Sarcasm is employed widely on various social media platforms. Due to the potential for sarcasm to alter the intended meaning of a statement, the opinion analysis technique is susceptible to inaccuracies. Detecting sarcasm is one of the most challenging problems in analyzing sentiment and mining opinions in social media. Therefore, identifying sarcasm is crucial when making informed public opinion decisions. Preliminary research indicates that sarcastic statements alone have a substantial negative impact on the accuracy of automatic sentiment analysis. Several distinct natural language processing strategies have been previously suggested. However, each technique has limits in terms of textual context and proximity, and the accuracy of classifiers is affected by noise in the dataset. This research introduces SarcAE, a unique method for combining feature-level embedding fusion using an autoencoder and fuzzy logic-based reasoning to classify sarcasm. The evaluation experiments used two benchmark datasets: the News Headlines dataset and Ironic Tweet dataset, subjected to several preprocessing techniques. Extensive experiments conducted using the proposed SarcAE approach demonstrate that the proposed method outperforms other fusion models with an accuracy of 98.53% on the News Headlines dataset and 89.83% on the Ironic Tweet dataset, respectively, surpassing baseline methods by up to 3.7%. These results indicate the effectiveness of SarcAE in capturing contextual and semantic cues needed for sarcasm detection.

Abstract Icons are the first visual element users encounter when searching for applications in online store. Icons with eye-catching features can make an app stand out in user searches, playing a crucial role in attracting user attention and influencing selection. This increases the likelihood of downloads, which can expand the user base, improve revenue, and enhance engagement, contributing to the application’s overall success. However, the majority of research focused on evaluating appeal of apps through application icons is empirical in nature and may lack comprehensive data analytical approaches. While empirical research holds its significance, it may still be limited by the size of the dataset analyzed and could also be subjective. This proposed research presents a novel data-analytical methodology to analyze a large dataset of application icons from Google Play to determine their influence on downloads. It clusters the icons using three different techniques: $K$-means clustering with two distinct feature vectors and agglomerative clustering, extracting various visual features from the clusters that are strongly correlated with application installs. Subsequently, validation of results has revealed that factors of varied colors, the dominance of white or black colors, text, and exposure in the icons can be linked to downloads.

Cleaning and inspection of pipelines and gun barrels are crucial for ensuring safety and integrity to extend their lifespan. Existing automatic inspection approaches lack high robustness, as well as portability, and have movement restrictions and complexity. This study presents the design and development of a scalable, comprehensive automated inspection, cleaning, and evaluation mechanism (CAICEM) for large‐sized pipelines and barrels with diameters in the range of 105 mm–210 mm. The proposed system is divided into electrical and mechanical assemblies that are independently designed, tested, fabricated, integrated, and controlled with industrial grid controllers and processors. These actuators are suitably programmed to provide the desired actions through toggle switches on a simple housing subassembly. The stress analysis and material specifications are obtained using ANSYS to ensure robustness and practicability. Later, on‐ground testing and optimization are performed before industrial prototyping. The inspection system of the proposed mechanism includes barrel‐mounted and brush‐mounted cameras with sensors utilized to keep track of the pipeline deposits and monitor user activity. The experimental results demonstrate that the proposed mechanism is cost‐effective and achieves the desired objectives with minimum human efforts in the least possible time for both smooth and rifled large‐diameter pipes and barrels.

The digital era, while offering unparalleled access to information, has also seen the rapid proliferation of fake news, a phenomenon with the potential to distort public perception and influence sociopolitical events. The need to identify and mitigate the spread of such disinformation is crucial for maintaining the integrity of public discourse. This research introduces a multi-view learning framework that achieves high precision by systematically integrating diverse feature perspectives. Using a diverse dataset of news articles, the approach combines several feature extraction methods, including TF-IDF for individual words (unigrams) and word pairs (bigrams), and counts vectorization to represent text in multiple ways. To capture additional linguistic and semantic information, advanced features, such as readability scores, sentiment scores, and topic distributions generated by latent Dirichlet allocation (LDA), are also extracted. The framework implements a multi-view learning strategy, where separate views focus on basic text, linguistic, and semantic features, feeding into a final ensemble model. Models like logistic regression, random forest, and LightGBM are employed to analyze each view, and a stacked ensemble integrates their outputs. Through rigorous tenfold cross-validation, our proposed multi-view ensemble achieves a state-of-the-art accuracy of 0.9994, outperforming strong baselines, including single-view models and a BERT-based classifier. Robustness testing confirms the model maintains high accuracy even under data perturbations, establishing the value of structured feature separation and intelligent ensemble techniques.

