In today’s digital ecosystem, there is a dominance of attention driven content platforms that promote sensationalism over informational quality. These platforms use various means to manipulate users. Clickbait is among them. It often uses misleading or exaggerated headlines to lure people to click on a link. This leaves them in discontent as the promises are never met. The aim is to gain user engagement by either routing them to a page with lots of advertisements that, in turn, boost their revenue or simply spreading misinformation. This necessitate the development of automatic clickbait detection models. This article serves as a systematic review of the work done in this domain, focusing on two important areas: existing datasets with their labeling techniques, and the evolution of various clickbait detection models from ML to DL to new pre-trained language models such as BERT and RoBERTa. This paper aims to serve academic researchers and industry professionals seeking an overview of clickbait detection methods with particular emphasis on ground truth datasets generation and their labeling strategies.
The theory of intuitionistic fuzzy sets (IFS) is ideally suited to handle uncertainty and haziness. In this study, an enhanced fuzzy TOPSIS-based method for dealing with multi-attribute group decision making (MAGDM) problems under intuitionistic fuzzy information, where the weights of the decision-makers (DMs) and criteria are completely unknown. Firstly, the Yager operational rules are initiated for intuitionistic fuzzy numbers (IFNs) constructed on Yager T-norm (TN) and T-conorm and various core properties of these operational rules are investigated. Secondly, utilizing these operational various weighted aggregation operators such as, intuitionistic fuzzy Yager weighted averaging (IFYWA) operator, intuitionistic fuzzy Yager weighted ordered weighted averaging (IFYOWA) operator, intuitionistic fuzzy Yager weighted hybrid averaging (IFYWHA) operator, intuitionistic fuzzy Yager weighted geometric (IFYWG) operator, intuitionistic fuzzy Yager ordered weighted geometric (IFOWG) operator, intuitionistic fuzzy Yager hybrid weighted geometric (IFYHWG) operator are initiated. Thirdly, a few characteristics of the intended aggregation operators are investigated. Fourthly, a novel MAGDM model is constructed based on the intended aggregation operators to handle IF information. Finally, a numerical example related to the selection of suitable green suppliers is provided to show the efficacy and practicality of the initiated MAGDM approach, along with the comparison to some existing MAGDM approaches.
This study introduces a hybrid framework for the precise detection and classification of wheat rusts (Leaf Rust, Stem Rust, and Stripe Rust) and Wheat Leaf Septoria, aiming to enhance diagnostic accuracy and support effective disease management. The framework comprises key stages: lesion segmentation, Hybrid Feature Engineering and disease classification. Advanced machine learning and image enhancement methods are combined to effectively segment lesions using a multilevel hybrid segmentation model, followed by feature extraction involving shape, color, texture, and edge properties refined through PCA, entropy and skewness. To address challenges related to limited datasets and model overfitting, robust handcrafted features are utilized instead of deep feature dependency. Finally, a Multi-Class Support Vector Machine (M-SVM) is employed for classification.Evaluated on four wheat diseases-septoria, stem rust, leaf rust, and stripe rust-the framework achieved a segmentation performance index of 98.21% on 40 images per disease and a classification accuracy of 99.1% using 10-fold cross-validation on 1,000 images. With an F1-score of 0.989, AUC of 0.991, and minimal error rates, the system outperforms existing methods, averaging 5.8 seconds per image, demonstrating its robustness and efficiency.
Computation Offloading (CO) transfers computationally intensive tasks from hardware-limited devices (clients) to more powerful servers. This enables clients to overcome performance and power limitations and meet Quality of Service requirements. Research in CO focuses on “Why,” “What,” “If/When,” “Where,” and “How” to offload. This work investigates the “If/When” question from a performance perspective in dynamic environments. To optimize decision making, a Reinforcement Learning (RL) agent was designed, implemented, and trained to make efficient offloading decisions. The practicality of our solution was tested by emulating real hardware in industrial environments using 5G New Radio. Experimental results show that the proposed RL agent outperforms the existing closed-form solution from the literature, improves the total completion time of emulated apps, and reduces client device power consumption.
