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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

CAAI Transactions on intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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Design of Human Adaptive Mechatronics Controller for Upper Limb Motion Intention Prediction

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computers, Materials & Continua 2022年第4期71卷 1171-1188页

作者： R.Joshua Samuel Raj J.Prince Antony Joel Salem Alelyani Mohammed Saleh Alsaqer C.Anand Deva Durai Department of Information Science&Engineering CMR Institute of TechnologyBengaluruIndia Department of Mechatronics Engineering Rajas Engineering CollegeTirunelveliIndia Center for Artificial Intelligence King Khalid UniversityAbhaKingdom of Saudi Arabia College of Computer Science King Khalid UniversityAbhaKingdom of Saudi Arabia

Human Adaptive Mechatronics(HAM)includes human and computer system in a closed *** person with disabilities,normally carry out their daily routines with some assistance to move their *** the short fall of human care takers,mechatronics devices are used with the likes of exoskeleton and exosuits to assist *** rehabilitation and occupational therapy equipments utilize the electromyography(EMG)signals to measure the muscle activity *** paper focuses on optimizing the HAM model in prediction of intended motion of upper limb with high accuracy and to increase the response time of the *** characteristics extraction from EMG signal and prediction of optimal controller parameters are *** and frequency based approach of EMG signal are considered for feature *** models used for estimating motion and muscle parameters from EMG signal for carrying out limb movement predictions are *** on the extracted features,optimal parameters are selected by Modified Lion Optimization(MLO)for controlling the HAM ***,supervised machine learning makes predictions at different points in time for individual sensing using Support Vector Neural Network(SVNN).This model is also evaluated based on optimal parameters of motion estimation and the accuracy level along with different optimization models for various upper limb *** proposed model of human adaptive controller predicts the limb movement by 96%accuracy.

关键词： Exoskeleton electromyography(emg) human adaptive mechatronics occupational therapy motion prediction machine learning

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Identity-preserving 3D Facial Completion under Skull Constraints 18

Identity-preserving 3D Facial Completion under Skull Constra...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Yu, Longtao Zhao, Junli Duan, Fuqing Lv, Chenlei Li, Dantong Pan, Zhenkuan Zhou, Mingquan Qingdao University College of Computer Science and Technology China Beijing Normal University School of Artificial Intelligence China Shenzhen University College of Computer Science and Software Engineering China Cardiff University School of Computer Science and Informations United Kingdom Northwest University School of Information Science and Technology United States Northwest University National and Local Joint Engineering Research Center for Cultural Heritage Digitization United States

ISBN: (纸本)9798350364132

3D face shape completion is a necessary pre-process for various facial applications as they are often mutilated due to the acquiring environment or occlusion. However, it is challenging to ensure identity consistency when completing faces with large missing regions. Therefore, we introduce craniofacial information to supervise the completion of face shapes. Firstly, a novel dual encoder-decoder structure for face depth image inpainting is constructed by combining dilated convolution and the coherent semantic attention mechanism, guaranteed to generate smooth results with complete semantic information even in the presence of large missing regions in the face model. Then, we innovatively design a craniofacial superimposition module to determine the probability that the inpainted face and corresponding skull come from the same person, constraining the inpainting network to learn identity consistency information. Finally, extensive experimental results show that our method can effectively complete 3D face shapes containing large arbitrary missing regions while guaranteeing identity consistency. © 2024 IEEE.

关键词： 3D modeling

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Multimodal Large Models are Effective Action Anticipators

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IEEE Transactions on Multimedia 2025年 27卷 2949-2960页

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le Xi'an University of Architecture and Technology College of Information and Control Engineering Xi'an710055 China Northwestern Polytechnical University School of Astronautics Xi'an710072 China Peking University School of Electronic and Computer Engineering Shenzhen Graduate School Shenzhen518055 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230022 China University of Science and Technology of China School of Information Science and Technology Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. © 1999-2012 IEEE.

关键词： Semantics

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Deep Learning-Based Approaches for Accurate Brain Tumor Detection in MRI Images

Deep Learning-Based Approaches for Accurate Brain Tumor Dete...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Noori, Mohammed Ahmed, Mohsin Ibrahim, Amer Saeed, Ali Khishe, Mohammad Mahfuri, Mahmoud Al-ma'Moon University College Baghdad Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates Northern Technical University Computer Center Ninevah Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara Research Center Jadara University Jordan International University for Science and Technology Computer and Information Technology Department Syria

ISBN: (纸本)9798350369106

Brain tumor classification is considered one of the major tasks in medical image analysis, where correct and timely diagnosis could be achieved to serve as the key to effective treatment. This research paper proposes a deep learning-based approach for automatic classification of brain tumors from MRI images by fine tuning the ResNet50V2 CNN model. The dataset is made up of four classes, namely: Glioma Tumor, Meningioma Tumor, No Tumor, and Pituitary Tumor, totaling 2,844 images. We have done some strategy data augmentation and class weighting in order to prevent class imbalance, which ensures good performance in all tumor classes. Pre-processing and augmentation of the input images are done via rotation, shifting, zooming, and flipping. It yielded 95.29% on the validation set, with the major highlights of performance metrics for the minority class being class precision equal to 0.97 and class recall equal to 0.92, hence proving the efficiency of performed class balancing. The range of F1-scores, from 0.93 to 0.97, means fantastic predictive capability across all tumor types. Other techniques were also used to make this model optimal, such as early stopping, learning rate reduction, and checkpointing. The high performance of this model shows great promise in helping clinicians toward the right diagnosis of brain tumors in an effective way. Future efforts will involve incorporating larger datasets, exploring advanced augmentation techniques to enhance further model generalizability. Model explainability tools such as Grad-CAM will be used to extract insight into the model's decision-making process. This enhances the clinical interpretability of the results. © 2024 IEEE.

