Abstract Identification and classification of extremist-related tweets is a hot issue. Extremist gangs have been involved in using social media sites like Facebook Twitter for propagating their ideology recruitment individuals. This work aims at proposing terrorism-related content analysis framework with the focus on classifying into extremist non-extremist classes. Based user-generated posts Twitter, we develop tweet system deep learning-based sentiment techniques to classify as or...

Citrus fruit diseases are the major cause of extreme citrus yield declines. As a result, designing an automated detection system for plant is important. Deep learning methods have recently obtained promising results in number artificial intelligence issues, leading us to apply them challenge recognizing and leaf diseases. In this paper, integrated approach used suggest convolutional neural networks (CNNs) model. The proposed CNN model intended differentiate healthy fruits leaves from...

With the rapid increase in social networks and blogs, media services are increasingly being used by online communities to share their views experiences about a particular product, policy event. Due economic importance of these reviews, there is growing trend writing user reviews promote product. Nowadays, users prefer blogs review sites purchase products. Therefore, considered as an important source information Sentiment Analysis (SA) applications for decision making. In this work, we...

DDoS (Distributed Denial of Service) attacks have now become a serious risk to the integrity and confidentiality computer networks systems, which are essential assets in today’s world. Detecting is difficult task that must be accomplished before any mitigation strategies can used. The identification has already been successfully implemented using machine learning/deep learning (ML/DL). However, due an inherent limitation ML/DL frameworks—so-called optimal feature selection—complete...

Summary In the competing era of online industries, understanding customer feedback and satisfaction is one important concern for any business organization. The well‐known social media platforms like Twitter are a place where customers share their feedbacks. Analyzing beneficial, as it provides an advantage way unveiling interests. proposed system, namely Senti‐eSystem , aims at development sentiment‐based eSystem using hybridized Fuzzy Deep Neural Network Measuring Customer Satisfaction to...

Customer churn, a phenomenon that causes large financial losses when customers leave business, makes it difficult for modern organizations to retain customers. When dissatisfied find their present company's services inadequate, they frequently migrate another service provider. Machine learning and deep (ML/DL) approaches have already been used successfully identify customer churn. In some circumstances, however, ML/DL-based algorithms lacks in delivering promising results detecting client...

Modern-day digitalization has a profound impact on business and society, revolutionizing logistics. Supply chain improves transparency, speed, cost-effectiveness, increasing tech adoption—transportation benefits from IoT-driven shipment tracking web data storage. However, cyber threats target IoT by exploiting vulnerabilities. Although ML/DL approaches have showed potential in finding vulnerabilities, the difficulty of selecting appropriate features remains. Existing research produced...

This research presents a novel framework for distinguishing between actual and non-suicidal ideation in social media interactions using an ensemble technique. The prompt identification of sentiments on networking platforms is crucial timely intervention serving as key tactic suicide prevention efforts. However, conventional AI models often mask their decision-making processes primarily designed classification purposes. Our methodology, along with updated method, bridges the gap Explainable...

Abstract Of the many social media sites available, users prefer microblogging services such as Twitter to learn about product services, events, and political trends. is considered an important source of information in sentiment analysis applications. Supervised unsupervised machine learning‐based techniques for data have been investigated last few years, often resulting incorrect classification sentiments. In this paper, we focus on these issues present a unified framework classifying tweets...

The exponential increase in the health-related online reviews has played a pivotal role development of sentiment analysis systems for extracting and analyzing user-generated health about drug or medication. existing general purpose opinion lexicons, such as SentiWordNet limited coverage terms, creating problems health-based applications. In this work, we present hybrid approach to create domain specific lexicon efficient classification scoring users' sentiments. proposed is based on...

The classification of emotional states from poetry or formal text has received less attention by the experts computational intelligence in recent times as compared to informal textual content like SMS, email, chat, and online user reviews. In this study, an state system for is proposed using latest cutting edge technology Artificial Intelligence, called Deep Learning. For purpose, attention-based C-BiLSTM model implemented on corpus. approach classifies into different states, love, joy,...

Recently, Cognitive-based Sentiment Analysis with emphasis on automatic detection of user behaviour, such as personality traits, based online social media text has gained a lot attention. However, most the existing works are conventional techniques, which not sufficient to get promising results. In this research work, we propose hybrid Deep Learning-based model, namely Convolutional Neural Network concatenated Long Short-Term Memory, show effectiveness proposed model for 8 important traits...

As the amount of historical data available in legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this order forecast court case rulings. However, predicting justifying rulings while using judicial facts is no easy task. Currently, previous research on forecasting outcomes small experimental datasets yielded a number unanticipated predictions utilizing machine learning (ML) models conventional methodologies for categorical feature encoding. The...

Deep neural networks have made tremendous strides in the categorization of facial photos last several years. Due to complexity features, enormous size picture/frame, and severe inhomogeneity image data, efficient face classification using deep convolutional remains a challenge. Therefore, as data volumes continue grow, effective mobile context utilizing advanced learning techniques is becoming increasingly important. In recent past, some Learning (DL) approaches for identify images been...

A serious eye condition called cataracts can cause blindness. Early and accurate cataract detection is the most effective method for reducing risk averting The optic nerve head harmed by neurodegenerative known as glaucoma. Machine learning deep systems glaucoma have recently received much attention in research. automatic of these diseases also depends on transfer platforms like VeggNet, ResNet, MobilNet. authors proposed MobileNetV1 MobileNetV2 based an optimized architecture building...

The detection of natural images, such as glaciers and mountains, holds practical applications in transportation automation outdoor activities. Convolutional neural networks (CNNs) have been widely employed for image recognition classification tasks. While previous studies focused on fruits, land sliding, medical there is a need further research the particularly mountains. To address limitations traditional CNNs, vanishing gradients many layers, proposed work introduces novel model called...

The online information explosion has created great challenges and opportunities for both producers consumers. Understanding customer's feelings, perceptions satisfaction is a key performance indicator running successful business. Sentiment analysis the digital recognition of public opinions, emotions attitudes. People express their views about products, events or services using social networking services. These reviewers excessively use Slangs acronyms to views. Therefore, Slang's essential...