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Exploring Nonuniform Structure of NSRTDs for Characterizing MACA Rules in the Null-Boundary Condition

Complex Systems 2025年第1期34卷 91-127页

作者： Hazra, Suvadip Banerjee, Som Dalui, Mamata Chakraborty, Bidesh Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad58009 India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

The cellular automaton (CA), a discrete model, is gaining popularity in simulations and scientific exploration across various domains, including cryptography, error-correcting codes, VLSI design and test pattern generation. This paper examines a two-state three-neighborhood nonuniform finite CA with a specified number of fixed points. We utilize a graph-based tool known as the next state rule minterm transition diagram (NSRTD) to analyze the spatiotemporal behavior of these cellular automata (CAs) across all lengths. We devise methods for synthesizing nonuniform single length cycle multi-attractor CAs (MACAs), a specialized class of irreversible CA with a predefined number of fixed points for any arbitrary length. Despite the method’s exponential worstcase time complexity, it offers the advantage of selecting rules for each CA cell from a set of candidate rules, ensuring the desired number of fixed points. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： cellular automata fixed-point attractor length cycle multi-attractor cellular automata MACA next state rule minterm transition diagram NSRTD

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Parkinson’s disease Severity Detection Based on Fused features with tuned Residual Deep Convolutional Neural Network

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Multimedia Tools and Applications 2025年第14期84卷 13111-13131页

作者： Vishwakarma, Gagan Nandanwar, Amit Kumar Thakur, Ghanshyam Singh Computer Science & Engineering Indian Institute of Information Technology MP Bhopal India Computer Science & Engineering Maulana Azad National Institute of Technology MP Bhopal India Department of Computer Applications Maulana Azad National Institute of Technology MP Bhopal India

Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. During the diagnosis process, clinicians must consider gait abnormalities as one of the key symptoms. However, relying solely on clinical judgment for the diagnosis of PD may have limitations. In this work, we have proposed a Multikernel SVM (MK-SVM) based Parkinson's severity analysis method. Initially, an adaptive median filter is used to reduce the noise in the gait image. Then, to extract the features, we propose a dual-layer method that employs stacked autoencoders (SAEs) for local feature extraction and residual deep convolution neural networks (RDCNN) for global feature extraction. The hyper-parameters from the RDCNN are optimized using the Enhanced walrus optimization algorithm (EWO). In the final step, we use MK-SVM to classify PD according to severity. This model is evaluated using the GAIT-IT and GAIT-IST datasets. With the help of Python, we evaluated the proposed model in terms of precision, accuracy, recall, specificity, and F-scores, and the model achieved a maximum accuracy of 97.3%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data

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Multimedia Tools and Applications 2024年第37期83卷 85139-85168页

作者： Dubey, Madhuri Tembhurne, Jitendra Makhijani, Richa Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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Silver Lining in the Fake News Cloud: Can Large Language Models Help Detect Misinformation?

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 14-24页

作者： Kumar, Raghvendra Goddu, Bhargav Saha, Sriparna Jatowt, Adam Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India University of Innsbruck Department of Computer Science Innsbruck6020 Austria

In the times of advanced generative artificial intelligence, distinguishing truth from fallacy and deception has become a critical societal challenge. This research attempts to analyze the capabilities of large language models (LLMs) for detecting misinformation. Our study employs a versatile approach, covering multiple LLMs with few- and zero-shot prompting. These models are rigorously evaluated across various fake news and rumor detection datasets. Introducing a novel dimension, we additionally incorporate sentiment and emotion annotations to understand the emotional influence on misinformation detection using LLMs. Moreover, to extend our inquiry, we employ ChatGPT to intentionally distort authentic news as well as human-written fake news, utilizing zero-shot and iterative prompts. This deliberate corruption allows for a detailed examination of various parameters such as abstractness, concreteness, and named entity density, providing insights into differentiating between unaltered news, human-written fake news, and its LLM-corrupted counterpart. Our findings aspire to furnish a refined framework for discerning authentic news, human-generated misinformation, and LLM-induced distortions. This multifaceted approach, utilizing various prompt techniques, contributes to a comprehensive understanding of the subtle variations shaping misinformation sources. © 2024 IEEE.

