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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Facial Expression Recognition with DToF Sensing

Facial Expression Recognition with DToF Sensing

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengxiao Li Xie Zhang Chenshu Wu Department of Computer Science School of Computing and Data Science The University of Hong Kong HK SAR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Facial Expression Recognition (FER) is crucial for understanding human emotions, with applications spanning from mental health assessment to marketing recommendation systems. However, existing camera-based methods raise privacy concerns, while RF-based approaches suffer from limited environmental generalizability and high cost. In this work, we propose ToFace, a FER system leveraging a low-cost (4.8$) Direct Time-of-Flight (DToF) sensor that has been available on commodity smartphones. This sensor provides an extremely low-resolution 8 × 8 depth map and a clear Field of View (FoV), significantly mitigating privacy concerns while avoiding the impact of ambient objects. Despite the benefits, the low-resolution depth map introduces significant challenges for precise expression recognition due to limited facial structure information. We first develop a physical model to extract additional spatial information from the intermediate sensor output, i.e., the transient histograms. We then propose a physics-integrated neural network to reconstruct a facial structure map comprising both depth and orientation for accurate expression recognition. We conduct real-world experiments with 12 users and compare our model with several baselines. The results demonstrate that ToFace achieves the highest recognition accuracy of 75%.

关键词： Privacy Accuracy Face recognition Speech recognition Signal processing Robustness Sensors Transient analysis Speech processing Smart phones

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Hybrid genetic model with ANOVA for predicting breast neoplasm using METABRIC gene data

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Materials Today: Proceedings 2022年 56卷 1847-1852页

作者： Thakur, Bharti Gupta, Gaurav Kumar, Nagesh Yogananda School of AI Computing and Data Science Shoolini University Himachal Pradesh Solan India Chitkara School of Engineering & Technology Chitkara University Himachal Pradesh India

In 2020, 2.3 million females identified with breast cancer, and it is the second most frequent cancer among females. As a result, there is severe requirement of an accurate procedure to recognize breast cancer. In accordance with breast cancer organization the breast cancer is divided into two types one is invasive and another one is non-invasive. In Indian cities like Delhi, Kolkata and Mumbai have the highest death rate for female due to breast cancer. Unusual alteration in genes is the prime reason for breast cancer. Our paper contains metabric dataset related to 1905 patients along with 31 clinical characteristics and 331 genes. The novelty of this paper is to find out genes which are highly responsible for death from breast cancer with foremost clinical features and implementation of a hybrid method which is a combination of genetic algorithm and artificial neural network to get the best accuracy with the help of machine learning as this can preserve life of females. For attribute scaling standard scaler is used which range data in between 0 and 1. Attribute selection is performed with the help of ANOVA which is a statistical appliance. With this tool 6 leading clinical attributes are examined. In our research 12 genes gsk3b, kmt2c, map4, tsc2, tnk2, pdgfb, ncoa3, akt1, afdn, sdc4,map2k2 and tubb4b which are extremely responsible for death in our dataset are recognized and our hybrid method implementation gives the perfection of 90.57%. © 2021

关键词： Analysis of variance (ANOVA)

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λGrapher: A Resource-Efficient Serverless System for GNN Serving through Graph Sharing 24

λGrapher: A Resource-Efficient Serverless System for GNN Se...

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33rd ACM Web Conference, WWW 2024

作者： Hu, Haichuan Liu, Fangming Pei, Qiangyu Yuan, Yongjie Xu, Zichen Wang, Lin National Engineering Research Center for Big Data Technology and System The Services Computing Technology and System Lab Cluster and Grid Computing Lab in the School of Computer Science and Technology Huazhong University of Science and Technology 1037 Luoyu Road Wuhan China Peng Cheng Laboratory Huazhong University of Science and Technology China School of Mathematics and Computer Science Nanchang University China Paderborn University Paderborn Germany