With the large use of social media, the dissemination of intentionally altered and falsified information has become easy, thus posing negative effects on society. Detecting fake content is a non-trivial task as fake news has unique characteristics and challenges. Additionally, the wide use of artificial intelligence (AI) for fake content generation makes the detection of fake content further complicated. Fake news presents engineered content, making it difficult for traditional approaches to comprehend. Existing fake news detection approaches face four problems: lack of robustness, adaptability, limited or no use of auxiliary information, and inability to handle diversity. Fake content diversity introduces the models’ complexities and degrades their performance. Similarly, the accuracy of fake news detection approaches remains low for practical systems. This study focuses on detecting fake news by using an AI-based approach to obtain high accuracy and robustness by using the concept of text transformation into images. It transforms the text into a standard image format which enriches the feature space and boosts the performance of machine learning models. Extensive experiments using two different datasets involving binary and multi-class classification reveal that the proposed approach outperforms existing solutions by yielding superior accuracy. The use of AI approaches helps obtain higher accuracy of 99.70% and 92% for fake news detection using ISOT and LIAR datasets, respectively.

The emergence of new network architectures, protocols, and tools has made it easier for cybercriminals to launch attacks using AI-based tools, presenting challenges in network security. To protect such systems, a versatile malicious traffic detection system is required that can identify attacks regardless of the type of traffic coming toward the network. In this paper, a system is proposed that can singly analyze multi-environment traffic (IoT and traditional IP-based) to detect malicious activity. The existing techniques for managing Multi-Environment traffic are inefficient due to the absence of AI utilization. To overcome these issues, the proposed approach generates a novel multienvironment traffic dataset by merging existing network datasets containing both traditional IP-based traffic and IoT network traffic. Synthetic Data Augmentation TEchnique (S-DATE) is also proposed to overcome the problem of imbalanced data distribution in the new multi-environment dataset. The results show that the utilization of S-DATE results in faster machine learning model convergence and an improvement in the detection rate of normal and abnormal traffic. The proposed approach achieves an impressive overall detection rate of 0.991 and is statistically significant compared to other state-of-the-art approaches.

ChatGPT OpenAI, a large-language chatbot model, has gained a lot of attention due to its popularity and impressive performance in many natural language processing tasks. ChatGPT produces superior answers to a wide range of real-world human questions and generates human-like text. The new OpenAI ChatGPT technology may have some strengths and weaknesses at this early stage. Users have reported early opinions about the ChatGPT features, and their feedback is essential to recognize and fix its shortcomings and issues. This study uses the ChatGPT tweets Arabic dataset to automatically find user opinions and sentiments about ChatGPT technology. The dataset is preprocessed and labeled using the TextBlob Arabic Python library into positive, negative, and neutral tweets. Despite extensive works for the English language, languages like Arabic are less studied regarding tweet analysis. Existing literature about Arabic tweet sentiment analysis has mainly focused on machine learning and deep learning models. We collected a total of 27,780 unstructured tweets from Twitter using the Tweepy SNscrape Python library using various hash-tags such as # Chat-GPT, #OpenAI, #Chatbot, Chat-GPT3, and so on. To enhance the model’s performance and reduce computational complexity, unstructured tweets are converted into structured and normalized forms. Tweets contain missing values, URL and HTML tags, stop words, punctuation, diacritics, elongations, and numeric values that have no impact on the model performance; hence, these increase the computational cost. So, these steps are removed with the help of Python preprocessing libraries to enhance text quality and consistency. This study adopts Transformer-based models such as RoBERTa, XLNet, and DistilBERT that automatically classify the tweets. Additionally, a hybrid transformer-based model is proposed to obtain better results. The proposed hybrid model is developed by combining the hidden outputs of the RoBERTA and BERT models using a concatenation layer, then adding dense layers with “Relu” activation employed as a hidden layer to create non-linearity and a “softmax” activation function for multiclass classification. They differ from existing state-of-the-art models due to the enhanced capabilities of both models in text classification. Hybrid models combine the different models to make accurate predictions and reduce bias and enhanced the overall results, while state-of-the-art models are incapable of making accurate predictions. Experiments show that the proposed hybrid model achieves 96.02% accuracy, 100% precision on negative tweets, and 99% recall for neutral tweets. The performance of the proposed model is far better than existing state-of-the-art models.