关键词： Iterative methods

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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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SN computer science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

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Early detection of stroke disease using patients previous medical data instil with deep learning

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Multimedia Tools and Applications 2025年第16期84卷 16853-16881页

作者： Diwan, Tausif Gajbhiye, Saurav M. Goydani, Purva R. Gannarpwar, Vedant R. Khandait, Harshal R. Tembhurne, Jitendra V. Sahare, Parul Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Early detection of any disease and starting its treatment in this early stage are the most important steps in case of any life-threatening disease. Stroke is not an exception in this regard which is one of the leading causes of death and disability worldwide. We develop a simple but efficient deep neural network for the stroke prediction that accurately evaluates the probability of occurrence of stroke disease by treating this as a binary classification problem on one of the standard datasets named as Stroke Prediction Dataset available on Kaggle. With the help of effective pre-processing techniques such as SMOTE and FastICA on the noisy and imbalanced dataset, we could achieve improved performance for the binary classification of stroke prediction by employing a deep dense neural network. The architectural hyper-parameters are critically designed with the help of Keras Tuner and consisting of just ten layers in the deep dense neural network. With the help of this light weight deep dense model, we report an accuracy of 95%, AUC Score of 100% and F1-Score of 95% on a testing set. Moreover, we also present comparative illustration of various machine learning models for the aforesaid task along with the comparative illustration of various state of the arts with our proposed model for the stroke prediction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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Counting Malicious Host Behind NAT Using Ensemble Machine Learning Approach 6

Counting Malicious Host Behind NAT Using Ensemble Machine Le...

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Shukla, Sanjeev Misra, Manoj Varshney, Gaurav Indian Institute of Technology Roorkee Dept. of Computer Science and Engg Uttrakhand Roorkee India Indian Institute of Technology Jammu Dept. of Computer Science and Engg J&K Jammu India

ISBN: (纸本)9798350374162

Network Address Translation (NAT) is widely used to map private IP addresses to public ones, offering anonymity that not only benefits legitimate users by protecting their internal IP addresses but also poses a challenge by enabling malicious users to obscure their identities during cyber-attacks. Identifying the actual host behind NAT involved in such activities is critical for effective forensic analysis. In this study, we introduce a novel, passive, and non-intrusive technique that leverages ensemble machine learning (ML) to pinpoint and characterize malicious hosts behind NAT. By analyzing network traffic patterns at the flow level, our method successfully penetrates the veil of anonymity NAT provides. Designed to be operating system-agnostic and independent of IP addresses, our approach addresses privacy concerns while maintaining high efficacy. Demonstrating a remarkable accuracy rate of 99.12%, our technique not only outperforms existing ML and non-ML-based methods but also sets a new benchmark in detecting malicious entities operating behind NAT. © 2024 IEEE.

关键词： Anonymity

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Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI 17

Unlocking Eye Gestures with Earable Inertial Sensing for Acc...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chugh, Garvit Chakraborty, Suchetana Chakraborty, Sandip Indian Institute of Technology Department of Computer Science and Engineering Jodhpur India Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India

ISBN: (纸本)9798331531195

Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer's, accessibility, etc. The conventional method for eye gesture detection needs visual information processing devices or smart glasses to capture the movement patterns of the eyeballs. In this paper, we present a novel framework that utilizes inertial measurement unit (IMU) sensors embedded in earable devices for real-time eye gesture detection, which can work as a complementary to visual information processing devices. The framework translates specific eye movements into actionable commands, enabling hands-free interaction for users with limited mobility. Through extensive experimentation with 14 participants across diverse activities such as sitting, walking, running, and driving, the framework demonstrated high accuracy in eye gesture recognition, achieving a macro F1 score of 0.85 on our self-collected dataset. © 2025 IEEE.

关键词： Eye movements

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A Review on Smart Healthcare employing Quantum Internet of Things

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IEEE engineering Management Review 2024年 1-9页

作者： Sutradhar, Kartick Venkatesh, Ranjitha Venkatesh, Priyanka Department of Computer Science and Engineering Indian Institute of Information Technology Sri City India Department of Computer Science and Engineering GITAM School of Technology Bengaluru India Department of Computer Science and Engineering Presidency University Bengaluru India

The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE

关键词： Internet of things

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Workout Action Recognition in Video Streams Using an Attention Driven Residual DC-GRU Network

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computers, Materials & Continua 2024年第5期79卷 3067-3087页

作者： Arnab Dey Samit Biswas Dac-Nhuong Le Department of Computer Science and Technology Indian Institute of Engineering Science and TechnologyShibpurHowrah711103India Faculty of Information Technology Haiphong UniversityHaiphong180000Vietnam

Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd datas

关键词： Workout action recognition video stream action recognition residual network GRU attention

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