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Medical image fusion method by deep learning

International Journal of Cognitive Computing in engineering 2021年 2卷 21-29页

作者： Li, Yi Zhao, Junli Lv, Zhihan Li, Jinhua College of Data Science Software Engineering Qingdao University Shandong Qingdao 266071 China Business School Qingdao University Shandong Qingdao 266071 China

Deep learning technology has been extensively explored in pattern recognition and image processing areas. A multi-mode medical image fusion with deep learning will be proposed, according to the characters of multi-modal medical image, medical diagnostic technology and practical implementation, according to the practical needs for medical diagnosis. It cannot be only made up for the deficiencies of MRI, CT and SPECT image fusion, but also can be implemented in different types of multi-modal medical image fusion problems in batch processing mode, and can be effectively overcome the limitation of only one-page processing. The proposed method can greatly improve the fusion effect, image detail clarity and time efficiency. The experiments on multi-modal medical images are implemented to analyze performance, algorithm stability and so on. The experimental results prove the superiority of our proposed method in terms of visual quality and a variety of quantitative evaluation criteria. © 2021

关键词： Deep learning Image fusion Multi-modal medical image

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Pioneering Minimalist Speech Analysis Through Optimized Spectral Features Machine Learning Models

Pioneering Minimalist Speech Analysis Through Optimized Spec...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： P A Harsha Vardhini S Shiva Prasad M H S Vishnu Sai Ch. Santoshi Dhanush Konduru Department of ECE Vignan Institute of Technology and Science Deshmukhi Telangana India Department of CSE (Data Science) Malla Reddy Engineering College Telangana India Department of CSE Matrusri Engineering College Telangana India Department of CSE Integrated Software Engineering VIT Vellore Tamilnadu India

In the contemporary landscape, speech processing stands as a cornerstone across diverse applications, with gender identification emerging as a pivotal task in numerous domains, including law enforcement for swift resolution of criminal cases. The discernment of gender from speech recordings, especially in languages such as Telugu, holds paramount importance given its widespread usage and cultural significance in South India. This paper addresses the imperative need for robust gender identification methodologies tailored specifically for Telugu speech. Leveraging the rich heritage of Telugu as one of the most historically significant and widely spoken Dravidian languages, our study delves into the application of sophisticated statistical models, namely Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM), to unravel the gender nuances embedded within Telugu speech patterns. To facilitate this identification task, we meticulously curated a bespoke dataset, drawing from diverse sources to ensure representativeness and reliability. Our methodology encompasses an array of advanced feature extraction techniques, including Mel Frequency Cepstral Coefficient (MFCC), augmented with first and second-order differentials, and Zero Crossing Rate (ZCR). Moreover, to streamline the analysis and optimize accuracy, we employ Principal Component Analysis (PCA) for feature reduction, thus enhancing computational efficiency without compromising on discernment accuracy. The efficacy of our approach is substantiated through rigorous evaluation and comparison analyses, wherein we meticulously assess the performance of both original and reduced feature sets. Our findings underscore the effectiveness of our methodology in achieving notable accuracy levels, thereby underscoring its potential for real-world applications in gender identification from Telugu speech data.

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ShiftwiseConv: Small Convolutional Kernel with Large Kernel Effect

arXiv

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arXiv 2024年

作者： Li, Dachong Li, Li Chen, Zhuangzhuang Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Mathematical Sciences Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology China

Large kernels make standard convolutional neural networks (CNNs) great again over transformer architectures in various vision tasks. Nonetheless, recent studies meticulously designed around increasing kernel size have shown diminishing returns or stagnation in performance. Thus, the hidden factors of large kernel convolution that affect model performance remain unexplored. In this paper, we reveal that the key hidden factors of large kernels can be summarized as two separate components: extracting features at a certain granularity and fusing features by multiple pathways. To this end, we leverage the multi-path long-distance sparse dependency relationship to enhance feature utilization via the proposed Shiftwise (SW) convolution operator with a pure CNN architecture. In a wide range of vision tasks such as classification, segmentation, and detection, SW surpasses state-of-the-art transformers and CNN architectures, including SLaK and UniRepLKNet. More importantly, our experiments demonstrate that 3 × 3 convolutions can replace large convolutions in existing large kernel CNNs to achieve comparable effects, which may inspire follow-up works. Code and all the models at https://***/lidc54/shift-wiseConv. © 2024, CC BY-NC-SA.

关键词： Convolution

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 24

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xiaoqiang Wu Qingling Zhu Qiuzhen Lin Weineng Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400704864

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method.