Impressive technological advancements over the past decades commenced significant advantages in the maritime industry sector and elevated commercial, operational, and financial benefits. However, technological development introduces several novel risks that pose serious and potential threats to the maritime industry and considerably impact the maritime industry. Keeping in view the importance of maritime cyber security, this study presents the cyber security threats to understand their impact and loss scale. It serves as a guideline for the stakeholders to implement effective preventive and corrective strategies. Cyber security risks are discussed concerning maritime security, confidentiality, integrity, and availability, and their impact is analyzed. The proneness of the digital transformation is analyzed regarding the use of internet of things (IoT) devices, modern security frameworks for ships, and sensors and devices used in modern ships. In addition, risk assessment methods are discussed to determine the potential threat and severity along with the cyber risk mitigation schemes and frameworks. Possible recommendations and countermeasures are elaborated to alleviate the impact of cyber security breaches. Finally, recommendations about the future prospects to safeguard the maritime industry from cyber-attacks are discussed, and the necessity of efficient security policies is highlighted.

Abstract With the multitude of companies that flourish today, job seekers want to join companies with highly satisfied employees. So, job satisfaction prediction is an important task that helps companies in sustaining or redesigning employee policies. Such predictions not only help in reducing employee attrition but also affect the goodwill and reputation of a company. The higher satisfaction level of current employees attracts potential new employees and confirms the positive policies of a company toward its employees. Job satisfaction prediction can be performed using employee reviews either manually or via automated machine learning algorithms. This study first evaluates four widely used machine learning algorithms, that is, random forest, logistic regression, support vector classifier, and gradient boosting, and then proposes a deep learning model to predict employee job satisfaction level. Experiments are carried out on a dataset that contains text reviews from the employees of Google, Facebook, Amazon, Microsoft, and Apple. Three feature extraction methods are analyzed as well including term frequency‐inverse document frequency (TF‐IDF), bag‐of‐words (BOW), and global vector for word representation (GloVe). Performance is evaluated using accuracy, precision, recall, F1 score, as well as, macro average precision, and weighted average. The performance of the proposed model is compared with state‐of‐the‐art deep learning models. Results demonstrate that the proposed model performs better than both the machine learning and state‐of‐the‐art approaches.

Abstract Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.

Social Network Analysis (SNA) has emerged as a new paradigm to effectively represent complex patterns of relationships between all categories of social groups. It helps to find the structure of ties and its impact on individuals, groups or even incidents. This article is a similar attempt to explore the vehicles and vehicle-related violations leading to accidents through the use of SNA. SNA is performed on accident data of New York for 2016. SNA measures including degree centrality, betweenness centrality and eigenvector centrality are used to probe into the impact of ties between actors of accidents. The empirical analysis shows that 'Passenger vehicle' with degree centrality of 7513 has the highest degree of accidents, explaining 41.12% of total accidents. In addition, it is involved in 57.42% of accidents when accidents occur between same types of vehicles. 'Sport utility/station wagon' and 'taxi' rank second and third in this category with degree values of 4657 and 1454 respectively. It is also found that 'driver inattention' holds the pivotal place when violations leading to accidents are concerned. It accounts for 19.09% accidents in general and 44.58% when accidents, where both parties commit the same violation, are considered. 'Failure to yield right-of-way' and 'following too closely' are ranked second and third. Research also finds that Manhattan area of New York is marked by elevated number of accidents.