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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

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Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

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Modeling and Evaluating Stock Market and Commodity Price Fluctuation Using Time Series Sequence Learning

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SN computer Science 2025年第5期6卷 1-18页

作者： Veningston, K. Ronalda, M. Shaikh, Tawseef Ayoub Gazi, Farhaan Bhagat, Sanket Department of Computer Science and Engineering National Institute of Technology Srinagar Jammu and Kashmir Srinagar 190006 India

Algorithmic trading using neural network models has gained significant attention in predicting stock and commodity price fluctuations and understanding their dynamics. Deep learning techniques have shown significant results in predicting stock market prices in recent years. Modelling stock market price dynamics using sequence learning techniques leverages the historical trends and dependencies in price data to predict future price movements. This paper presents a comprehensive analysis of Autoregressive Integrated Moving Average (ARIMA), standard neural network, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for forecasting stock movements in various industry stocks. The main objective is to assess the performance of each model in predicting stock price fluctuations. Each model undergoes systematic validation and evaluation based on key performance indicators such as accuracy, normalised root mean squared error (NRMSE), and Weighted Mean Absolute Percentage Error (WMAPE). The forecast made by each model is compared to the actual stock prices to measure the effectiveness of the forecasting methods. This paper presents a time series sequence learning approach for modelling and evaluating stock market price fluctuation. The proposed approach uses an LSTM network to learn the sequential patterns in stock price data. The LSTM network is trained on a historical dataset of stock prices, and it is then used to predict future stock prices. The proposed approach is evaluated on a dataset of stock prices from seven different industry domains, including Investment, Infrastructure, Information technology, Pharma, Fast Moving Consumer Goods and agri-business (FMCG), Vehicles, and Banking, which enables a comprehensive evaluation of various models. The results show that the proposed LSTM approach can achieve notable accuracy in predicting future stock prices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Lt

关键词： Algorithmic trading Sequence learning Share price dynamics Stock market price forecast Time-series analysis

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Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

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SN computer Science 2024年第8期5卷 1000页

作者： Singh, Abhijeet Pratap Chaurasia, Brijesh Kumar Tripathi, Ashish Department of Information Technology Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

A revolutionary approach for enhancing driving efficiency and safety in intelligent transportation systems (ITS) is deploying autonomous vehicles. Vehicle-to-everything (V2X) technology facilitates interactions between vehicles and infrastructure. However, the Internet of Vehicles (IoV) is susceptible to many cyberattacks, encompassing impersonation, denial of service (DoS), and fuzzy assaults. This paper proposes an intelligent network intrusion detection system (NIDS) using machine learning algorithms. The usage of the ML approach in a Stacking-enabled Ensemble Learning-based Intrusion Detection Scheme (SELIDS) for IoV is proposed. We additionally examine each technique’s shortcomings and how they affect the NIDS efficiency. Deploying the proposed NIDS on the benchmark dataset demonstrates the capacity of the system to recognize different kinds of assaults. Finally, we explore the potential for NIDS to collaborate with additional security technologies in the future. Empirical results prove the efficacy of the proposed mechanism. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attacks Intelligent transportation systems (ITS) Internet of vehicles (IoV) Machine learning (ML) Network intrusion detection system (NIDS)

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Homomorphic Encryption Library, Framework, Toolkit and Accelerator: A Review

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SN computer Science 2024年第1期5卷 24页

作者： Dhiman, Shalini Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

Homomorphic encryption ensures secure computation on encrypted data without the need for decryption beforehand. It enables the secure offloading of computations to untrusted servers. This paper provides a comprehensive description of multiple methods for conducting secure computations, along with the appropriate approaches and tools needed for successful implementation. Various strategies are outlined for selecting a suitable homomorphic encryption (HE) library, assisting developers and researchers in determining the most suitable library for their projects. To begin with, the paper presents a comparison of homomorphic encryption libraries based on different parameters. Additionally, it outlines the steps involved in choosing the right framework for effective implementation, comparing them based on various parameters. The Framework discussed in the paper supports a range of homomorphic encryption libraries and provides detailed information about them. Furthermore, the paper surveys three different homomorphic encryption accelerators and compares them to determine which one would maximize bootstrapping throughput when implemented. Lastly, the Fully Homomorphic Encryption-IBM toolkit is discussed. This toolkit supports the development of resources, primarily involved in the flow of messages. The paper concludes that secure computation is achievable by implementing the appropriate tools, considering their performance and implementation limitations. Moreover, the selection of the appropriate library, framework, and accelerator depends on the specific demands and requirements of the implementation chosen by the developer. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Evervault encryption engine HEaaN Homomorphic encryption IBM toolkit MS SEAL Pallisade

