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FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17

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Scientific reports 2025年第1期15卷 17697页

作者： Rahul Singh Sheifali Gupta Ahmad Almogren Ateeq Ur Rehman Salil Bharany Ayman Altameem Jaeyoung Choi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh 11633 Saudi Arabia. Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India. Applied Science Research Center Applied Science Private University Amman Jordan. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India. salil.bharany@***. Department of Natural and Engineering Sciences College of Applied Studies and Community Services King Saud University Riyadh 11543 Saudi Arabia. School of Computing Gachon University Seongnam-si 13120 Republic of Korea. jychoi19@gachon.ac.kr.

Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging techniques, such as CT and MRI, rely on manual interpretation, which can be both time-intensive and subject to variability. This study introduces FasNet, an innovative hybrid deep learning model that combines ResNet-50 and VGG-16 architectures, incorporating Channel and Spatial Attention mechanisms alongside Monte Carlo Dropout to improve segmentation precision and reliability. FasNet leverages ResNet-50's robust feature extraction and VGG-16's detailed spatial feature capture to deliver superior liver tumor segmentation accuracy. Channel and spatial attention mechanisms could selectively focus on the most relevant features and spatial regions for suitable segmentation with good accuracy and reliability. Monte Carlo Dropout estimates uncertainty and adds robustness, which is critical for high-stakes medical applications. Tested on the LiTS17 dataset, FasNet achieved a Dice Coefficient of 0.8766 and a Jaccard Index of 0.8487, surpassing several state-of-the-art methods. The Channel and Spatial Attention mechanisms in FasNet enhance feature selection, focusing on the most relevant spatial and channel information, while Monte Carlo Dropout improves model robustness and uncertainty estimation. These results position FasNet as a powerful diagnostic tool, offering precise and automated liver tumor segmentation that aids in early detection and precise treatment, ultimately enhancing patient outcomes.

关键词： Attention mechanisms Deep learning FasNet Image segmentation Liver tumor Monte Carlo dropout ResNet50 VGG16

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Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

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computers, Materials & Continua 2022年第1期70卷 715-738页

作者： Shidrokh Goudarzi Seyed Ahmad Soleymani Mohammad Hossein Anisi Domenico Ciuonzo Nazri Kama Salwani Abdullah Mohammad Abdollahi Azgomi Zenon Chaczko Azri Azmi Centre for Artificial Intelligent(CAIT) Universiti Kebangsaan MalaysiaBangi43600Malaysia School of Computer Engineering Iran University of Science and TechnologyResalat Sq.16846-13114TehranIran School of Computing Faculty of Engineering Universiti Teknologi MalaysiaJohorMalaysia School of Computer Science and Electronic Engineering University of EssexColchester CO43SQUnited Kingdom Department of Electrical Engineering and Information Technologies University of Naples“Federico II”Naples80125Italy Razak Faculty of Technology and Informatics Universiti Teknologi MalaysiaKuala Lumpur54100Malaysia School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

The Wireless Sensor Network(WSN)is a promising technology that could be used to monitor rivers’water levels for early warning flood detection in the 5G ***,during a flood,sensor nodes may be washed up or become faulty,which seriously affects network *** address this issue,Unmanned Aerial Vehicles(UAVs)could be integrated with WSN as routers or data mules to provide reliable data collection and flood *** light of this,we propose a fault-tolerant multi-level framework comprised of a WSN and a UAV to monitor river *** framework is capable to provide seamless data collection by handling the disconnections caused by the failed nodes during a ***,an algorithm hybridized with Group Method Data Handling(GMDH)and Particle Swarm Optimization(PSO)is proposed to predict forthcoming floods in an intelligent collaborative *** proposed water-level prediction model is trained based on the real dataset obtained fromthe Selangor River *** performance of the work in comparison with other models has been also evaluated and numerical results based on different metrics such as coefficient of determination(R2),correlation coefficient(R),RootMean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),and BIAS are provided.

