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Plausible reasoning and knowledge extraction in Cognitive IoT

Multimedia Tools and applications 2025年第12期84卷 10781-10816页

作者： Jha, Vidyapati Tripathi, Priyanka Department of Computer Applications National Institute of Technology Raipur India

One central area of current Internet of Things (IoT) research is developing methods for objects to sense their environment autonomously and then link them together so that they can exchange their findings quickly. In response to this urgent need, a new paradigm has emerged that is known as the Cognitive Internet of Things (CIoT). It adds cognitive capability in the form of sophisticated intelligence to the existing IoT. With the generation of huge amounts of data by CIoT applications, there is a compelling need to derive valuable insights from data in a computationally efficient way. Therefore, this research proposes the modified plausible reasoning for extracting knowledge from massive heterogeneous datasets. In the first step, the data is passed onto total variance regularizers to regularise the variance. Subsequently, the clusters are created with probabilistic clustering, and the plausibility theory is redefined at the cluster and the cluster-member levels. The experimental assessment of the environmental data spanning 21.25 years and the cross-validation using a variety of measures demonstrate that the proposed method is more effective than other competing approaches. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Bibliometric Analysis of Digital Twin: Scientometrics & Future Research Trends

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IEEE Internet of Things Journal 2025年第12期12卷 19043-19064页

作者： Bhatia, Munish Kumar, Vijay National Institute of Technology Department of Computer Applications Kurukshetra136119 India

A Digital Twin (DT) is a precise digital replica of an object or the physical world in which real-time data is input to model, analyze, forecast, and improve key performance indicators. To explore the complex landscape of DT research, the current scientometric review focuses on four pivotal components: Architecture, Application, Middleware, and Tools. For the study, the Citespace visualization tool is employed and articles are sourced from the Scopus database, focusing on publications between 2010 and 2024. The initial search yielded 11,247 publications, which, after applying filters dataset was reduced to a final dataset of 2,214 relevant articles. The view in the current study is centered on the following: keyword co-occurrence network (KCN), country analysis, document co-citation network (DCN), and burst reference analysis. The goal of the current study is to explore the four important components: architecture, applications, middleware, and tools. Limited previous study has explored such components. The current work presents a statistical summary of the development of research on DTs, examining existing patterns and highlighting important scientific discoveries. Finding the most popular trends and patterns of DT technology around the world will assist future researchers gain important knowledge about new sectors and indicate areas that could benefit from more research and development. © 2025 IEEE.

关键词： Middleware

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HPRXF Model: An Ensemble Transfer Learning-based Fusion model for handling Pandemic-related Calls received by the Emergency Response Support System

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2035-2046页

作者： Nimmi, K. Janet, B. Kalai selvan, A. Sivakumaran, N. Department of Computer Applications National Institute of Technology Tiruchirappalli India

The COVID-19 epidemic has surely disrupted daily life and resulted in unexpected shifts that have resulted in serious psychological reactions and mental health crises. In the early days of COVID-19, little was known about it, which understandably generates worry and distress, especially for those of us who suffer from anxiety disorders. In the early phases of the pandemic, there were large volume of calls to emergency lines, thus it was critical to divert non-emergency inquiries regarding the disease, particularly those looking for information, and to offer help for emergency calls about "breathing difficulty" and "person collapsing". It became vital to distinguish between emergency and non-emergency calls. This study examines the call content of calls landed in the emergency response support system during the pandemic and help in categorising the calls as "emergency" or "non-emergency" based on call content related to pandemic. The proposed model is a Hadamard product-based pretrained ensemble model of RoBERTa and XLNet using the ERSS dataset. The performance of the HPRXF proposed model is evaluated using the Hadamard product of the final probability vectors of the RoBERTa and XLNet models. The HPRXF Model outperforms existing models with an accuracy: 81.16% and an F1-score: 80.66%. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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A novel multi document summarization with document-elements augmentation for learning materials using concept based ILP and clustering methods

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International Journal of computers and applications 2024年第2期46卷 78-89页

作者： Sakkaravarthy Iyyappan, K. Balasundaram, S.R. Department of Computer Applications National Institute of Technology Tiruchirappalli India

Multi Document Summarization (MDS) is a technique for extracting succinct summaries from groups of related documents. The usage of MDS in the e-learning context is more appealing for providing summaries for learning materials, which helps students and teachers to focus on key concepts of the learning materials. In a learning material, availability of non-textual document-elements such as figures/diagrams, plots, graphs, tables and algorithms can be seen widely, but keeping such document-elements in the summary is not possible as they are non-textual. This proposed work incorporates the text summarization approach with document-elements augmentation to the summary to provide a detailed coverage of information without exceeding the summary length constraint. The key information in the source text is identified by important phrase features and sentence features, and the summaries are generated by selecting important sentences using the Integer Linear Programming (ILP) framework while reducing the redundancy using pre-trained sentence vectors. The relationships between the summary and document-elements are identified through document-element snippet extraction and a Hierarchical Agglomerative Clustering approach. Experimental results of the proposed summary extraction and augmentation on educational dataset (EduSumm) show better performance compared to the state-of-the-art approaches. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Extraction

