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PODBoost: an explainable AI model for polycystic ovarian syndrome detection using grey wolf-based feature selection approach

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Neural Computing and Applications 2024年第30期36卷 18627-18644页

作者： Moral, Poonam Mustafi, Debjani Sahana, Sudip Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi835215 India

Polycystic Ovary Syndrome (PCOS) is a recurring endocrine disorder that primarily affects women of reproductive age. It is difficult to diagnose due to its heterogeneous characteristics and overlapping symptoms with other illnesses. As a result, accurate and trustworthy prediction models are required to detect PCOS early. This research work aims to develop ML methods that predict the risk of PCOS among women based on demographic and clinical features. The entire framework is divided into four phases: in Phase I of the study, the SMOTE-Tomek Links (SMTL) technique balances the data set by combining oversampling and undersampling approaches. A novel meta-heuristic-based feature selection approach, the Grey Wolf Optimization (GWO) method, has been employed to select the most crucial features from the dataset, explained in Phase II. Subsequently, in Phase III, a hybridized classifier PODBoost algorithm (Polycystic Ovarian Disorder Boosting algorithm) is devised for faithful early prediction of PCOS using the concepts of different classical supervised learning algorithms. Finally, Explainable AI (XAI) such as the Local Interpretable Model-Agnostic Explanations (LIME) tool has been implemented to interpret relevant predictions made by the proposed classifier. The proposed algorithm is examined utilizing numerous metrics such as Accuracy, Error-Rate, ROC-AUC Score, Recall, Precision, and F1-Score. Among all the evaluated models, the proposed hybridized model has shown an impressive performance with an exceptional accuracy of 97.42%, indicating its superiority by delivering outstanding results. Based on the findings, the novel meta-heuristic-based feature selection method significantly impacts the outcomes of the proposed hybridized PODBoost algorithm. This algorithm may be recommended to predict PCOS or other relevant diseases having datasets which are multimodal in nature. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 202

关键词： Feature Selection

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Pre-Routing Timing Prediction Across Different Technology Nodes

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2024年第7期44卷 2697-2710页

作者： Zhang, Xinyun Zhu, Binwu Liu, Fangzhou Jiang, Jiaxi Wang, Ziyi Xu, Peng Xu, Hong Yu, Bei The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong

In the domain of Very-Large-Scale Integration (VLSI) design, the accuracy of pre-routing timing prediction is of paramount importance for ensuring the performance and reliability of integrated circuits. Traditional methods based on machine learning necessitate the availability of extensive and high-quality datasets. However, this requirement poses significant challenges for advanced technology nodes due to the laborious and time-intensive nature of data preparation. To address this critical issue, we introduce a novel transfer learning framework that leverages data from preceding technology nodes to facilitate learning and prediction on the target node. Our methodology commences with the disentanglement and alignment of timing path features across different nodes, ensuring the preservation and effective translation of intrinsic timing path properties. Subsequently, we employ a Bayesian-based model to predict the arrival times of individual timing paths. This model is particularly adept at managing the high variability inherent in arrival times and exhibits strong generalization capabilities to novel design scenarios. Moreover, we propose a new algorithm to reweight the preceding node data during training by estimating their transferability through the cell type distribution. We validate the efficacy of our proposed framework through comprehensive experimental evaluations, demonstrating successful transfer learning from 130nm or 45nm to 7nm technology nodes. The results underscore the potential of our approach to significantly mitigate the dependency on extensive data preparation while maintaining high accuracy in timing prediction for cutting-edge VLSI designs. © 1982-2012 IEEE.

关键词： Prediction models

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Text Analysis Using Morphological Operations on a Neutrosophic Text Hypergraph

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Neutrosophic Sets and Systems 2023年 61卷 337-364页

作者： Dhanya, P.M. Ramkumar, P.B. Department of Computer Science and Engineering RSET India Department of Mathematics RSET India

Due to the rise in the operation of platforms on social media, there is more opportunity for users to post content online, out of which some tend to be hate speech. Hate speech is found in almost all domains like sports, politics, religion, government affairs, and personal matters. Its detection and removal from platforms like Twitter, Facebook, etc. are tedious. Over the years, a lot of methods have evolved in this area most of which are more time-consuming machine learning methods. Our objective is to find a better method that considers indeterminacy at the word level and sentence level for the detection and removal of hate speech using fuzzy logic applied to Neutrosophic hypergraphs. A neutrosophic hypergraph is a kind of hypergraph where each node and hyperedge has three associated membership functions namely Indeterminacy, Truth and Falsity. Our work has successfully modeled Text documents into neutrosophic hypergraphs and morphological operators like dilation, erosion etc. are applied to it. Using these operations further operators like thinning, thickening, hit-or-miss, and skeletoning are applied. Finally hate speech is identified and removed. This a novel method in this area. The system is tested with Twitter tweets and the results are promising with an accuracy of 88%. © (2023), (University of New Mexico). All Rights Reserved.