关键词： Unmanned aerial vehicles wireless sensor networks group method data handling particle swarm optimization river flow prediction

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AlphaEvolve: A learning framework to discover novel alphas in quantitative investment

arXiv

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arXiv 2021年

作者： Cui, Can Wang, Wei Zhang, Meihui Chen, Gang Luo, Zhaojing Ooi, Beng Chin School of Computing National University of Singapore School of Computer Science and Technology Beijing Institute of Technology College of Computer Science and Technology Zhejiang University

Alphas are stock prediction models capturing trading signals in a stock market. A set of effective alphas can generate weakly correlated high returns to diversify the risk. Existing alphas can be categorized into two classes: Formulaic alphas are simple algebraic expressions of scalar features, and thus can generalize well and be mined into a weakly correlated set. Machine learning alphas are data-driven models over vector and matrix features. They are more predictive than formulaic alphas, but are too complex to mine into a weakly correlated set. In this paper, we introduce a new class of alphas to model scalar, vector, and matrix features which possess the strengths of these two existing classes. The new alphas predict returns with high accuracy and can be mined into a weakly correlated set. In addition, we propose a novel alpha mining framework based on AutoML, called AlphaEvolve, to generate the new alphas. To this end, we first propose operators for generating the new alphas and selectively injecting relational domain knowledge to model the relations between stocks. We then accelerate the alpha mining by proposing a pruning technique for redundant alphas. Experiments show that AlphaEvolve can evolve initial alphas into the new alphas with high returns and weak correlations. Copyright © 2021, The Authors. All rights reserved.

关键词： Domain Knowledge

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Various Deep Learning Methods for Hyperspectral Images

Various Deep Learning Methods for Hyperspectral Images

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2020 International Conference on computing and Information technology, ICCIT 2020

作者： Sivaram, M. Shobana, S. Jeya Khan, Mujeebudheen Ramakrishnan, Jayabrabu Goel, Pallavi Murghai Maseleno, Andino Lebanese French University Department of Computer Networking Erbil Kurdistan Region Iraq College of Science Knowledge University Department of Computer Science Erbil Kurdistan Region Iraq Ibra College of Technology Department of Electrical and Electronics Oman Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Galgotias University School of Computing Science and Engineering Greater Noida India Universiti Tenaga Nasional Institute of Informatics and Computing Energy Department of Business Administration and Information System Malaysia

ISBN: (纸本)9781728126807

Hyperspectral imagery is widely used in remote sensing applications that take into account thousands of spectral channel compositions over a single scene. Hyperspectral imagery requires accurate models of learning to extract the hyperspectral features in an image. Due to the presence of its spatial and spectral resolution, the image learning model presents a core challenge due to its complicated nature of image frames. In order to assist it during the learning process, several attempts have been made to address its complicated nature. However, these methods failed to provide the hyperspectral imagery with a deeper understanding. Because of the presence of mixed pixels, limited training samples and redundant data, the utilization of deep learning techniques addresses the problems. The deep learning process addresses the complex image data relationship. In this paper, various deep learning methods are studied which are used for the learning of hyperspectral imagery. Initially, we present an overview on various deep learning methods for various image processing techniques. A system review is then carried out on various hyperspectral image learning models based on deep learning. © 2020 IEEE.

关键词： Remote sensing

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Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

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Data and Information Management 2021年第4期5卷 363-371页

作者： Wang, Yongzhen Liu, Xiaozhong Ju, Yingnan Börner, Katy Lin, Jun Sun, Changlong Si, Luo WISE Lab Institute of Science of Science and S&T Management Faculty of Humanities and Social Sciences Dalian University of Technology Dalian China Computer Science Department Worcester Polytechnic Institute WorcesterMA United States School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Alibaba DAMO Academy Hangzhou China

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been an explosive growth in e-commerce sales during Valentine's Day over the recent years. This exploratory study investigates the extent to which each product category and each shopper group can exhibit romantic love within China's e-market throughout the 2 weeks leading up to 2019 Valentine's Day. Massive data from Alibaba, the biggest e-commerce retailer worldwide, are utilized to formulate an innovative romance index (RI) to quantitatively measure e-romantic values for products and shoppers. On this basis, millions of shoppers, along with their millions of products purchased around Valentine's Day, are analyzed as a case study to demonstrate their love consumption and romantic gift-giving. The results of the analysis are then illustrated to help understand Chinese e-romance based on the perspectives of different product categories and shopper groups. This empirical information visualization also contributes to improving the segmentation, targeting, and positioning of China's e-market for Valentine's Day. © 2021 Yongzhen Wang et al., published by Sciendo

关键词： Sales

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C1-Conforming Quadrilateral Spectral Element Method for Fourth-Order Equations

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Communications on Applied Mathematics and Computation 2019年第3期1卷 403-434页

