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Multiview data fusion technique for missing value imputation in multisensory air pollution dataset

Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3173-3191页

作者： Middya, Asif Iqbal Roy, Sarbani Department of Computer Science and Engineering Jadavpur University Kolkata India

The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To address this problem, in this paper, a multi-view based missing value (MV) imputation method called MVDI (Multi-View Data Imputation) is proposed for air pollution related time series data. MVDI combines four models namely LSTM (Long-Short Term Memory), IDS (Inverse Distance Squared), SVR (Support Vector Regressor), and KNN (K-Nearest Neighbors) to estimate MVs. These four models are mainly employed to capture the variations in data from different views of the dataset. Here, different views represent different portions (subsets) of the actual dataset. The estimates of MVs from all the views are combined using a kernel function to get an overall result. The proposed model MVDI is evaluated on real-world air pollution dataset in terms of RMSE, MAE, MAPE, and R2. The experimental results show that MVDI dominates over the baseline methods namely AR (AutoRegressive), ARIMA (AutoRegressive Integrated Moving Average), RFR (Random Forest Regressor), ANN (Artificial Neural Network), LI (Linear Interpolation), NN (Nearest Neighbors), MI (Mean Imputation), CNN (Convolutional Neural Network), ConvLSTM (Convolutional LSTM). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Time series

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A Novel Audio-Visual Multimodal Semi-Supervised Model Based on Graph Neural Networks for Depression Detection

A Novel Audio-Visual Multimodal Semi-Supervised Model Based ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Yaqin Sun, Chenjian Dong, Yihong Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (纸本)9798350368741

There is a significant correlation between depression, verbal behavior, and facial expressions. By analyzing patients' audio and facial visuals, depression assessments can be conducted. However, existing work is predominantly based on single modalities. Additionally, acquiring a sufficient amount of labeled data in clinical settings is challenging and costly. To leverage multimodal audio-visual data while addressing the issue of lacking trainable labeled data, we propose an audiovisual multimodal semi-supervised depression detection model based on Graph Neural Networks (AVS-GNN). This model first extracts dual-modality temporal information from audio features and facial visual features of patients and obtains modality-specific high-level embedding representations through graph representation learning. Subsequently, it utilizes graph-based contrastive unsupervised learning to capture consistency information between pairs of unlabeled samples across different modalities and to facilitate cross-modal interactions. We specifically designed a hybrid weighted pseudo-labeling strategy to assign high-confidence pseudo-labels to unlabeled data and further retraining the model. Experiments on two depression datasets show that this model outperforms baseline methods across all evaluation metrics. © 2025 IEEE.

关键词： audio-visual multimodal depression detection graph neural networks semi-supervised

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A Comprehensive Review of Blockchain and Machine Learning Integration for Peer-to-Peer Energy Trading in Smart Grids

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IEEE Access 2025年 13卷 92756-92782页

作者： Fatima, Sameen Arshad, Muhammad Junaid University of Engineering & Technology Department of Computer Science Lahore54000 Pakistan

The transition from traditional energy or electrical grids to smart energy or electrical grids has significantly transformed energy management. This evolution emphasizes decentralization, efficiency, and sustainability in energy systems. However, it also introduces numerous risks, including cyber-physical system vulnerabilities and challenges in energy trading. The application of blockchain and Machine Learning (ML) offers potential solutions to these issues. Blockchain enhances energy transactions by making them safer, more transparent, and tamper-proof, while ML optimizes grid performance by improving predictions, fault detection, and anomaly identification. This systematic review examines the application of blockchain and ML in peer-to-peer (P2P) energy trading within smart grids and analyzes how these technologies complement each other in mitigating risks and enhancing the efficiency of smart grids. Blockchain enhances security by providing privacy for transactions and maintaining immutable records, while ML predicts market trends, identifies fraudulent activities, and ensures efficient energy use. The paper identifies critical challenges in smart grids, such as unsecured communication channels and vulnerabilities to cyber threats, and discusses how blockchain and ML address these issues. Furthermore, the study explores emerging trends, such as lightweight blockchain systems and edge computing, to overcome implementation challenges. A new architecture is proposed, integrating blockchain with ML algorithms to create resilient, secure, and efficient energy trading markets. The paper underscores the need for global standardization, improved cybersecurity measures, and further research into how blockchain and ML can revolutionize smart grids. This study integrates current knowledge with a forward-looking perspective, providing valuable insights for researchers, policymakers, and stakeholders in the energy sector to collaboratively build a future of efficient and int

关键词： Power management (telecommunication)

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Converse Attention Knowledge Transfer for Low-Resource Named Entity Recognition

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International Journal of Crowd science 2024年第3期8卷 140-148页

作者： Lyu, Shengfei Sun, Linghao Yi, Huixiong Liu, Yong Chen, Huanhuan Miao, Chunyan School of Computer Science and Technology University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