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Chasing Pelican based Deep Learning for Multiple Object Detection from Single Input Trash Image

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Multimedia Tools and Applications 2025年第18期84卷 18801-18828页

作者： Hingmire, Amruta Pujeri, Uma Research Scholar Department of Computer Engineering and Technology Dr Vishwanath Karad MIT World Peace University Maharashtra Pune India Department of Computer Engineering and Technology Dr Vishwanath Karad MIT World Peace University Maharashtra Pune India

Waste management is essential for developing a smart city to enhance the population's living standards. Harmonious and healthy living environments are achieved through waste sorting and recycling techniques. Waste object detection is crucial for waste sorting and recycling. Several methods were devised for the waste object detection in the image, but the inaccurate detection and computation overhead limit the model's performance. Hence, optimized deep learning named Chasing Pelican-based Adaptive Mask Region-Based Convolutional Neural Network (RCNN) is proposed for waste object detection to enhance detection accuracy with minimal computation overhead. The adaptive learning concept is incorporated in Mask RCNN to improve the accuracy of detection. Besides, optimal weight adjustment of Adaptive Mask RCNN using the Chasing Pelican algorithm minimizes the information loss during the data learning phase. The inclusion of the adaptation concept helps the model to better adapt to the features of waste objects in different environmental situations, resulting in more accurate detection results. Furthermore, the optimal weight adjustment of Adaptive Mask RCNN using the Chasing Pelican algorithm plays a crucial role in minimizing information loss during the data learning phase. This optimization ensures that the model efficiently learns and retains essential characteristics and patterns from the data, resulting in better detection performance. Thus, the proposed method accomplished enhanced accuracy in detecting the trash object based on F1-Score, recall, precision, accuracy, mAP, Mean Absolute Percentage Error (MAPE) and mean square error (MSE) obtained 96%, 99.75%, 99.75%, 99.875%, 99.75%, 0.3% and 0.4% respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Object detection

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Dynamic strategy to use optimum memory space in real-time video surveillance

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2771-2784页

作者： Biswas, Tamal Bhattacharya, Diptendu Mandal, Gouranga Department of Computer Science and Engineering National Institute of Technology Agartala Tripura Agartala India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

In present days real-time video surveillance is a very essential aspect of establishing safety, observing traffic, and detecting violence and crimes. Intelligent video surveillance (IVS) is one of the most acknowledged frameworks in a security application. But, video surveillance systems consume huge memory space to store the recorded video, especially in the case of high-resolution recording. However, if we use low-resolution video recording then the video quality will be reduced and the objects/violence cannot be identified clearly. To overcome the stated limitation, this article introduced a smart video surveillance system with a dynamic strategy to use optimum memory space with the best surveillance objective. The proposed system can dynamically record video with high-resolution during suspicious movement of objects/violence as well as low-resolution video at other times. The system is based on the detection of suspicious movement of objects in consecutive frames, saving of suspicious frames with high resolution, and neglecting of less important frames. Finally, the Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Color Channel is applied to all the suspicious frames as a post-processing step for better contract adjustment and to provide a clear view of suspicious objects in frames. The quality of enhanced frames are assessed by two no-reference methods, i.e. NIQMC and BIQME. Multiple visual quality parameters like sharpness, contrast, colorfulness, brightness, and naturalness of frames, etc. are used for assessment Several real-time experiments reveal that the proposed system can detect suspicious objects with 97.65% accuracy. Besides, it consumes 75% less memory space in real-time surveillance. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Video recording

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Computation of Graph Fourier Transform Centrality Using Graph Filter

IEEE Open Journal of Circuits and Systems

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IEEE Open Journal of Circuits and Systems 2024年 5卷 69-80页

