonstrate that
the U-Net model outperforms the pretrained SAM architecture in accurately
identifying and segmenting tumor regions in both BUS and mammographic images.
The U-Net exhibits superior performance in challenging cases involving
irregular shapes, indistinct boundaries, and high tumor heterogeneity. In
contrast, the pretrained SAM architecture exhibits limitations in accurately
identifying tumor areas, particularly for malignant tumors and objects with
weak boundaries or complex shapes. These findings highlight the importance of
selecting appropriate deep learning architectures tailored for medical image
segmentation. The U-Net model showcases its potential as a robust and accurate
tool for tumor detection, while the pretrained SAM architecture suggests the
need for further improvements to enhance segmentation performance
The performance of artificial intelligence language models in board-style dental knowledge assessment: A preliminary study on ChatGPT
BACKGROUND: Although Chat Generative Pre-trained Transformer (ChatGPT) (OpenAI) may be an appealing educational resource for students, the chatbot responses can be subject to misinformation. This study was designed to evaluate the performance of ChatGPT on a board-style multiple-choice dental knowledge assessment to gauge its capacity to output accurate dental content and in turn the risk of misinformation associated with use of the chatbot as an educational resource by dental students.
METHODS: ChatGPT3.5 and ChatGPT4 were asked questions obtained from 3 different sources: INBDE Bootcamp, ITDOnline, and a list of board-style questions provided by the Joint Commission on National Dental Examinations. Image-based questions were excluded, as ChatGPT only takes text-based inputs. The mean performance across 3 trials was reported for each model.
RESULTS: ChatGPT3.5 and ChatGPT4 answered 61.3% and 76.9% of the questions correctly on average, respectively. A 2-tailed t test was used to compare 2 independent sample means, and a 2-tailed χ
CONCLUSION: ChatGPT3.5 did not perform sufficiently well on the board-style knowledge assessment. ChatGPT4, however, displayed a competent ability to output accurate dental content. Future research should evaluate the proficiency of emerging models of ChatGPT in dentistry to assess its evolving role in dental education.
PRACTICAL IMPLICATIONS: Although ChatGPT showed an impressive ability to output accurate dental content, our findings should encourage dental students to incorporate ChatGPT to supplement their existing learning program instead of using it as their primary learning resource
Day of the year-based prediction of horizontal global solar radiation by a neural network auto-regressive model
The availability of accurate solar radiation data is essential for designing as well as simulating the solar energy systems. In this study, by employing the long-term daily measured solar data, a neural network auto-regressive model with exogenous inputs (NN-ARX) is applied to predict daily horizontal global solar radiation using day of the year as the sole input. The prime aim is to provide a convenient and precise way for rapid daily global solar radiation prediction, for the stations and their immediate surroundings with such an observation, without utilizing any meteorological-based inputs. To fulfill this, seven Iranian cities with different geographical locations and solar radiation characteristics are considered as case studies. The performance of NN-ARX is compared against the adaptive neuro-fuzzy inference system (ANFIS). The achieved results prove that day of the year-based prediction of daily global solar radiation by both NN-ARX and ANFIS models would be highly feasible owing to the accurate predictions attained. Nevertheless, the statistical analysis indicates the superiority of NN-ARX over ANFIS. In fact, the NN-ARX model represents high potential to follow the measured data favorably for all cities. For the considered cities, the attained statistical indicators of mean absolute bias error, root mean square error, and coefficient of determination for the NN-ARX models are in the ranges of 0.44–0.61 kWh/m2, 0.50–0.71 kWh/m2, and 0.78–0.91, respectively