关键词： Convolutional neural networks

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Coral-Shaped NiO@Graphene Composite Derived from Spent Zinc–Carbon Batteries: Geometric Structure and Electrochemical Studies of Supercapacitors

ACS Sustainable Resource Management

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ACS Sustainable Resource Management 2024年第2期1卷 201-212页

作者： Phuoc-Anh Le Phi Long Nguyen Thi Viet Bac Phung Center for Environmental Intelligence and College of Engineering & Computer Science VinUniversity Hanoi 100000 Vietnam Institute of Sustainability Science VNU Vietnam Japan University Vietnam National University Hanoi 100000 Vietnam

An easy, low-cost, and environmentally safe method for preparing graphene nanosheets (GNs) from carbon electrodes in waste zinc–carbon batteries is presented, which employs an electrochemical exfoliation process using table salt (also known as natural sea salt) as an electrolyte under 5 V from a recycled cell phone charger. The GNs are then creatively used as a framework for a two-dimensional nanostructure and conductive backbone, while nickel acetate tetrahydrate is used as a precursor for shrinking and bundling to create the coral flower structure of the graphene/NiO (GN/NiO) composite by a simple calcination process. The coral GN/NiO exhibits a beautiful nanoflower structure, which is clearly confirmed by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Moreover, the coral GN-NiO composite shows potential as a candidate for supercapacitor electrodes. For a three-electrode measurement cell in 3 M KOH electrolyte, the GN-NiO material exhibits a maximum specific capacitance of 140.7 F g–1, which is higher than that of GNs (97.5 F g–1) at the same current density of 0.5 A g–1. Additionally, density functional theory (DFT) is employed to investigate the structure and electronic properties of the coral flower structure of the graphene/NiO (GN/NiO) structure. Using DFT, the electronic properties of the structure, including the band structure, density of states, and charge transfer between the graphene and NiO, as well as the stability of the structure, are calculated.

关键词： Composites Electrical properties Electrodes Oxides Two dimensional materials

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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Adaptive dynamic surface control for quadrotor-slung load transportation system with uncertainties

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science China(Technological sciences) 2023年第10期66卷 2917-2929页

作者： DING Feng HUANG Jian SUN Chong AI Yong XU WenXia YANG ChenGuang School of Computer Science South-Central Minzu UniversityWuhan 430074China Key Laboratory of Ministryof EducationforImage Processing and Intelligent ControlHubei Key Laboratoryof Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Engineering Wuhan Institute of TechnologyWuhan 430205China Key Laboratory of Ministry of Education for Autonomous Systems and Networked Control Guangdong Engineering Technology Research Center of Control of Intelligent SystemsCollege of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Quadrotors play a significant role in our lives and are transforming our *** cable-suspended loads is an unavoidable quadrotor application trend and a hot research topic in the control ***,the load swing and unpredictability pose significant challenges to the quadrotor's *** this paper,an anti-swing controller with an inner-outer control strategy for the quadrotor-slung load transportation system is *** facilitate the controller design,the outer position dynamics are restructured in the form of ***,a virtual controller is created to force the underactuated states to the dynamic surface to ensure the position subsystem's *** improve robustness,an adaptive law is used to eliminate the effects of uncertain cable ***,a dynamic surface controller for the inner attitude subsystem is presented to drive the actual force to the virtual *** is demonstrated that the control strategy can stabilize the quadrotor despite mass and cable length *** results are provided to demonstrate the efficacy and durability of the proposed method.

关键词： quadrotor-slung load transportation system dynamic surface control adaptive law cascade system model uncertainties

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Efficient Resource Allocation Framework for Network Slicing-enabled Open RAN

Efficient Resource Allocation Framework for Network Slicing-...

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International Conference on Communications and Electronics, ICCE

作者： Tuan-Vu Truong Quang-Trung Luu Van-Dinh Nguyen College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798350379792

ISBN: (纸本)9798350379808

The transition to 5G technology has revolutionized cellular networks, leading to an unprecedented demand for high-bandwidth and low-latency services. To meet these evolving demands, next-generation mobile networks are turning to innovative solutions such as network slicing within the open radio access network (RAN) architecture. This paper focuses on addressing the challenges associated with dynamic virtual network function (VNF) mapping and resource allocation in terms of both communication and computing resources. The ultimate goal is to simultaneously maximize the total reward for admitted slices and minimize the associated cost by allocating resources to the Open RAN slices catering to diverse service types. To efficiently solve the formulated problem, we decompose it into two sub-problems tailored to different timescales which are executed at the different layers within the Open RAN architecture. To solve the non-convex long-term problem, we first transform it to a more tractable form and employ the successive convex approximation (SCA) method to achieve at least a locally optimal solution. Numerical simulations demonstrate the efficacy and quick convergence of the proposed algorithm, highlighting its potential to improve network slicing performance and support diverse applications in future wireless networks.

关键词： Costs Network slicing Wireless networks Open RAN computer architecture Transforms Numerical simulation

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