ISBN: (纸本)9798400701719

Graph Neural Networks (GNNs) have been increasingly adopted for graph analysis in web applications such as social networks. Yet, efficient GNN serving remains a critical challenge due to high workload fluctuations and intricate GNN operations. Serverless computing, thanks to its flexibility and agility, offers on-demand serving of GNN inference requests. Alas, the request-centric serverless model is still too coarse-grained to avoid resource waste. Observing the significant data locality in computation graphs of requests, we propose λGrapher, a serverless system for GNN serving that achieves resource efficiency through graph sharing and fine-grained resource allocation. "Grapher features the following designs: (1) adaptive timeout for request buffering to balance resource efficiency and inference latency, (2) graph-centric scheduling to minimize computation and memory redundancy, and (3) resource-centric function management with fine-grained resource allocation catered to the resource sensitivities of GNN operations and function orchestration optimized to hide communication latency. We implement a prototype of λGrapher based on the representative open-source serverless platform Knative and evaluate it with real-world traces from various web applications. Our results show that λGrapher can achieve an average savings of 61.5% in memory resource and 47.2% in computing resource compared with the state of the arts while ensuring GNN inference latency. © 2024 ACM.

关键词： Efficiency

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Semantic Querying of Integrated Raster and Relational data: A Virtual Knowledge Graph Approach 17

Semantic Querying of Integrated Raster and Relational Data: ...

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17th International Rule Challenge and 7th Doctoral Consortium @ RuleM+RR, RuleML+RR-Companion 2023

作者： Ghosh, Arka Šimkus, Mantas Calvanese, Diego Department of Computing Science Umeå Universitet Umeå Sweden Research Centre for Knowledge and Data Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy

Ontology-based data access (OBDA) facilitates access to heterogeneous data sources through the mediation of an ontology (e.g. OWL), which captures the domain of interest and is connected to data sources through a declarative mapping. In our study, large, heterogeneous earth observational (EO) data, known as raster data, and geometrical data, known as vector data, are considered as (heterogeneous) data sources. Raster data represent, e.g., Earth's natural phenomena, such as surface temperature, elevation, or air pollution, as multidimensional arrays. In contrast, vector data depict, e.g., locations, networks, or regions on Earth, using geometries. Domain experts, such as earth scientists and GIS practitioners, still struggle to undertake advanced studies by querying large raster and vector data in an integrated way because, unlike relational data, they come in diverse formats and different data structures. In our approach to integration, we use a geospatial extension of an RDBMS to represent vector data as relational data, and a domain-agnostic array DBMS to handle raster data. Our aim is to extend the OBDA paradigm to effectively deal with relational, vector, and raster data in a combined way, while leveraging the built-in capabilities of data management tools relevant to each type of data. We also plan to develop techniques to calculate on the fly for each user query posed over the ontology an optimal query plan that exploits, at best, the query processing capabilities of each tool, while limiting costly data transfer operations between tools. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Semantics

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Structure-aware Table-to-Text Generation with Prefix-tuning 23

Structure-aware Table-to-Text Generation with Prefix-tuning

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2023 4th International Conference on Control, Robotics and Intelligent System, CCRIS 2023

作者： Liu, Cencen Xu, Yi Yin, Wen Zheng, Dezhang University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Sichuan Chengdu China

ISBN: (纸本)9798400708190

Table-to-text generation is designed to generate descriptive natural language for structured tables that conforms to objective facts and follows the source data. The current challenge in this field is to capture the structural information of the table and improve the quality of the generated text. The existing sequence-to-sequence approach is to linearize the table, which leads to miss captured structure information and is not conducive to the model learning contextual semantics. In this paper, we introduce structural-aware self-attention, which focuses on table structure to capture cell relationships between the same row or column. In this way, the generated descriptive text can more accurately reflect the correlation between the cells in the table, discarding irrelevant information. In order to adapt the pre-trained language model to the table-to-text generation task, we introduce prefix-tuning. Traditional fine-tuning methods update all model parameters, which leads to increased training costs. In contrast, using prefix-tuning for a more lightweight approach can improve model performance considerably. Attaching continuous prompts to tokens helps the model better understand the structure and semantics of the input sequence. All of our models are extended based on T5 and have strong competitiveness in the ToTTo dataset and Hitab dataset compared with several classical baselines. © 2023 ACM.

