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Enhanced Clickbait Detection through Ensemble Machine Learning Techniques

Procedia computer science 2025年 258卷 599-608页

作者： Neetu Sardana Deepika Varshney Seema Luthra Department of Computer Science & Engineering-Data Science ABES Engineering College Ghaziabad India

Nowadays online news websites are one of the quickest ways to get information. However, the credibility of news from these sources is sometimes questioned. One common problem with online news is the prevalence of clickbait. Clickbait uses exaggerated headlines to lure people to click the suspected link, but the content often disappoints the reader and degrades user experience it may also hamper public emotions. The proposed work aims to examine diverse set of models for clickbait detection. The models are formed by integration of Machine learning (ML) and Ensemble learning methods (EL) with Term Frequency and Inverse Document Frequency (TF-IDF) & Embedding technique. Five ML and three EL are analysed &compared. Random Forest along with TF-IDF gave the best results of 85%. The resultant model shows significant improvements with a minimal false-positives.

关键词： Clickbait Detection Machine Learning text classification NLP Ensemble learning Stacking classifir

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Learning to Communicate Among Agents for Large-Scale Dynamic Path Planning With Genetic programming Hyperheuristic

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1269-1283页

作者： Liao, Xiao-Cheng Hu, Xiao-Min Chen, Xiang-Ling Mei, Yi Jia, Ya-Hui Chen, Wei-Neng Victoria University of Wellington Centre for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of South China University of Technology School of Future Technology Guangzhou510006 China Pazhou Lab Guangzhou510005 China South China University of Technology School of Computer Science and Engineering State Key Laboratory of Subtropical Building and Urban Science Guangzhou510006 China

Genetic programming hyperheuristic (GPHH) has recently become a promising methodology for large-scale dynamic path planning (LDPP) since it can produce reusable heuristics rather than disposable solutions. However, in this methodology, the extracted local and decentralized heuristic for agents that lack a global systemic view sometimes may be problematic. Therefore, a new challenge is to strike a balance between conciseness to improve generalization ability and incorporation of more global information to obtain better performance. In this work, we target the LDPP problem and propose a communication learning mechanism (ComLGP) for GPHH to address the above difficulties. In ComLGP, a communication function is introduced to serve as a communication protocol and exist in the form of an extra terminal in GPHH. Compared to the classic terminals which are fixed in genetic programing, this communication function undergoes optimization along with the evolutionary process of GPHH. In this way, the communication function can be learned which enables agents to communicate without a predefined communication protocol. Then, a caching and lazy updating mechanism for ComLGP is presented to accelerate the calculation of communication content. Last, we verified our method on 22 scenarios including two real world road networks. The experimental results demonstrate that the proposed ComLGP can successfully learn to communicate. Although in the absence of any manually designed communication features, ComLGP is capable of achieving performance competitive to the state-of-the-art method that employs a predefined communication protocol and outperforms the remaining compared methods in most scenarios. © 2020 IEEE.

关键词： Genetic programming

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A polyhedral reconstruction of a 3D object from a chain code and a low-density point cloud

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Multimedia Tools and Applications 2025年 1-28页

作者： Tapia-Dueñas, Osvaldo A. López, Hiram H. Sánchez-Cruz, Hermilo Department of Mathematics Computer Science and Data Science John Carroll University University HeightsOH United States Department of Mathematics Virginia Tech BlacksburgVA United States Departamento de Ciencias de la Computación Universidad Autónoma de Aguascalientes Aguascalientes Aguascalientes Mexico

The manipulation of 3D objects is becoming crucial for many applications, such as health, industry, or entertainment, to mention some. However, these 3D objects require substantial energy and different types of resources. With the goal of obtaining a simplified representation of a 3D object that can be easily managed, for example, for transmission, in some recent works, the authors associate low-density point clouds with a 3D object that simplifies the original 3D object. More precisely, given a 3D object in a polyhedral format, some authors associate a chain code and then use grammar-free context to obtain key points that give rise to several point clouds with different densities. In this work, we complete the cycle by developing a polyhedral reconstruction from an associated low-density point cloud and the chain code. The polyhedral reconstruction is crucial for handling 3D objects because it allows us to visualize them after they are efficiently compressed and transmitted. We apply our algorithms to well-known 3D objects in the literature. We use the Hausdorff and Chamfer distances to compare our results with the state-of-the-art proposals. We show how our proposed polyhedral reconstruction based on a helical chain code reconstructs a medical image represented or transmitted by slices into a 3D object in a polyhedral format, helping thus to mitigate and alleviate the management of 3D medical objects. The polyhedron that we propose provides better compression when compared with the original set of slices of a 3D medical object. © The Author(s) 2025.