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Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

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Multimedia Tools and applications 2025年第9期84卷 6091-6111页

作者： Steni Mol, T.S. Sreeja, P.S. Department of Computer Science Hindustan Institute of Technology and Science Chennai India Department of Computer Applications Vellore Institute of Technology Chennai India

Various content-sharing platforms and social media are developed in recent times so that it is highly possible to spread fake news and misinformation. This kind of news may cause chaos and panic among people. The automatic and accurate detection of fake news is a complex task. To consider this issue, an automatic and effective fake news detection approach is implemented. The proposed methodology is named Adaptive Adam Adadelta Optimizer-based Deep Bi-directional Long Short Term Memory (Bi-LSTM), in which Adaptive Adam AO is the merging of Adadelta Optimizer (AO), Adaptive concept, and Adam Optimization. The Bidirectional Encoder Representations from Transformers (BERT) is used for the tokenization and then, feature mining is done. Also, the dimensionality of the features is reduced by Singular Value Decomposition (SVD) and Moving Principal Component Analysis (MPCA). From them, the top-N feature selection is performed using Joint Quantum Entropy. Finally, for fake news detection, Adaptive Adam AO trained Deep Bi-LSTM is used, which obtained maximum specificity, sensitivity, and accuracy of 0.928, 0.945, and 0.938. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Principal component analysis

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Semi supervised K–SVCR for multi-class classification

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Multimedia Tools and applications 2025年第9期84卷 6737-6753页

作者： Srivastava, Vivek Prakash Kapil Department of Computer Applications National Institute of Technology Haryana Kurukshetra136119 India

In recent developments, the traditional binary class SVM has evolved into a multi-class classifier utilizing a ‘1-versus-1-versus-rest’ approach named K−SVCR. This innovative version efficiently categorizes multi-class data samples, generating ternary mode outputs {-1,0,+1}, enabling simultaneous classification into three distinct classes. However, the availability of labelled data is scarce as it requires human effort for labelling it. But, the availability of unlabelled samples is easier as these are devoid of explicit class labels. This unlabelled data can be utilized by augmenting graph Laplacian regularisation term, evolving it into a semi-supervised learning approach for mode training. Thus, the model can capture samples’ distribution information available with abundant unlabeled data. Now, even a few labelled samples can elevate the effectiveness of the trained model on out-of-sample data. This article’s primary objective is to harness the potential of unlabeled data. To understand the intuition behind the proposed model two artificial datasets are considered. Further, the experiments were conducted on several real-world datasets from the UCI machine learning repository and the Flavia dataset for plant identification from leaf images, to evaluate the performance of the Semi-supervised graph Laplacian K−SVCR (Lap−KSVCR) model in terms of accuracy, precision, recall, and F-score of classification. It was observed that the model could achieve 63% accuracy by training on a single labelled sample per class in case of non-linear three class artificial datasets. Additionally, on UCI real-world dataset and Flavia dataset an improvement of up to 17.5%, 17.3%, 30.1%, and 17.2% was observed in accuracy, precision, recall, and f-score, respectively when compared to K−SVCR. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Support vector machines

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Anomalous data detection in cognitive IoT sensor network

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International Journal of Networking and Virtual Organisations 2024年第4期30卷 309-328页

作者： Jha, Vidyapati Tripathi, Priyanka Department of Computer Applications National Institute of Technology Chattisgarh Raipur India

Recent research in the internet of things (IoT) focuses on the insertion of cognition into its system architecture and design, which introduces the new discipline known as cognitive IoT (CIoT). The cognitive internet of things sensor network defines a new paradigm for bridging the gap between the virtual and the real world. Sensors integrated into the CIoT network serve as the primary data collectors. These sensors are used in hazardous or unmanaged a situation, which makes sensor readings prone to errors and abnormalities. Since sensor data are essential to the system’s operation, the quality of various data-centric CIoT services will ultimately depend on the accuracy of sensor readings. However, detecting anomalies in sensor data is a complex process because CIoT sensor networks are frequently resource-constrained devices with limited computation, networking, and storage power. To fulfil the objectives, an effective and affordable cognitively-inspired detecting method is required. Therefore, this research proposed a novel technique to identify the anomaly in sensor node data. The experimental evaluation is conducted on the environmental data of 21.25 years, and detection accuracy reveals the efficacy of the proposed method over competing approaches. © 2024 Inderscience Enterprises Ltd.