关键词： Morphology

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital Communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Q-Cogni: An Integrated Causal Reinforcement Learning Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6186-6195页

作者： Da Costa Cunha, Cristiano Liu, Wei French, Tim Mian, Ajmal University of Western Australia Department of Computer Science and Software Engineering PerthWA6009 Australia

We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning to improve the learning process with causal inference. Q-Cogni achieves improved policy quality and learning efficiency with a prelearned structural causal model of the environment, queried to guide the policy learning process with an understanding of cause-and-effect relationships in a state-action space. By doing so, we not only leverage the sample efficient techniques of reinforcement learning but also enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply Q-Cogni on vehicle routing problem (VRP) environments including a real-world dataset of taxis in New York City using the Taxi and Limousine Commission trip record data. We show Q-Cogni's capability to achieve an optimally guaranteed policy (total trip distance) in 76% of the cases when comparing to shortest-path-search methods and outperforming (shorter distances) state-of-the-art reinforcement learning algorithms in 66% of cases. Additionally, since Q-Cogni does not require a complete global map, we show that it can start efficiently routing with partial information and improve as more data is collected, such as traffic disruptions and changes in destination, making it ideal for deployment in real-world dynamic settings. © 2020 IEEE.

关键词： Adversarial machine learning

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Explainable Anomaly Detection Using Vision Transformer Based SVDD

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computers, Materials & Continua 2023年第3期74卷 6573-6586页

作者： Ji-Won Baek Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si 16227Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si 16227Korea

Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic *** is possible to offer the explainable basis of decision-making for inference *** the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease *** addition,the technique makes it possible to accurately predict disease risk for anomaly *** transformer for anomaly detection from image data makes classification through ***,in MLP,a vector value depends on patch sequence information,and thus a weight *** should solve the problem that there is a difference in the result value according to the change in the *** addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the ***,there is a need for an explainablemethod for the part where the disease *** solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision *** order to draw the explainability of model results,it visualizes normal parts through *** health data,both medical staff and patients are able to identify abnormal parts *** addition,it is possible to improve the reliability of models and medical *** performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply *** Results The results of classification by applying the proposed SVDD are evaluated ***,through the proposed meth

关键词： Explainable AI anomaly detection vision transformer SVDD health care deep learning classification

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Enhancing MRI-Based Brain Tumor Classification Accuracy through EfficientNetB3 and Advanced Image Data Augmentation Techniques 2

Enhancing MRI-Based Brain Tumor Classification Accuracy thro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Vanitha, K. Mahesh, R.T. Aafiya Thahaseen, A. Manivannan, B. Anitha, K. Thakur, Arastu Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Department of Computer Science and Engineering Karnataka Bangalore India Al-Ameen Engineering College Department of Computer Science and Engineering Erode India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering India CSI College of Engineering Department of Information Technology Tamilnadu Ooty India

ISBN: (纸本)9798331534950

This work analyzes the possibilities of the EfficientNetB3 architecture, reinforced by modern image data augmentation methods, in the classification of brain cancers from MRI scans. Our key objective was to greatly boost the accuracy and reliability of brain tumor diagnosis by combining the advantages of a pre-trained EfficientNetB3 model, along with a custom-designed data augmentation strategy geared at effectively resolving the inherent variabilities in MRI images. To achieve this, comprehensive preprocessing protocols were applied to the MRI datasets designated for training, validation, and testing. These protocols included the resizing and normalization of images to ensure uniformity and compatibility with the neural network. Further, our approach utilized real-time image augmentation techniques such as rotations, shifts, and flips during the training process, thereby enhancing the model's exposure to a wide range of imaging scenarios and increasing its robustness against variations not present in the training set. The outcome of this research was a notable enhancement in the model's classification accuracy, achieving up to 99% accuracy in identifying various types of brain tumors. These results not only demonstrate the effectiveness of integrating sophisticated convolutional network architectures with extensive image augmentation in medical diagnostics but also suggest potential pathways for setting new standards in the accuracy and reliability of medical image analysis, particularly in the critical domain of brain tumor diagnosis. © 2024 IEEE.

