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A hybrid machine learning approach for enhanced anomaly detection in drinking water quality

International Journal of Environmental Studies 2024年第2期81卷 661-674页

作者： Kalaivanan, K. Vellingiri, J. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Machine learning has become important for anomaly detection in water quality prediction. Data anomalies are often caused by the difficulties of analysing large amounts of data, both technical and human, but approaches have been inadequate when water contamination increases. The paper reports an anomaly detection technique based on an autoencoder and an extreme learning machine (AEELM). This approach uses an autoencoder for feature selection and an ELM algorithm for classification purposes. It is estimated using the Cauvery River dataset. The autoencoder-based ELM techniques outperformed other models with 83% accuracy, 77% precision, 83% recall, and 83% F1-level characteristic performance indicators. These results demonstrate its great effectiveness. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Water quality

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An improved method for classifying depth-based human actions using self-adaptive evolutionary technique

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 157-173页

作者： Pareek, Preksha Thakkar, Ankit Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

Automatic Human Action Recognition (HAR) using RGB-D (Red, Green, Blue, and Depth) videos captivated a lot of attention in the pattern classification field due to low-cost depth cameras. Feature extraction in action recognition is an important aspect. As compared to Depth Motion Maps (DMM), Depth Motion Maps–Local Binary Pattern (DMM–LBP) provides compact representation of features. After extracting features using DMM–LBP, Principal Component Analysis (PCA) is used for dimensionality reduction. For classification task, randomly generated input weights of Extreme Learning Machine (ELM) can lead to non-optimal results. Therefore, in this paper, we have used an improved learning algorithm named Self-adaptive Differential Evolution (SaDE) ELM for action classification. In the proposed approach, DMM–LBP is used for feature extraction and SaDE–ELM is used for action classification. To evaluate strength of the proposed approach, experiments are performed on four public datasets, namely, MSRAction3D, MSRDaily Activity3D, UTD–MHAD, and MSRGesture3D. The proposed approach gives better accuracy compared to existing approaches Kernel ELM (KELM), l2-Collaborative Representation Classifier (l2-CRC), and Probabilistic CRC (ProCRC) methods. We have also presented statistical significance of results in the paper. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Local binary pattern

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Correlation Attention Registration Based on Deep Learning from Histopathology to MRI of Prostate

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Critical Reviews in Biomedical engineering 2024年第2期52卷 39-50页

作者： Wang, Xue Song, Zhili Zhu, Jianlin Li, Zhixiang School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Deep learning offers a promising methodology for the registration of prostate cancer images from histopathology to MRI. We explored how to effectively leverage key information from images to achieve improved end-to-end registration. We developed an approach based on a correlation attention registration framework to register segmentation labels of histopathology onto MRI. The network was trained using paired prostate datasets of histopathology and MRI from the Cancer Imaging Archive. We introduced An L2-Pearson correlation layer to enhance feature matching. Furthermore, our model employed an enhanced attention regression network to distinguish between key and nonkey features. For data analysis, we used the Kolmogorov–Smirnov test and a one-sample t-test, with the statistical significance level for the one-sample t-test set at 0.001. Compared with two other models (ProsRegNet and CNNGeo), our model exhibited improved performance in Dice coefficient, with increases of 9.893% and 2.753%, respectively. The Hausdorff distance was reduced by approximately 50% and 50%, while the average label error (ALE) was reduced by 0.389% and 15.021%. The proposed improved multimodal prostate registration framework demonstrated high performance in statistical analysis. The results indicate that our enhanced strategy significantly improves registration performance and enables faster registration of histopathological images of patients undergoing radical prostatectomy to preoperative MRI. More accurate registration can prevent over-diagnosing low-risk cancers and frequent false positives due to observer differences. © 2023 by Begell House, Inc.

关键词： Magnetic resonance imaging

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Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

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computers, Materials & Continua 2024年第1期78卷 1095-1113页

作者： Fang Xu Songhao Jiang Yi Ma Manzoor Ahmed Zenggang Xiong Yuanlin Lyu School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Artificial Intelligence Industrial Technology Research Institute Hubei Engineering UniversityXiaogan432000China

Effective data communication is a crucial aspect of the Social Internet of Things(SIoT)and continues to be a significant research *** paper proposes a data forwarding algorithm based on Multidimensional Social Relations(MSRR)in SIoT to solve this *** proposed algorithm separates message forwarding into intra-and cross-community forwarding by analyzing interest traits and social connections among *** new metrics are defined:the intensity of node social relationships,node activity,and community *** the community,messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node *** a node performs cross-community forwarding,the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between *** proposed algorithm was compared to three existing routing algorithms in simulation *** indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.