作者： Huiyuan Li Weikun Shan Zhimin Zhang State Key Laboratory of Computer Science/Laboratory of Parallel Computing Institute of SoftwareChinese Academy of SciencesBeijing 100190China School of Science Henan University of TechnologyZhengzhou 450001HenanChina Beijing Computational Science Research Center Beijing 100193China Department of Mathematics Wayne State UniversityDetroitMI 48202USA

This paper is devoted to Professor Benyu Guo's open question on the C1-conforming quadrilateral spectral element method for fourth-order equations which has been endeavored for years. Starting with generalized Jacobi polynomials on the reference square, we construct the C1-conforming basis functions using the bilinear mapping from the reference square onto each quadrilateral element which fall into three categories-interior modes, edge modes, and vertex modes. In contrast to the triangular element, compulsively compensatory requirements on the global C1-continuity should be imposed for edge and vertex mode basis functions such that their normal derivatives on each common edge are reduced from rational functions to polynomials, which depend on only parameters of the common edge. It is amazing that the C1-conforming basis functions on each quadrilateral element contain polynomials in primitive variables, the completeness is then guaranteed and further confirmed by the numerical results on the Petrov-Galerkin spectral method for the non-homogeneous boundary value problem of fourth-order equations on an arbitrary quadrilateral. Finally, a C1-conforming quadrilateral spectral element method is proposed for the biharmonic eigenvalue problem, and numerical experiments demonstrate the effectiveness and efficiency of our spectral element method.

关键词： Quadrilateral spectral element method Fourth-order equations Mapped polynomials C1-conforming basis Polynomial inclusion Completeness

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： Optimization algorithms

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Secure Goods Storage and Anti-Theft Approach using Ethereum Blockchain

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Procedia computer science 2024年 233卷 1-11页

作者： Likhitha Amasala Mahesh Ponnuru Srideviponmalar P School of Computer Science and Engineering Vellore Institute of Technology Amaravathi 522237 India School of Computing SRM Institute of Science and Technology Potheri 603203 India Department of Computational Intelligence School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Potheri 603203 India

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors.

关键词： Ethereum Blockchain Secure Goods Storage Anti-theft Interplanetary File System Smart Contracts Ownership verification

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Multi Head Graph Attention for Drug Response Predicton

Multi Head Graph Attention for Drug Response Predicton

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Smart Data Intelligence (ICSMDI), International Conference on

作者： P. Selvi Rajendran M. Sivannarayna Computer Science and Engineering School of Computing Hindustan Institute of Technology and Science Chennai

Precision medicine is based on curing diseases based on a patient's genetic profile, lifestyle, and environmental factors. This method improves clinical trial success rates and speed up drug regulatory approval. Predicting tumour vulnerability to specific anti-cancer therapy is critical for the successful implementation of precision medicine. Drug combinations have been shown to be very effective in cancer treatment to lower the drug resistance and improve the therapeutic effectiveness. The experiments carried out in all these therapeutic combinations have become expensive and time-consuming as a result of the increasing number of anti-cancer drugs. Large-scale drug response testing on cancer cell lines might help to understand the way drugs react with cancer cells. This study proposes a multi head graph attention network to perform drug response prediction.

关键词： Drugs Head Precision medicine Predictive models Data models Testing Immune system

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Reliable neighbors-based collaborative filtering for recommendation systems 1

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1st International Conference on Neural computing for Advanced Applications, NCAA 2020

作者： Zhang, Li Sun, Xiaohan Cheng, Jiangfei Li, Zepeng School of Computer Science and Technology Joint International Research Laboratory of Machine Learning and Neuromorphic Computing Soochow University SuzhouJiangsu215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University SuzhouJiangsu215006 China

ISBN: (数字)9789811576706

ISBN: (纸本)9789811576690

The sparsity of data and the diversity of ratings have a great effect on the performance of recommendation systems. To deal with these two issues, this paper proposes a reliable neighbors-based collaborative filtering (RNCF) method for recommendation systems. RNCF first uses the item similarity to pre-estimated missing ratings to solve the issue of data sparsity, which enriches the sample data and reduces the impact of extremely sparse ratings on the recommendation effect. Then, RNCF finds a set of reliable users for each item by defining a reliable degree for each item. Next, RNCF adopts the K-means clustering algorithm to find the similar users for a target user. The set of reliable neighbors is the intersection of sets of reliable users and similar users. Finally, RNCF provides a predicted rating of a target user on some item using the pre-estimated ratings of corresponding reliable neighbors. Experimental analysis shows that RNCF can improve the performance at the extreme levels of sparsity and rating diversity in recommendation systems. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Collaborative filtering

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