Modern diagnostic systems increasingly rely on longitudinal and multimodal data, yet they typically assume a stable relation between observations and disease risk. We introduce latent temporal modulation (LTM) as a conceptual framework for a class of problems in which the relation (i.e., the mapping between observations and risk) evolves over time due to temporally structured, non-observable factors. We formalise LTM and show that it is not explicitly represented within standard dynamic or latent-state modelling frameworks. In such settings, diagnostically relevant information is encoded in the evolving relation between observations and disease risk rather than in observed variables alone. This perspective has implications for the design of diagnostic decision systems operating on temporal medical data. In particular, LTM shifts the focus of diagnostic modelling from analysing temporal data alone to analysing how their interpretation evolves over time.
The functional requirements (FRs) classification in software requirements classification (SRC) is a difficult task due to class imbalance, fine-grained subcategories, and semantic complexities. Existing Machine Learning (ML) and Deep Learning (DL) models often rely on handcrafted characteristics or overlook the contextual meaning. This work presents a novel hybrid ensemble framework that refines three pre-trained transformers (BERT, DistilBERT, and RoBERTa) and combines them using two mechanisms: (1) an Attention-Based Fusion Mechanism that dynamically selects the most contextually relevant transformer for each instance, and (2) an Accuracy-Per-Class Weighted Ensemble that assigns weights based on per-class validation accuracy. Tested on multiple datasets, the approach outperformed single-transformer and DL models (CNN, LSTM, BiLSTM, and GRU) by a large margin (p < 0.001), achieving 95% accuracy and 0.94 F1-score. To the best of our knowledge, this is the first study to combine attention fusion and transformer-based ensembles for SRC, establishing a new standard for SRC.
Multi-objective optimization problems (MOPs) pose significant challenges due to the presence of multiple conflicting objectives. This paper introduces MOPOA, a novel Multi-Objective Pufferfish Optimization Algorithm inspired by the defensive behaviors of pufferfish in nature. MOPOA extends the original single-objective POA by incorporating Pareto dominance, an external archive for preserving non-dominated solutions, and a crowding distance mechanism to maintain solution diversity. The algorithm balances exploration and exploitation through biologically inspired phases simulating predator-prey interactions. To evaluate MOPOA’s performance, it was benchmarked against several state-of-the-art algorithms, including NSGA-III, MOPSO, MODA, and MOFDO, on two well-known test suites: the ZDT and CEC-2019 multi-objective functions. Results indicate that MOPOA not only achieves superior convergence to the Pareto front but also maintains high diversity and robustness across diverse optimization scenarios. These findings position MOPOA as a powerful and adaptive tool for solving complex real-world multi-objective problems.
A novel technique is developed for nonlinear optimization problem which is convex, separable and having multiple objective functions. In the development of the model all the objectives and the constraints of the multi objective model are linearly approximated over suitable intervals. The linear approximations are then aggregated to account for the original problem. The developed technique has been utilized for portfolio optimization problem. Firstly, the minimum variance model has been formulated and solved with machine leaning techniques. Secondly, the risk aversion model has been formulated and solved. The results obtained are combined into a multi objective framework of convex separable programming problem. All the three problems have been solved with the help of the XGBoost, neural network, and decision forest regression models. The renowned Python machine libraries of scikit-learn and keras have been utilized. The results identified portfolios that can return more financial benefits to the investors while investing in the capital market. The results of the proposed MOCSP approach are 22.5% improved in case of risk aversion model. Additionally, 17% improvement has been recorded in case of the minimum risk model. The MAE and RMSE for both XGBoost and decision forest regression have a frail value 0.0001. MAE and RMSE for the neural network regression have been recorded 1% and 2%, respectively. Both Accuracy and F1 score for XGBoost are 91%, for neural network regression are 98%, and for decision forest are 92%, respectively.