关键词： SIoT data forwarding social attributes social relations community

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Exploring ensemble optimized voting and stacking classifiers through Cross-validation for early detection of suicidal ideation

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 335-349页

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad Department of Computer Science Engineering National Institute of Technology Patna Bihar Patna India

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for *** ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data - common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds"and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. © 2024 - IOS Press. All rights reserved.

关键词： Semantics

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Diabetic retinopathy detection by fundus images using fine tuned deep learning model

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Multimedia Tools and Applications 2024年第39期83卷 86657-86679页

作者： Singh, Satya Prakash Gupta, Pankaj Dung, Rakshita Department of Computer Science & amp Engineering Birla Institute of Technology Ranchi Mesra India

This study employs transfer learning using a fine-tuned pretrained EfficientNetB0 convolutional neural network (CNN) model to accurately detect the various stages of Diabetic Retinopathy. The training process involved utilizing three datasets: Messidor, IDRiD (Indian Diabetic Retinopathy Detection), and APTOS 2019 Blindness Detection, which collectively encompassed 5,379 fundus images. Different types of processed fundus images were fed into the model to determine the optimal pre-processing approach for stage detection in Diabetic Retinopathy. The model was assessed on the original dataset with some augmentation techniques applied. According to the training data, the model achieved a maximum accuracy of 72%. However, converting the dataset to grayscale yielded an improved accuracy of 80%. Similarly, extracting the green, red, and blue channels individually resulted in accuracies of 72%, 76%, and 73% respectively. Notably, when the green channel extracted images underwent histogram equalization, the model achieved its highest accuracy of 83%. Furthermore, the application of a Sobel filter to the red channel images led to a maximum accuracy of 51%. Finally, to determine the effectiveness of each processed image type, sensitivity and specificity measures were compared. Among all the variations, the green channel extracted images with histogram equalization demonstrated superior performance in correctly identifying the respective classes, outperforming the other approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural network models

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A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 556-565页

作者： Aralikatti, Rajath C Pawan, S.J. Rajan, Jeny National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal575025 India

Deep neural networks have played a vital role in developing automated methods for addressing medical image segmentation. However, their reliance on labeled data impedes the practicability. Semi-Supervised learning is gaining attention for its intrinsic ability to extract valuable information from labeled and unlabeled data with improved performance. Recently, consistency regularization methods have gained interest due to their efficient learning procedures. They are, however, confined to data or network-level perturbations, negating the benefit of having both forms in a single framework. In light of this, we ask an intriguing but unexplored question: Can we have both network-level and data-level perturbation in the semi-supervised framework? To this end, we present a holistic approach that integrates data-level perturbation in the model pre-training stage, followed by implicit network-level perturbation in the fine-tuning stage. Furthermore, we incorporate networks with manifold learning paradigms throughout the training to facilitate the formation of robust data representations by ensuring local and global semantic affinities adhering to the theory of consensus. Notably, this may be the first attempt in the semi-supervised medical image segmentation archetype to use data and network-level perturbation with a model pre-training strategy. We extensively validated the efficacy of the proposed framework on three benchmark datasets, namely the Automated Cardiac Diagnosis Challenge, ISIC-2018, and Left Atrial Segmentation Challenge datasets, subjected to severely low-sampled labeled data. Notably, in ACDC (4%), ISIC-2018 (5%), and LA (6%) labeled cases, the proposed method outperforms the second-best method by 2.95%, 1.31%, and 0.71% in the Dice Similarity Metric. © 2023 IEEE.