关键词： Medical imaging

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Harnessing Insights for Optimizing Healthcare: Disease Prediction and Beyond 5th

Harnessing Insights for Optimizing Healthcare: Disease Predi...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Chatterjee, Soham Gupta, Ritwika Das Latifi, Shpetim Behera, Tapan Kumar Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo Technical Architect Independent Researcher BostonMA United States

ISBN: (纸本)9789819795147

This study offers a novel method for developing classification approaches for disease prediction. Exploratory analysis and meticulous data preprocessing were conducted to understand the relationships between symptoms and illnesses. The research involved assessing various machine learning models, including the random forest classifier, through cross-validation techniques, resulting in the identification of a high-performing model with an impressive accuracy rate. In addition, this study incorporates data visualization techniques to gain insights into symptom–disease connections. The study’s focus on data visualization and optimization strategies in health demonstrates the potential to transform healthcare by providing precise diagnoses and predicting diseases, ultimately improving patient outcomes. This research underscores the efficacy of data-driven techniques and their integration into recommendation and disease prediction systems, emphasizing the significance of data visualization and optimization strategies in health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Patient treatment

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Unveiling Patterns, Visualizations, and Trends from Patient Diabetes data 5th

Unveiling Patterns, Visualizations, and Trends from Patient ...

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5th International Symposium on Signal and Image Processing, ISSIP 2024

作者： Gupta, Ritwika Das Chatterjee, Soham Latifi, Shpetim Behera, Tapan Kumar Department of Statistics and Data Science CHRIST Deemed to be University Bangalore India Department of Computer Science AAB College Pristina Kosovo Technical Architect Independent Researcher BostonMA United States

ISBN: (纸本)9789819795147

The important role that exploratory data analysis, or EDA, plays in the context of diabetes prediction is explored in this work. EDA is used as a key component of a multimodal strategy to identify unique characteristics linked to diabetes. EDA offers insights that aid in the creation of prediction models by sifting through the complex patterns present in the medical data. The focus is on using EDA to fully grasp the data landscape while also comprehending the distinct features of diabetes. This investigation is critical to accurately categorizing people into discrete risk groups and emphasizes the use of domain-specific knowledge in enhancing diabetes prediction techniques. The research suggests using specific EDA techniques to gain deep insights and lead proactive responses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomedical engineering

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced data 4th

The Hidden Influence of Latent Feature Magnitude When Learn...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Dablain, Damien A. Chawla, Nitesh V. Department Computer Science and Lucy Family Institute for Data and Society University of Notre Dame Notre DameIN46553 United States

ISBN: (纸本)9789819787012

Machine learning (ML) models have difficulty generalizing when the number of training class instances are numerically imbalanced. The problem of generalization in the face of data imbalance has largely been attributed to the lack of training data for under-represented classes and to feature overlap. The typical remedy is to implement data augmentation for classes with fewer instances or to assign a higher cost to minority class prediction errors or to undersample the prevalent class. However, we show that one of the central causes of impaired generalization when learning with imbalanced data is the inherent manner in which ML models perform inference. These models have difficulty generalizing due to their heavy reliance on the magnitude of encoded signals. During inference, the models predict classes based on a combination of encoded signal magnitudes that linearly sum to the largest scalar. We demonstrate that even with aggressive data augmentation, which generally improves minority class prediction accuracy, parametric ML models still associate a class label with a limited number of feature combinations that sum to a prediction, which can affect generalization. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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computer Vision and Deep Learning-Based Model for Detecting Spoofed Faces in Images 2nd

Computer Vision and Deep Learning-Based Model for Detectin...

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2nd International Conference on data science and Network Engineering, ICDSNE 2024

作者： Salian, Gayathri P Rao, Manasa K Rashmi, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819783359

In the modern era of smart applications, video data is critically important in various contexts. In most of these applications, cameras are frequently incorporated to facilitate authentication. As a result, face recognition is the biometric method most frequently employed to authenticate users in these applications. The vulnerability of face recognition systems to spoofing attacks grows in tandem with their increased usage. As a result, robust countermeasures are required. This paper presents an approach to face anti-spoofing through transfer learning and YOLOv8 optimization. Additionally, a custom dataset was constructed using images obtained from web cameras and an existing dataset to assess the proposed work’s real-time effectiveness. The proposed approach also adds a blurriness threshold during image capture to improve performance. With a mean Average Precision (mAP50) of 0.975, the experimental outcomes highlight the model’s effectiveness in detecting face spoofing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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