120 research outputs found
BaitBuster-Bangla: A Comprehensive Dataset for Clickbait Detection in Bangla with Multi-Feature and Multi-Modal Analysis
This study presents a large multi-modal Bangla YouTube clickbait dataset
consisting of 253,070 data points collected through an automated process using
the YouTube API and Python web automation frameworks. The dataset contains 18
diverse features categorized into metadata, primary content, engagement
statistics, and labels for individual videos from 58 Bangla YouTube channels. A
rigorous preprocessing step has been applied to denoise, deduplicate, and
remove bias from the features, ensuring unbiased and reliable analysis. As the
largest and most robust clickbait corpus in Bangla to date, this dataset
provides significant value for natural language processing and data science
researchers seeking to advance modeling of clickbait phenomena in low-resource
languages. Its multi-modal nature allows for comprehensive analyses of
clickbait across content, user interactions, and linguistic dimensions to
develop more sophisticated detection methods with cross-linguistic
applications
A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges
In recent years, the combination of artificial intelligence (AI) and unmanned
aerial vehicles (UAVs) has brought about advancements in various areas. This
comprehensive analysis explores the changing landscape of AI-powered UAVs and
friendly computing in their applications. It covers emerging trends, futuristic
visions, and the inherent challenges that come with this relationship. The
study examines how AI plays a role in enabling navigation, detecting and
tracking objects, monitoring wildlife, enhancing precision agriculture,
facilitating rescue operations, conducting surveillance activities, and
establishing communication among UAVs using environmentally conscious computing
techniques. By delving into the interaction between AI and UAVs, this analysis
highlights the potential for these technologies to revolutionise industries
such as agriculture, surveillance practices, disaster management strategies,
and more. While envisioning possibilities, it also takes a look at ethical
considerations, safety concerns, regulatory frameworks to be established, and
the responsible deployment of AI-enhanced UAV systems. By consolidating
insights from research endeavours in this field, this review provides an
understanding of the evolving landscape of AI-powered UAVs while setting the
stage for further exploration in this transformative domain
Microstructure and mechanical properties of metal powder treated AISI- 430 FSS welds
Abstract: An innovative yet simple technique for the inoculation of the weld pool of commercial AISI 430 Ferritic Stainless Steel (FSS) with metal powders for grain refinement is discussed. Aluminum or titanium powder in varying amounts introduced into the weld pool via powder preplacement technique was melted under a tungsten inert gas (TIG) torch. This strategy of inoculating the welds offers dual benefits of grain refinement and constriction in the weld geometry. The addition of the metal powders constricts the HAZ by as much as 50% of the conventional weld providing a grain refinement index (GRI) of about 0.8 in titanium powder treated welds. It equally emerged that weld property is not influenced by the grain size alone but equally by the amount of delta ferrite in the microstructure
convoHER2: A Deep Neural Network for Multi-Stage Classification of HER2 Breast Cancer
Generally, human epidermal growth factor 2 (HER2) breast cancer is more
aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is
detected using expensive medical tests are most expensive. Therefore, the aim
of this study was to develop a computational model named convoHER2 for
detecting HER2 breast cancer with image data using convolution neural network
(CNN). Hematoxylin and eosin (H&E) and immunohistochemical (IHC) stained images
has been used as raw data from the Bayesian information criterion (BIC)
benchmark dataset. This dataset consists of 4873 images of H&E and IHC. Among
all images of the dataset, 3896 and 977 images are applied to train and test
the convoHER2 model, respectively. As all the images are in high resolution, we
resize them so that we can feed them in our convoHER2 model. The cancerous
samples images are classified into four classes based on the stage of the
cancer (0+, 1+, 2+, 3+). The convoHER2 model is able to detect HER2 cancer and
its grade with accuracy 85% and 88% using H&E images and IHC images,
respectively. The outcomes of this study determined that the HER2 cancer
detecting rates of the convoHER2 model are much enough to provide better
diagnosis to the patient for recovering their HER2 breast cancer in future
Addressing Uncertainty in Imbalanced Histopathology Image Classification of HER2 Breast Cancer: An interpretable Ensemble Approach with Threshold Filtered Single Instance Evaluation (SIE)
Breast Cancer (BC) is among women's most lethal health concerns. Early
diagnosis can alleviate the mortality rate by helping patients make efficient
treatment decisions. Human Epidermal Growth Factor Receptor (HER2) has become
one the most lethal subtype of BC. According to the College of American
Pathologists/American Society of Clinical Oncology (CAP/ASCO), the severity
level of HER2 expression can be classified between 0 and 3+ range. HER2 can be
detected effectively from immunohistochemical (IHC) and, hematoxylin \& eosin
(HE) images of different classes such as 0, 1+, 2+, and 3+. An ensemble
approach integrated with threshold filtered single instance evaluation (SIE)
technique has been proposed in this study to diagnose BC from the
multi-categorical expression of HER2 subtypes. Initially, DenseNet201 and
Xception have been ensembled into a single classifier as feature extractors
with an effective combination of global average pooling, dropout layer, dense
layer with a swish activation function, and l2 regularizer, batch
normalization, etc. After that, extracted features has been processed through
single instance evaluation (SIE) to determine different confidence levels and
adjust decision boundary among the imbalanced classes. This study has been
conducted on the BC immunohistochemical (BCI) dataset, which is classified by
pathologists into four stages of HER2 BC. This proposed approach known as
DenseNet201-Xception-SIE with a threshold value of 0.7 surpassed all other
existing state-of-art models with an accuracy of 97.12\%, precision of 97.15\%,
and recall of 97.68\% on H\&E data and, accuracy of 97.56\%, precision of
97.57\%, and recall of 98.00\% on IHC data respectively, maintaining momentous
improvement. Finally, Grad-CAM and Guided Grad-CAM have been employed in this
study to interpret, how TL-based model works on the histopathology dataset and
make decisions from the data
Big Data - Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article intends to systematically identify and comparatively analyze
state-of-the-art supply chain (SC) forecasting strategies and technologies. A
novel framework has been proposed incorporating Big Data Analytics in SC
Management (problem identification, data sources, exploratory data analysis,
machine-learning model training, hyperparameter tuning, performance evaluation,
and optimization), forecasting effects on human-workforce, inventory, and
overall SC. Initially, the need to collect data according to SC strategy and
how to collect them has been discussed. The article discusses the need for
different types of forecasting according to the period or SC objective. The SC
KPIs and the error-measurement systems have been recommended to optimize the
top-performing model. The adverse effects of phantom inventory on forecasting
and the dependence of managerial decisions on the SC KPIs for determining model
performance parameters and improving operations management, transparency, and
planning efficiency have been illustrated. The cyclic connection within the
framework introduces preprocessing optimization based on the post-process KPIs,
optimizing the overall control process (inventory management, workforce
determination, cost, production and capacity planning). The contribution of
this research lies in the standard SC process framework proposal, recommended
forecasting data analysis, forecasting effects on SC performance, machine
learning algorithms optimization followed, and in shedding light on future
research
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