关键词： Feature extraction

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OptiFog: A Framework for Acquiring State Information and Predicting Resource Availability for Task Offloading in Cooperative Fog-Networks

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IEEE Transactions on Services Computing 2024年 1-13页

作者： Alam, Mehbub Ahmed, Nurzaman Ghosh, Shyamal Matam, Rakesh Barbhuiya, Ferdous Ahmed Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati India Department of Computer Science Dartmouth College Hanover USA School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram India

The primary objective of fog computing is to minimize the reliance of IoT devices on the cloud by leveraging the resources of fog network. Typically, IoT devices offload computation tasks to fog to meet different task requirements such as latency in task execution, computation costs, etc. So, selecting such a fog node that meets task requirements is a crucial challenge. To choose an optimal fog node, access to each node's resource availability information is essential. Existing approaches often assume state availability or depend on a subset of state information to design mechanisms tailored to different task requirements. In this paper, OptiFog: a cluster-based fog computing architecture for acquiring the state information followed by optimal fog node selection and task offloading mechanism is proposed. Additionally, a continuous time Markov chain based stochastic model for predicting the resource availability on fog nodes is proposed. This model prevents the need to frequently synchronize the resource availability status of fog nodes, and allows to maintain an updated state information. Extensive simulation results show that OptiFog lowers task execution latency considerably, and schedules almost all the tasks at the fog layer compared to the existing state-of-the-art. IEEE

关键词： computer architecture

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Research on lightweight pavement disease detection model based on YOLOv7

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10573-10589页

作者： Wang, Chishe Li, Jun Wang, Jie Zhao, Weikang School of Computer Science and Engineering Anhui University of Science and Technology Huainan China Jinling Institute of Technology Nanjing China

Rapid urbanization has made road construction and maintenance imperative, but detecting road diseases has been time-consuming with limited accuracy. To overcome these challenges, we propose an efficient YOLOv7 road disease detection model. Our approach involves integrating MobilieNetV3 as the backbone feature extraction network to reduce the network's parameters and computational requirements. Additionally, we introduce the BRA attention module into the spatial pyramid pooling module to eliminate redundant information and enhance the network's feature representation capability. Moreover, we utilize the F-ReLU activation function in the backbone network, expanding the convolutional layers' receptive field range. To optimize the model's boundary loss, we employ the Wise-IoU loss function, which places more emphasis on the quality of ordinary samples and enhances the overall performance and generalization ability of the network. Experimental results demonstrate that our improved detection algorithm achieves a higher recall rate and mean average precision (mAP) on the public dataset (RDD) and the NJdata dataset in Nanjing's urban area. Specifically, compared to YOLOv7, our model increases the recall rate and mAP on RDD by 3.3% and 2.6%, respectively. On the NJdata dataset, our model improves the recall rate and mAP by 1.9% and 1.3%, respectively. Furthermore, our model reduces parameter and computational requirements by 30% and 22.5%, respectively, striking a balance between detection accuracy and speed. In conclusion, our road disease detection model presents an effective solution to address the challenges associated with road disease detection in urban areas. It offers improved accuracy, efficiency, and generalization capabilities compared to existing models. © 2024 - IOS Press. All rights reserved.

关键词： Roads and streets

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A Systematic Review on Detection of Gastric Cancer in Endoscopic Imaging System in Artificial Intelligence Applications 5th

A Systematic Review on Detection of Gastric Cancer in Endosc...

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5th International Conference on Data and Information sciences, ICDIS 2023

作者： Pooja, K. Kishore Kanna, R. Department of Biomedical Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (纸本)9789819969050

Artificial intelligence and disease detection go hand in hand. Convolutional neural networks, a significant area of artificial intelligence applications, are crucial in the detection of stomach cancer. AI’s significance in cancer research and clinical applications is becoming increasingly recognized. Cancers like stomach and gastric cancer are ideal test subjects to determine whether early efforts to apply AI to medicine can result in beneficial outcomes. Deep learning (DL) and machine learning (ML) are two examples of AI-derived concepts. The definition of ML refers to the capability of learning data features without explicit programming. It seeks to improve the efficacy by computing methods. ML-based predictive prognostic models are becoming more prevalent in cancer research. The objective of this review paper focused on the role in the artificial intelligence improvements in treatment, prognosis, and diagnosis gastric cancer. Artificial neural networks and convolutional neural network have gained a lot of attention for biomedical applications. The main goal of the ML subset known as DL is to challenge multilayer intelligence networks. When it comes to the clinical management of stomach cancer, there is a lot more to be discussed. Despite rising efforts, it is important to adapt machine learning and artificial intelligence to enhance stomach cancer diagnostics. It can be used to enhance visual modalities for treatment and diagnosis procedures, even though that it can be slow and difficult. It might eventually turn into a helpful tool for doctors. Moreover, artificial intelligence changes ideas about the future of medicine as well as the effectiveness of diagnostic and treatment procedures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Convolutional neural networks

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