关键词： Deep learning

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Tomato plant leaf diseases detection and classification using an advanced self-developed CNN model

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Multimedia Tools and Applications 2024年第36期83卷 83837-83866页

作者： Samal, Sumitra Verma, Vijayant Department of Computer Science Engineering MATS University Aarang Campus Raipur Chhattisgarh492004 India

Better disease detection and classification for tomato leaves at early sage provide finest productivity results. It is a natural occurrence for tomato plants to get sick, and if the appropriate attention and corrective action are not given in a timely manner. Moreover, it negatively impacts the productivity, quality, and quantity of the corresponding product. It is crucial to check the health and look for disease in tomato crop leaves since, if handled, these issues can affect the plant, leading to significant losses at marketing fields. Thus this article proposed an advanced Self-developed Convolution Neural Network (CNN) with fewer layers than the other standard model and its accuracy was tested by varying different parameters. Consequently, results shows the highest accuracy obtained at the learning rate of 0.0003, 20 epochs, and on using the three different optimizer. All the experiments are performed on 4 classes such as yellow curl diseases, early blight, bacterial spot and healthy leaf of the plant village public dataset the highest accuracy of 99.67% achieved on 12 layers CNN model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fruits

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Novel Bidirectional On-Board Charger for G2V and V2X Applications on Wide-Range Batteries

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2024年第2期12卷 2292-2305页

作者： Lin, Chun-Wei Chiu, Huang-Jen Tzeng, Ming-Shiang Yeh, Jia-Wei Huang, Chun-Hao National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10673 Taiwan

This article aims to study a two-stage converter for an 11-kW bidirectional on-board charger (OBC) with grid-to-vehicle (G2V) and V2X applications on wide-range batteries. In the first stage, an interleaved bridgeless totem-pole power factor correction (PFC) architecture is employed. A novel and simple dynamic phase adjustment method on PFC is proposed to realize the bidirectional function of G2V and V2X in single-phase ac conditions. Based on the proposed control strategy, good dynamic and steady-state performance can be achieved under various load conditions. In addition, a three-phase PFC with space vector pulsewidth modulation (SVPWM) control is applied to a full-bridge topology in three-phase ac conditions to achieve lower iTHD and higher voltage utilization ratio. A bidirectional CLLC resonant converter operating at 180 kHz is selected for the second stage, and pulse frequency modulation (PFM), delay time control, and phase shift modulation control are implemented to adjust a wide output voltage range effectively and improve performance under light-load conditions. © 2013 IEEE.

关键词： Poles

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Panoptic Region Slicing Segmentation and Optimized Alexnet-Based CNN for Early Melanoma Diagnosis 1

Panoptic Region Slicing Segmentation and Optimized Alexnet-B...

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1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Pareddy, Sudha V. Virupakshappa PDA College of Engineering Department of Computer Science & Engineering Kalaburagi India Sharnbasva University Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9798350371802

The early and accurate diagnosis of melanoma, a potentially harmful skin cancer, is essential in enhancing the survival of patients. This paper describes a new method for detecting melanoma called Panoptic Region Slicing Segmentation (PRS2), which employs an AlexNet-based optimized convolution neural network (PRS2-OCNN). The proposed system integrates several advanced techniques to improve the efficiency and accuracy of melanoma detection. The first step is to preprocess the dermoscopic image with a 2D Fusion Filter to improve its quality and prepare it for further analysis. The Panoptic Region Slicing Segmentation (PRS2) method is then used to emphasize the boundary regions, allowing for more precise localization of melanoma-affected areas. We use Threshold Histogram Evaluation (THE) to assess color variations within segmented regions, which effectively characterizes melanoma-specific color patterns. The features were extracted using Spread Spectral Menzies' Feature Selection (SSMFS), which reduced dimensionality and increased the efficiency of the subsequent analysis. The optimized convolution neural network, derived from the influential AlexNet architecture, serves as the foundation of our proposed approach. By fine-tuning the AlexNet-OCNN on the reduced feature set, we can improve its ability to accurately classify melanoma lesions based on their risk level. The PRS2-OCNN categorizes melanoma classes based on their risk severity, allowing dermatologists to make informed decisions about timely and appropriate treatment. A broad and diversified dataset of dermoscopic images was used in the experimental evaluations. By thoroughly analyzing the melanoma-affected regions, the proposed system performed better than existing methods in terms of detection accuracy. © 2024 IEEE.

关键词： Diagnosis

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