The TODIM method was the first Multiple Criteria Decision Making (MCDM) approach to incorporate the advances of judgment and decision theory based on the findings of prospect theory. Since its advent, several studies have proposed new methods for the same purpose, such as Behavioral TOPSIS. Additionally, new versions of the original TODIM method have been developed to enhance its adherence to prospect theory. Among these approaches, the ExpTODIM version demonstrated high accuracy in predictions under deductive reasoning scenarios. In this research, an inductive reasoning analysis of the potential of ExpTODIM and Behavioral TOPSIS is conducted. The results are then compared with a native inductive reasoning method, namely multinomial regression analysis. It was found that ExpTODIM demonstrated strong potential for applications under an inductive reasoning perspective, which supports the application of prospect theory in tasks such as autonomous classification, a key area in artificial intelligence studies.
This research investigates the impact of graphical representation on the accuracy of three-dimensional shape classification. 3D models, both scanned and modeled, are used in engineering, computer graphics and scientific data visualization. Various approaches are adopted in these fields to visually represent 3D geometry, utilizing solutions such as OpenGL and Direct3D, each with its distinct goal of achieving either real-time manipulation or photorealism. The purpose of this research was to determine the most effective graphical representation for categorizing mechanical components with a high degree of geometric similarity, such as beams and rods. The study examined various image representations and their combinations, obtained through the adjustment of rendering parameters and image compositing. In an effort to improve classification accuracy, novel techniques for addressing image recognition issues were developed and tested against commonly used image representation methods. This innovative approach proposed in the paper led to a 48% reduction in classification errors.
Early and accurate diagnosis of thyroid disorders is essential due to their prevalence and health impact. To enhance interpretability in clinical settings, we propose a comprehensive workflow for transparent thyroid disease prediction using a multiclass classification problem with five diagnostic categories. A dataset of 9172 samples with 31 features was used to train various machine and deep learning models. A dual-layered framework combining Feature Selection (ETC, MI, RFE) and Explainable AI (SHAP, LIME) improved performance and transparency. Gradient Boosting achieved the highest accuracy (0.97). SHAP explained global feature influence, while LIME clarified individual predictions. Our approach supports interpretable, reliable AI-based diagnostic tools for thyroid disorder classification.
We aim to establish estimation bounds for various divergences, including total variation, Kullback-Leibler (KL) divergence, Hellinger divergence, and Pearson χ2 divergence, within the GAN estimator. We derive an inequality based on empirical and population objective functions of the GAN model, achieving almost surely convergence rates. Subsequently, this inequality was employed to derive estimation bounds for total variation, Kullback-Leibler (KL) divergence, Hellinger divergence, and Pearson χ2 divergence, leading to almost surely convergence rates and differences between the expected outputs of the discriminator on real data and generated data. Our study demonstrates better results compared to some existing ones, which are a specific case of the general objective function.
In the digital world, inequalities in digital participation often arise as a consequence of the uneven distribution of digital resources. Therefore, the objective of this study is to develop a digital divide index (DDI) that facilitates the temporal and spatial examination of disparities in the availability and quality of internet access, internet use and outcomes. Empirical data for 27 European countries were collected from online available datasets for 2014 and 2022. Exploratory factor analysis (EFA) was used to identify factors in DDI, while the weight values were calculated using the Shannon entropy method. The average difference in DDI scores indicates a spread of the digital divide facilitated by Finland, Latvia, Estonia, Germany, Poland and Slovakia. States in Northern and Central Europe like Norway, Austria and the Czech Republic are at the forefront of achieving digital equality. Spatial analysis reveals that Benelux countries along with Scandinavian countries show the highest levels of DDI, while Southern Europe lags behind. Sensitivity testing results show a stable index structure with variations in the importance of factors not significantly affecting the ranking results.