关键词： Internet of things

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Enhancing Image Captioning Using Deep Convolutional Generative Adversarial Networks

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Recent Advances in computer Science and Communications 2024年第5期17卷 37-47页

作者： Jaiswal, Tarun Pandey, Manju Tripathi, Priyanka Department of Computer Applications National Institute of Technology NIT Raipur India

Introduction: Image caption generation has long been a fundamental challenge in the area of computer vision (CV) and natural language processing (NLP). In this research, we present an innovative approach that harnesses the power of Deep Convolutional Generative Adversarial Networks (DCGAN) and adversarial training to revolutionize the generation of natural and contextually relevant image captions. Method: Our method significantly improves the fluency, coherence, and contextual relevance of generated captions and showcases the effectiveness of RL reward-based fine-tuning. Through a comprehensive evaluation of COCO datasets, our model demonstrates superior performance over baseline and state-of-the-art methods. On the COCO dataset, our model outperforms current state-of-the-art (SOTA) models across all metrics, achieving BLEU-4 (0.327), METEOR (0.249), Rough (0.525) and CIDEr (1.155) scores. Result: The integration of DCGAN and adversarial training opens new possibilities in image captioning, with applications spanning from automated content generation to enhanced accessibility solutions. Conclusion: This research paves the way for more intelligent and context-aware image understanding systems, promising exciting future exploration and innovation prospects. © 2024 Bentham Science Publishers.

关键词： Generative adversarial networks

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Monkeypox data enhancement and diagnosis using improved DCGAN

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Soft Computing 2025年第5期29卷 2497-2513页

作者： Tulasiram, Jinaga Banothu, Balaji Nickolas, S. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamilnadu Tiruchirappalli India

The recent emergence of the monkeypox virus has attracted significant attention due to its serious effects, including pneumonia, retinal problems, secondary skin infections, and rectal swelling, leading to discomfort and urinary issues. Therefore, accurate identification is crucial, but the lack of comprehensive databases for each condition presents a challenge. Although Generative Adversarial Networks (GANs) show promise for improving data through augmentation, their training is hindered by differences between random noise and image structures. Because GAN-based models take random noise as input for the generators, they must acquire an adaptive function to translate this noise from randomness to meaningful visuals, leads to difficulty in training and converging. To tackle this issue, a new technique is proposed for data augmentation using a Densely Connected Decoder Encoder Generative Adversarial Network (D3EGAN). This approach combines adversarial training with variational Bayesian inference to enhance image production performance. D3EGAN uses a reversed encoder–decoder decoder–encoder framework which is pre-trained to transform random noise into more informative vectors, improving the generator’s learning ability. With this enhanced dataset, advanced deep learning models are employed such as VGG-19, ResNet50, InceptionV3, and EfficientNet to distinguish monkeypox from other diseases. Our study’s results demonstrate significant improvements in image clarity and detection accuracy, surpassing current techniques’ performance. This study highlights the effectiveness of our methodology in improving timely identification and intervention for monkeypox and related diseases, addressing a significant global health issue. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Generative adversarial networks

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Enhancing neural network predictions with finetuned numeric embeddings for stock trend forecasting

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Soft Computing 2025年第3期29卷 1829-1844页

作者： Trivedi, Avinash Sangeetha, S. Department of Computer Applications National Institute of Technology Tamilnadu Trichy620015 India

The financial markets, particularly stock trading, offer a variety of profit-generating opportunities based on complex and volatile behaviour. Investors seek strategies to maximise returns, leading to an investigation of inherent market patterns. Converting OHLC (Open, High, Low, Close) data into transformers-based pre-trained language model compatible text is an innovative method for representing numeric data. Extending the language model’s utility to integrate stock market numeric time-series data incorporates its inherent numeracy in embeddings. Raw data are converted into a format compatible with the pre-trained language model through preprocessing and text templates. Using an ensemble of Bidirectional Encoder Representations from Transformers (BERT), FinBERT (BERT finetuned with the financial corpus), FLANG-BERT (BERT finetuned with the financial corpus) and FLANG-ELECTRA (ELECTRA finetuned with the financial corpus) as feature extractor, historical stock market data are utilised to generate an embedding matrix and fused with established neural network architectures, such as Backpropagation Neural Network (BPNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to predict stock market trends. The simulation results demonstrate that the proposed integrated approach is preferable to previous methodologies. The significance of the findings is confirmed by statistical validation using the Wilcoxon signed-rank test (p value © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Long short-term memory

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