Abstract The classification of brain tumors (BT) is significantly essential for the diagnosis Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used accurate detection cancer. However, due to inaccuracy artificial systems, medical professionals not effectively incorporating them into process Brain Cancer. In this research study, we proposed a robust tumor method using Deep Learning (DL) address...

Adversaries and anti-social elements have exploited the rapid proliferation of computing technology online social media in form novel security threats, such as fake profiles, hate speech, bots, rumors. The speech problem on networks (OSNs) is also widespread. existing literature has machine learning approaches for detection OSNs. However, effectiveness contextual information at different orientations understudied. This study presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Online social networks(OSNs) face the challenging problem of hate speech, which should be moderated for growth OSNs. The machine learning approaches dominate existing set speech detection. In this study, we introduce BiCHAT: a novel BiLSTM with deep CNN and Hierarchical ATtention-based model tweet representation toward proposed takes tweets as input passes through BERT layer followed by an attention-aware convolutional layer. encoded further Bidirectional LSTM network. Finally, labels...

Online social networks(OSNs) facilitate their users in real-time communication but also open the door for several challenging problems like hate speech and fake news. This study discusses on OSNs presents an automatic method to identify messages. We introduce attentional multi-channel convolutional-BiLSTM network classification of hateful content. Our model uses existing word representation techniques a environment having filters with different kernel sizes capture semantics relations at...

COVID-19 is a transferable disease that also leading cause of death for large number people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system human being. Therefore, it necessary to diagnosis this at early stage proper treatment, recovery, controlling spread. The automatic significantly detection. To diagnose from chest X-ray images, employing artificial intelligence techniques based methods are more effective could correctly it....

Introduction Heart disease remains a complex and critical health issue, necessitating accurate timely detection methods. Methods In this research, we present an advanced machine learning system designed for efficient precise diagnosis of cardiac disease. Our approach integrates the power Random Forest Ada Boost classifiers, along with incorporating data pre-processing techniques such as standard scaling Recursive Feature Elimination (RFE) feature selection. By leveraging ensemble technique...

Accurate energy demand forecasting is critical for efficient management and planning. Recent advancements in computing power the availability of large datasets have fueled development machine learning models. However, selecting most appropriate features to enhance prediction accuracy robustness remains a key challenge. This study proposes an ensemble approach that integrates genetic algorithm with multiple models optimize feature selection. The identifies optimal subset from dataset includes...

Detecting fake news is increasingly crucial in the digital age as social platforms and online outlets amplify spread of misinformation. This study proposes a novel approach for detection using Bi-LSTM neural network with an attention mechanism. The model's ability to discern long-range relationships sequential patterns text data augmented by mechanism, focusing on key segments within enhance discriminative power. dataset training comprises diverse collection articles, meticulously annotated...

News ticker recognition is a vital area of research due to its applications such as information analysis, opinion mining and language translation for media regulatory authorities. Without automated systems, manual anatomizing difficult. In this paper, we focus on the automatic Arabic Urdu news system. It mainly consists segmentation text generate textual data various online services. Our work investigates character-wise explicit syntactical models with Kufi Nastaleeq fonts. Various network...

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick planning can be facilitated models' ability process rapidly deliver outcomes instantly. Our aims provide non-invasive method eye disease Convolutional Neural Network (CNN). We used dataset Fundus Multi-disease Image Dataset (RFMiD), which...

Automatic movie genre detection is vital for improving content recommendations, user experiences, and organization. Multi-label generation assigns multiple labels to a recognizes movie's diverse themes. Although there are many existing methods generating from movies but do not provide comprehensive analysis visual depiction. This work introduces GenVis, visualization system that provides better understanding of multi-label genres extracted trailers. The initially uses text features classify...

Machine learning techniques' effectiveness and practicality in manufacturing, particularly servicing, have greatly evolved recent years. Big Data has important possibilities & prospects for SMEs. can foster collaboration among SMEs by developing quick fixes problems across all sectors. By availing use of the availability judgement, this may be accomplished. For two major reasons, are carefully chosen inside context: benefit adaptability speedier adapting to shifts toward productivity because...