The study aims to develop an effective and efficient deep learning model for detecting skin diseases, as skin diseases rank as the world’s number one health problem. Besides, cancers and dermatological anomalies should be diagnosed at an early stage, so that subsequent treatment can be efficient and complication-free. The existing methods of diagnosis are associated with lower precision and, in most cases, are inefficient, which can be attributed to the lack of effective data augmentation, segmentation techniques, and improved feature extraction. In this paper, a general framework is introduced that uses Generative Adversarial Networks for data augmentation, Mask R-CNN for precise segmentation, and a tailored multilayer Convolutional Neural Network with an attention mechanism incorporated into it to classify 23 skin disease classes using 25,250 images, among them 5,750 generated by GAN, to balance underrepresented classes. The accuracy attained was 97.30%, which was much better than that reported in earlier studies, which ranged from 85 to 92. The metrics, including an accuracy of 95.65%, a recall of 97.09%, and an F1-score of 96.98%, were used to assess the system’s performance in classifying invisible dermatological images. The scalable system provides explanations that support real-time diagnosis, preventing delays and acute health costs. The findings fully fulfil the capabilities of deep learning in dermatology, as the initial diagnosis of the skin disease is accurate, accessible and efficient.
This study presents an Artificial Intelligence-based system designed to predict cyanobacterial harmful algal blooms (CyanoHABs). The system utilizes Long Short-Term Memory (LSTM) networks to predict the timing of bloom occurrences and One-Dimensional Convolutional Neural Networks (1D-CNNs) to estimate cyanobacterial density. Additionally, Generative Adversarial Networks (GANs) are employed for data augmentation to enrich the database. The system’s performance was validated using the Algerian Mexa database, achieving an R-squared (R2) value of 98% and a root mean square error (RMSE) of 9% for cyanobacterial density prediction, and an R-squared value of 88% with a root mean square error of 31% for bloom timing prediction. These results highlight the system’s robust predictive capabilities, enabling proactive monitoring and management of CyanoHABs to mitigate their adverse impacts on health and the environment.
In this manuscript, we extend the Overlapping Box Suppression (OBS) algorithm, a novel approach designed to enhance window-based object detection systems by reducing false-positive detections. While window-based methods are commonly used for small object detection, they often face challenges due to partially visible objects and intersecting detection windows. To address this, the proposed OBS algorithm uses the detection window coordinates to effectively filter out redundant partial detections, improving detection quality. Additionally, we introduce a novel Overlapping Box Merging (OBM) algorithm, which further refines detection results by combining partial detections into a single, more accurate detection. Together, OBS and OBM offer a robust solution for handling overlapping and fragmented detections. We evaluate this combined global filtering block on sequences from the SeaDronesSee dataset, demonstrating superior performance across multiple object detection metrics compared to traditional NMS-based filtering methods.
In this paper, we present a genetic algorithm for a concurrent real-time optimization problem occurring in the embedded system design process. The problem consists of two concurrent phases, each impacting the other in real time. In the first phase, parameters are selected for optimization, and in the second, the parameters are optimized and their choice is validated in real time. During the implementation of the embedded system, unexpected situations can arise, each of which can be solved in many ways; each way, in turn, may require the execution of different unexpected tasks. However, identifying the optimal path to follow is significantly challenging. Furthermore, some of the proposed solutions to the problem may not yield appropriate results. The proposed algorithm generates a certain number of individuals and evolves them using genetic operators, performing the proper optimization and comparing the results.
This manuscript investigates the integration of positional encoding – a technique widely used in computer graphics – into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that machine learning-based techniques, such as lightweight multilayer perceptrons (MLPs) operating in a low-dimensional feature space, offer a faster alternative for collision checking than traditional methods that rely on geometric approaches, such as triangle-to-triangle intersection tests and Bounding Volume Hierarchies (BVH) for mesh-based models.
In this paper, we introduce a novel variant of the Vehicle Routing Problem (VRP), the Rechargeable Rover Routing Problem (RRRP), which addresses the routing of energy-constrained autonomous electric rovers for Martian missions. We formulate a graphbased representation of the problem and propose an initial formulation as a mixed-integer non-linear program (MINLP). To enhance computational efficiency, we demonstrate how the model can be linearized. The resulting mixed integer linear model is evaluated on small-scale test cases, and its computational complexity is analyzed for larger problems with up to 30 Points of Interest (PoIs). Our experiments show that the problem can be solved to optimality for problem sizes anticipated in upcoming Mars expeditions. However, for future missions involving swarms of rovers, the development of more efficient heuristic or approximation algorithms will be necessary.
