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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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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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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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Title-Based Topic Modeling on E-learning Web Content Titles Using BERTopic Model 5th

Title-Based Topic Modeling on E-learning Web Content Titles ...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

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

ISBN: (纸本)9789819747108

In today’s information-rich digital age, the volume of web content available to users has become overwhelming, making it challenging for individuals to find relevant and personalized content. Recommendation systems have emerged as a transformative solution, catering to individual users by offering customized suggestions aligned with their unique interests. This research explores a novel approach that utilizes topic modeling techniques on web content titles for recommendation purposes. Topic modeling, a subfield of natural language processing (NLP) is utilized to automatically identify latent topics within a large corpus of text. The proposed work begins by collecting a diverse dataset of web content titles across the domains. It employs a combination of other state-of-the-art topic modeling algorithms like BERTopic modeling and statistical model to uncover the underlying topics in the titles. By leveraging this approach on web content titles, aim to extract meaningful themes and categorize the content efficiently. Then preprocess the data to remove irrelevant information, ensuring that the subsequent topic modeling process yields accurate and meaningful results. This approach not only expedites the recommendation process but also conserves computational resource. Once the topics are identified, associate them with appropriate metadata, such as user preferences, and content types. This step forms the foundation of our content-based recommendation approach. Then maps the user’s interests to the most relevant topics, enabling us to present a tailored list of web content titles. By recommending content based on underlying themes rather than just keywords, this approach surpasses traditional methods, ensuring more accurate and diverse suggestions for users. The results demonstrate the system’s ability to provide highly personalized recommendations, enhancing user satisfaction and engagement. By delving into the semantic structure of content rather than relying solely on

关键词： Modeling languages

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Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

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Recent Patents on Engineering 2025年第1期19卷 41-49页

作者： Anandaraj, A. Alphonse, P.J.A. Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures. Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance. Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories;feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn. Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time. Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels. © 2025 Bentham Science Publishers.

关键词： Electroencephalography

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Classifying the state of knowledge-based question answering: patterns, progress, and prospects

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International Journal of computers and applications 2025年第1期47卷 93-105页

作者： Sharma, Sumit Jain, Sarika Tiwari, Mukesh Kumar lal, Sohan Department of Computer Science and Engineering Quantum University Roorkee Uttarakhand Roorkee India Department of Computer Applications National Institute of Technology Haryana Kurukshetra India

In response to inquiries posed in natural languages, question-answering systems (QASs) produce responses. The capabilities of early QASs are limited because they were designed for certain domains. The current generation of QASs focuses on the types of inquiries that users typically enquire, the features of the data sources that are used, and the formats in which the right responses are produced. Many QASs have been developed since the 1960s when research on the subject first started. There are several cutting-edge frameworks for answering questions that can be used to categorize the QA system according to a number of criteria. The necessity for an extensive survey on QASs comes naturally in order to determine the future focus of research in this field. In this study, QASs are analyzed by the number of criteria used to categorize question-answering (QA) and provide the dimension using a variety of standards to cover all the QAS. We describe the current state of research in each QAS area and make recommendations for its future direction. Based on the dimension criteria outlined in the research methodology, research studies are chosen for additional review and comparison. Constructed some criteria to classify the QASs, we provide the classification and go over the benefits and drawbacks of each class's QAS, as well as the problems with the corresponding study. Results are detailed summarized and compared. As a result of this study, a general architecture of frameworks for QA over knowledge base (KB) is drawn in addition to the comparative analysis of systems, serving as a reference for academics working on related topics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Question answering

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Progressive reasoning: a cognitively inspired method for interesting pattern extraction in cognitive IoT

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Iran Journal of computer Science 2025年 1-18页

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

This research pioneers the integration of cognitive capabilities into the Internet of Things (IoT), creating a transformative subfield known as cognitive IoT (CIoT). Building on IoT’s foundational principles, CIoT addresses the immense challenge of deriving meaningful insights from the vast data it generates. To tackle this complexity, the study proposes progressive reasoning, a cognitively inspired method for processing sensory data and uncovering significant patterns. At its core, progressive reasoning treats sensory observations as relationships, seeking their transitive closure—a state that signifies the progression of data as it transitions through various states. This progression enables the transformation of raw data into meaningful insights, revealing patterns and their probabilistic occurrences. By quantifying and analyzing this transformation, the research offers a robust mechanism to extract value from CIoT datasets. The methodology’s effectiveness is validated through experimental analysis of environmental and healthcare data, achieving an impressive accuracy of over 99.26%. This success highlights its potential to revolutionize critical applications in fields like healthcare and environmental monitoring by enabling smarter, more efficient systems. This study not only advances the technical boundaries of CIoT but also underscores the power of blending cognitive processes with technology to drive deeper understanding and impactful innovation. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cognitive IoT Heterogeneous Massive Progressive reasoning

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Imprecise reasoning: extracting precision in imprecision for knowledge discovery in cognitive IoT

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Iran Journal of computer Science 2025年 1-19页

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

An emerging field within the internet of things (IoT) known as cognitive IoT (CIoT) has gained attention due to the growing interest in integrating cognitive capabilities into IoT systems. Similar to IoT, which consists of a network of interconnected devices, CIoT generates vast amounts of data that are diverse, unpredictable, and time-sensitive. Consequently, efficiently extracting computational knowledge from these large volumes of real-time sensor data presents a significant challenge for CIoT. This study proposes a novel approach to identifying meaningful concepts and patterns through imprecise reasoning. Initially, the proposed method addresses the impact of noisy inputs, followed by the construction of a concept lattice. The weight of the generated concepts is computed using plausibility values, allowing for the selection of minimally weighted (significant) concepts. In the next phase, relevant data are extracted from these significant concepts, which are then combined to form meaningful patterns. The effectiveness of the proposed approach is empirically evaluated using weather data spanning 21.25 years. A variety of metrics were employed to validate the proposed method, and the results, with an accuracy exceeding 99.57%, demonstrate that the proposed algorithm outperforms existing competitors. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cognitive IoT Concept lattice Imprecise reasoning Significant pattern

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