In recent years, great success has been achieved in many tasks of natural language processing (NLP), e.g., named entity recognition (NER), especially in the high-resource language, i.e., English, thanks in part to the considerable amount of labeled resources. More labeled resources, better word representations. However, most low-resource languages do not have such an abundance of labeled data as high-resource English, leading to poor performance of NER in these low-resource languages due to poor word representations. In the paper, we propose converse attention network (CAN) to augment word representations in low-resource languages from the high-resource language, improving the performance of NER in low-resource languages by transferring knowledge learned in the high-resource language. CAN first translates sentences in low-resource languages into high-resource English using an attention-based translation module. In the process of translation, CAN obtains the attention matrices that align word representations of high-resource language space and low-resource language space. Furthermore, CAN augments word representations learned in low-resource language space with word representations learned in high-resource language space using the attention matrices. Experiments on four low-resource NER datasets show that CAN achieves consistent and significant performance improvements, which indicates the effectiveness of CAN. © The author(s) 2024.

关键词： Labeled data

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CNN-Based Multi-Output and Multi-Task Regression for Supershape Reconstruction from 3D Point Clouds

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第5期49卷 37-48页

作者： Remmach, Hassnae Razak, Siti Fatimah Abdul Ullah, Arif Yogarayan, Sumendra Sayeed, Md Shohel Mrhari, Amine Computer Systems Engineering Laboratory Cadi Ayyad University Marrakesh40000 Morocco Faculty of Information Science and Technology Multimedia University Ayer Keroh75450 Malaysia Research in Computer Science Laboratory Faculty of Sciences Ibn Tofail University Kenitra Morocco

In a number of industries, including computer graphics, robotics, and medical imaging, three-dimensional reconstruction is essential. In this research, a CNN-based Multi-output and Multi-Task Regressor with deep learning capabilities is proposed for three-dimensional object reconstruction from 3D point cloud. Our approach is grounded in the original Point Net architecture, which addresses the difficulties associated with convolution when applied to point clouds. Firstly, this paper is modified using a Multi-Output Regressor to accurately recreate Super forms from 3D point clouds. Using this method, we first extract features from the 3D point cloud using Point Net. After that, a Multi-Output Regressor receives these data and uses them to anticipate the Super shape parameters needed to reconstruct the shape. Taking in the data, the Multi-Output Regressor retrieved characteristics from Point Net and simultaneously predicts several outcomes. Second, a Multi-Task Regressor is used to modify the Point Net. The network gains from the capacity to transfer knowledge from one task to another, improving the model's overall performance. The model would forecast the ten parameters needed to create the shape in the case of rebuilding Super shapes. The test findings were better than expected;they are intriguing in terms of prediction accuracy and cost, and they update the result by 80%, which is a good accomplishment for the study. © 2025 Slovene Society Informatika. All rights reserved.

关键词： 3D reconstruction

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AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias

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computers, Materials & Continua 2024年第2期78卷 2137-2158页

作者： S.Jayanthi S.Prasanna Devi Department of Computer Science and Engineering SRM Institute of Science and Technology-Vadapalani CampusChennaiTamil NaduIndia

In healthcare,the persistent challenge of arrhythmias,a leading cause of global mortality,has sparked extensive research into the automation of detection using machine learning(ML)***,traditional ML and AutoML approaches have revealed their limitations,notably regarding feature generalization and automation *** glaring research gap has motivated the development of AutoRhythmAI,an innovative solution that integrates both machine and deep learning to revolutionize the diagnosis of *** approach encompasses two distinct pipelines tailored for binary-class and multi-class arrhythmia detection,effectively bridging the gap between data preprocessing and model *** validate our system,we have rigorously tested AutoRhythmAI using a multimodal dataset,surpassing the accuracy achieved using a single dataset and underscoring the robustness of our *** the first pipeline,we employ signal filtering and ML algorithms for preprocessing,followed by data balancing and split for *** second pipeline is dedicated to feature extraction and classification,utilizing deep learning ***,we introduce the‘RRI-convoluted trans-former model’as a novel addition for binary-class *** ensemble-based approach then amalgamates all models,considering their respective weights,resulting in an optimal model *** our study,the VGGRes Model achieved impressive results in multi-class arrhythmia detection,with an accuracy of 97.39%and firm performance in precision(82.13%),recall(31.91%),and F1-score(82.61%).In the binary-class task,the proposed model achieved an outstanding accuracy of 96.60%.These results highlight the effectiveness of our approach in improving arrhythmia detection,with notably high accuracy and well-balanced performance metrics.