作者： Tseng, Chien-Cheng Lee, Su-Ling National Kaohsiung University of Science and Technology Department of Computer and Communication Engineering Kaohsiung824005 Taiwan Chang Jung Christian University Department of Computer Science and Information Engineering Tainan711301 Taiwan

In this paper, the computation of graph Fourier transform centrality (GFTC) of complex network using graph filter is presented. For conventional computation method, it needs to use the non-sparse transform matrix of graph Fourier transform (GFT) to compute GFTC scores. To reduce the computational complexity of GFTC, a linear algebra method based on Frobenius norm of error matrix is applied to convert the spectral-domain GFTC computation task to vertex-domain one such that GFTC can be computed by using polynomial graph filtering method. There are two kinds of designs of graph filters to be studied. One is the graph-aware method;the other is the graph-unaware method. The computational complexity comparison and experimental results show that the proposed graph filter method is more computationally efficient than conventional GFT method because the sparsity of Laplacian matrix is used in the implementation structure. Finally, the centrality computations of social network, metro network and sensor network are used to demonstrate the effectiveness of the proposed GFTC computation method using graph filter. © 2020 IEEE.

关键词： Complex networks

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis

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SN computer Science 2024年第6期5卷 756页

作者： Ganesh, V.N. Gulappagol, Laxmi Pushparani, M.K. Ganesh, K. Department of Electronics and Communication Engineering Alvas Institute of Engineering and Technology Department of Internet of Things and Cyber Security including Block chain Technology A J Institute of Engineering and Technology Department of Computer Science & amp Design Alvas Institute of Engineering and Technology

Early and accurate diagnosis of melanoma, a potentially life-threatening skin cancer, is crucial for improving patient outcomes. In this study, we propose a novel approach for melanoma detection, termed Panoptic Region Slicing Segmentation (PRS2) using an optimized convolution neural network (PRS2-OCNN) based on AlexNet. The proposed system integrates several advanced methods to enhance the accuracy and efficiency of melanoma identification. The initial step involves preprocessing the dermoscopic image using a 2D Fusion Filter, which enhances the image quality and prepares it for subsequent analysis. Next, the Panoptic Region Slicing Segmentation (PRS2) method is applied to emphasize the boundary regions, allowing for precise localization of melanoma-affected areas. To assess color variations within the segmented regions, we employ Threshold Histogram Evaluation (THE), which effectively characterizes melanoma-specific color patterns. The features extracted using Spread Spectral Menzies’s Feature Selection (SSMFS), reducing the dimensionality and improving the efficiency of the subsequent analysis. The core of our proposed approach lies in the optimized convolution neural network, derived from the influential AlexNet architecture. By fine-tuning the AlexNet-OCNN on the reduced feature set, we maximize its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN identifies melanoma classes according to their risk severity, aiding dermatologists in making informed decisions for timely and appropriate *** evaluations were conducted on a diverse and extensive dataset of dermoscopic images. The proposed system demonstrated superior performance compared to existing methods, exhibiting heightened detection accuracy by deeply analyzing the melanoma-affected regions. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： 2D fusion filter AlexNet Early diagnosis Melanoma Panoptic region slicing segmentation PRS2-OCNN

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Identification of products for campaigning with budget constraint

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International Journal of Information technology (Singapore) 2025年第2期17卷 727-748页

作者： Kumar, Abhishek Bishnu, Partha Sarathi Department of Computer Science Birla Institute of Technology Mesra India

technology helps producers to collect enormous amounts of customer-product interaction (CPI) data. From the collected CPI data, the importance of the customers and the products can be measured. This study focuses on finding the best number of products for the business for campaign selection based on customers’ liking and disliking of products and their frequency of purchases. Campaign selection is an essential process for marketing, and when the total budget of the campaign is fixed, identifying the optimal products for campaigning is challenging. This paper aims to identify the important existing customers and products and identify the optimal product combinations for campaign selection. We propose two algorithms to identify highly valuable customers and products and solve the complex optimization problem of identifying the optimal product combinations for campaign selection that maximize the business’s profits. We compare our proposed algorithms on real and synthetic datasets, and the results are presented. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Algorithm Business intelligence Data science Machine learning Marketing

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