关键词： evolutionary algorithm evolutionary reinforcement learning reinforcement learning

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A NovelMethod for Determining Tourism Carrying Capacity in a Decision-Making Context Using q−Rung Orthopair Fuzzy Hypersoft Environment

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Computer Modeling in engineering & sciences 2024年第2期138卷 1951-1979页

作者： Salma Khan Muhammad Gulistan NasreenKausar Seifedine Kadry Jungeun Kim Department of Mathematics and Statistics Hazara UniversityMansehra21120Pakistan Department of Mathematics Faculty of Arts and SciencesYildiz Technical UniversityEsenlerIstanbul34210Turkey Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityP.O.Box 13-5053ByblosLebanon Department of Software and CMPSI Konju National UniversityCheonan31080Korea

Tourism is a popular activity that allows individuals to escape their daily routines and explore new destinations for various reasons,including leisure,pleasure,or business.A recent study has proposed a unique mathematical concept called a q−Rung orthopair fuzzy hypersoft set(q−ROFHS)to enhance the formal representation of human thought processes and evaluate tourism carrying *** approach can capture the imprecision and ambiguity often present in human *** the advanced mathematical tools in this field,the study has also incorporated the Einstein aggregation operator and score function into the q−ROFHS values to supportmultiattribute decision-making *** implementing this technique,effective plans can be developed for social and economic development while avoiding detrimental effects such as overcrowding or environmental damage caused by tourism.A case study of selected tourism carrying capacity will demonstrate the proposed methodology.

关键词： q−Rung orthopair fuzzy hypersoft set decision-making tourism carrying capacity aggregation operator

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Spatial-Temporal Motion Compensation for Learned Video Compression

Spatial-Temporal Motion Compensation for Learned Video Compr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qian Huang Wenting Liu Hao Lu Yiming Wang College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the past few years, learned video compression has received increasing attention. However, most current learned methods rely on the bilinear warping operations in motion compensation, which is equivalent to a low-pass filtering operation and brings the distortion of reconstruction. To address this problem, we propose a spatial-temporal motion compensation for learned video compression (STMC-LVC). STMC-LVC uses a spatial-temporal motion compensation network (STMC-Net) to fully consider the spatial-temporal correlation between successive frames, performing accurate motion compensation. Specifically, STMC-Net mainly consists of the initial feature prediction module and the fusion module. The initial feature prediction module predicts reference features based on DCN to provide sufficient information for motion compensation. The fusion module obtains temporal attention information by calculating the similarity between features, performs spatial attention operations, and finally performs spatial-temporal fusion, further improving the robustness of motion compensation. In addition, STMC-LVC uses a conditional coding framework. We use a concatenation of the current feature and predicted feature as context to explore spatial-temporal correlations in feature space. Experimental results show that our method effectively improves video compression. Specifically, our model achieves an average of 33.86% and 58.70% bitrate savings than x265 (veryslow) on PSNR and MS-SSIM, respectively.

关键词： Correlation Codecs Filtering Video compression Distortion Motion compensation Robustness Encoding Complexity theory Cybernetics

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DBSSL: A Scheme to Detect Backdoor Attacks in Self-Supervised Learning Models

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Huang, Yuxian Yang, Geng Yuan, Dong Yu, Shui Nanjing University of Posts and Telecommunication College of Computer Science and Software China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China University of Sydney School of Electrical and Information Engineering Australia University of Technology Sydney School of Computer Science Australia

Recently, self-supervised learning has garnered significant attention for its ability to extract high-quality features from unlabeled data. However, existing research indicates that backdoor attacks can pose significant threats to self-supervised learning. Additionally, due to the substantial training overhead, traditional backdoor detection methods in supervised learning, such as meta-learning, are not well-suited for self-supervised learning. Current backdoor detection methods for self-supervised learning can only defend against backdoor attacks triggered by patch. To address this challenge, we proposes DBSSL, a scheme that can efficiently detect backdoor attacks in self-supervised learning models. In DBSSL, we connect backdoor attacks with adversarial attacks and use adversarial perturbations for detection. Specifically, we first conduct targeted adversarial attacks on the samples, classifying them into different classes. Then, we employ the Z-Score method to analyze these adversarial perturbations. If outliers are present, we can infer that the self-supervised learning model has been backdoored. Unlike previous works, our scheme is better suited for downstream users with constrained computation resources and exhibits excellent detection capabilities for backdoor attacks caused by patch -type or global triggers. Abundant theoretical analysis has demonstrated the feasibility of our scheme. Moreover, extensive experiments show that our method performs well in detecting prevalent backdoor attacks in self-supervised learning, achieving detection accuracy exceeding 95%. © 2004-2012 IEEE.

关键词： Self-supervised learning

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Statistical data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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Computer Systems science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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