关键词： Automated machine learning neural networks deep learning arrhythmias

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Efficient Heuristic Replication Techniques for High Data Availability in Cloud

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computer Systems science & engineering 2023年第6期45卷 3151-3164页

作者： H.L.Chandrakala R.Loganathan Department of Computer Science and Engineering School of EngineeringPresidency UniversityIndia Department of Computer Science and Engineering HKBK College of EngineeringIndia

Most social networks allow connections amongst many people based on shared *** networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a *** replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response *** simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application *** resolve the non-deterministic issue of replication,metaheuristics are *** this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of *** on large datasets show the effectiveness of the proposed *** is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

关键词： Cloud computing data replication bandwidth saving Tabu search Harmony search hybrid Harmony-Tabu search

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A comprehensive survey of image compression methods: from prediction models to advanced techniques

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Multimedia Tools and Applications 2025年 1-39页

作者： Devadason, Joshua Rajah D, Aju School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techniques are reviewed in this paper. The first group of techniques is the data compression technique. The data compression methods are Huffman encoding, Lempel–Ziv Welch (LZW), arithmetic encoding, run length encoding, and Shannon-Fano encoding. The second group of methods is prediction, here the neighboring pixels are utilized in the prediction of the present pixel and the error is encoded using an entropy encoder. In wavelet-based techniques, the source image is transformed from the spatial domain to the wavelet domain, and the coefficients are encoded. In learning-based techniques, a neural network is used to model the image data for compression. Fractal encoding is a compression method that searches for similar blocks in an image and compresses the difference between the blocks. With an average bitrate of 4.87 bpp (bits per pixel) on the first 10 photos of the Digital Imaging and Communications in Medicine (DICOM) image collection, the results indicate that LZW is the top performing data compression technique among the data compression techniques. In prediction-based techniques, Context Adaptive Lossless Image Codec (CALIC) is the best performing with 3.5 bpp. The modern neural method, compressive autoencoder outperforms established methods such as Joint Photographic Experts Group (JPEG), by achieving a PSNR of 35 on the Kodak dataset compared with 32 PSNR on the same dataset at 1.0 bpp. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Pixels

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3D brain image based tumor classification using ensemble of reinforcement transfer-based belief neural networks

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Multimedia Tools and Applications 2025年第14期84卷 14001-14028页

作者： Arora, Shraddha Lamba, Monika Department of Computer Science and Engineering The NorthCap University Haryana Gurugram India

The procedure of segmenting a brain tumour is an essential step in the field of medical image processing. In order to improve the efficacy of disease treatment choices and the likelihood of patient survival, the timely detection of a brain tumour is not only highly essential but a vital factor. For the purpose of cancer diagnosis, manually segmenting brain tumours from the large number of MRI images that are generated in clinical practice is a hard and time-consuming process. Automatic brain tumour image segmentation is a technique that absolutely has to exist. Using deep learning and machine learning architectures for segmentation and feature extraction, this research proposes a novel approach to the detection of brain tumours. In this case, the input consists of a collection of 3-D brain slice pictures that have been smoothed and with noise eliminated. After that, the picture that has been processed is segmented by using a convolutional radial function architecture that centres on contours. The segmented image features retrieval has been accomplished by the use of an ensemble of reinforcement transfer-based belief neural networks. The results of the experimental study have been completed with regard to precision, recall, accuracy, F-1 score, area under the curve, dice coefficient, and root mean square error. The strategy that was recommended was successful in achieving 97.57% accuracy, 97.15% precision, 89.37% recall, 83.56% F-1 score, 69.59% area under the curve, 63.83% dice coefficient, and 55.56% root mean square error. The sagittal, coronal, and axial planes of the pictures were shown in order to acquire the specific position of the tumour as well as its characteristics, which included its height, breadth, and depth. When it comes to the prediction of tumours, the findings of the assessment of semantic segmentation based on deep learning networks are highly positive. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Spri

关键词： Feature extraction

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Feature engineering in learning-to-rank for community question answering task

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International Journal of computers and Applications 2024年第8期46卷 555-566页

作者： Sajid, Nafis Hasan, Md. Rashidul Ibrahim, Muhammad Department of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Community question answering (CQA) forums are Internet-based platforms where users ask questions about a topic and other expert users try to provide solutions. Many CQA forums such as Quora, Stackoverflow, Yahoo!Answer, StackExchange exist with a lot of user-generated data. These data are leveraged in automated CQA ranking systems where similar questions (and answers) are presented in response to the user's query. In this work, we empirically investigate a few aspects of this domain. First, in addition to traditional features like Term Frequency (TF), Inverse Document Frequency (IDF), Best Match 25 (BM25), etc., we introduce a Bidirectional Encoder Representations from Transformers (BERT)-based feature that captures the semantic similarity between the question and answer. Second, most of the existing research works have focus on features extracted only from the question part;features extracted from answers have not been explored extensively. We combine both types of features in a linear fashion. Third, using our proposed concepts, we conduct an empirical investigation with different rank-learning algorithms, some of which have not been used so far in CQA domain. On three standard CQA datasets, our proposed framework achieves state-of-the-art performance (0.56, 0.58 and 0.60 NDCG@10 values). We also analyze the importance of the features we use in our investigation. This work is expected to guide the practitioners to devise a better set of features for the CQA retrieval task. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Semantics

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