关键词： Semantics

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Web service location-allocation using discrete NSGA-II with matrix based genetic operations and a repair mechanism

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Journal of Ambient Intelligence and Humanized computing 2023年第10期14卷 14163-14187页

作者： Verma, Shanu Pant, Millie Snasel, Vaclav Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Roorkee247667 India Centre for Artificial Intelligence and Data Science Indian Institute of Technology Roorkee Roorkee247667 India Department of Computer Science VSB-Technical University of Ostrava Ostrava70800 Czech Republic

Web Service Location-Allocation Problem (WSLAP) is an important and challenging NP-hard combinatorial optimization problem where multiple objectives affecting the quality of service (QoS) are to be optimized simultaneously. Most of the existing research articles mainly focus on selecting web service locations that minimize two QoS: total cost and total response time. However, another QoS factor throughput also needs attention when selecting sites for allocating web services. Therefore, we have constructed a new WSLAP model that minimizes the total cost and the total negative quality factor. This negative quality factor considers throughput along with latency to strengthen the QoS of the located web services. Further, three NSGA-II variants, NSGA-II.V1, NSGA-II.V2, and NSGA-II.V3, with a novel matrix-based chromosome representation, are developed to deal with the proposed bi-objective WSLAP model. These algorithms involve matrix-based genetic operations for better exploration and a repair mechanism for tackling infeasibility. The proposed model and algorithms are validated on 24 test instances, out of which 14 are existing, and 10 are newly created with increased web services, user centres and service locations. The experimental results claim that the proposed WSLAP model succeeded in improving the throughput with a comparatively lower total cost. Further, the proposed algorithms outperformed the compared algorithms in terms of hypervolume, inverted generational distance and computational time on 89% of instances. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Quality of service

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An Efficient Capsule Based Neural Network for Detection of Grape Plant Leaf Disease and Disease Stages

An Efficient Capsule Based Neural Network for Detection of G...

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Emerging Systems and Intelligent computing (ESIC), International Conference on

作者： N.Vasudevan S.Praveenkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Plant infections destroy and impair the quality of crops, and the pesticides used to treat them pollute the soil, rendering it unfit for planting. Image processing and deep learning technologies may be used to identify disease spots on grape leaves. The detection's precision and efficiency, on the other hand, remain problems. In this paper, the performance of a few well-known CNN models implemented utilizing transfer learning, such as ResNet18, VGG16, and GoogleNet were compared with capsule network. The local entity characteristics are first found by the Capsule layers. For the purpose of collecting aggregate data like "disease type" and "disease stage," the capsule layer makes use of geographic information and the frequency of local-level characteristics. Colored pictures of strong and unhealthy leaves were taken from the public dataset and then used to train the models. The proposed work focused on the novelty concept of identifying the stages of diseases. The Capsule based classification technique can give competitive benefits with an effective classification of accuracy and stages of leaf disease related to other models.

关键词： Deep learning Plant diseases Accuracy Image processing Pipelines Neural networks Transfer learning Soil Rendering (computer graphics) Diseases

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Heterogeneous Edge Service Deployment for Cyber Physical Social Intelligence in Internet of Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年 1-13页

作者： Meng, Ke Liu, Zhongjian Xu, Xiaolong Xia, Xiaoyu Tian, Hao Qi, Lianyong Zhou, Xiaokang School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Software Nanjing University of Information Science and Technology Nanjing China School of Computing Technologies RMIT University Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Qingdao China Faculty of Data Science Shiga University Otsu Japan

Cyber Physical Social Intelligence (CPSI) integrates the social intelligence and cyber-physical systems, enabling machines to better interact and respond to human social behaviors. Under CPSI, the Internet of Vehicles (IoV) has given rise to an increasing number of latency-sensitive services. Edge computing, as a distributed computing paradigm, enhances data processing capabilities, reduces data transmission latency, and minimizes bandwidth consumption. However, due to the limited resources of edge servers, striking a balance between service latency and deployment costs remains a highly challenging issue in the process of service deployment. In this paper, we propose a heterogeneous edge service deployment method for CPSI in IoV. Firstly, considering the heterogeneity of IoV services and edge servers, communication model, computational model, and heterogeneous service deployment cost model are constructed. Secondly, to maximize service deployment efficiency and minimize communication latency, a distance and workload-based edge server cluster division method is proposed. Subsequently, heterogeneous service deployment is performed in different clusters based on service category prioritization and minimal deployment quantity prioritization principles. Furthermore, an Analytic hierarchy process-based Heterogeneous edge Service dePloyment algorithm for CPSI in IoV, named AHSP, has been designed to determine optimal service deployment strategies. Finally, extensive numerical experimental results demonstrate the effectiveness of AHSP. IEEE

关键词： Computation theory

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Federated Model Heterogeneous Matryoshka Representation Learning 38

Federated Model Heterogeneous Matryoshka Representation Lear...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yi, Liping Yu, Han Ren, Chao Wang, Gang Liu, Xiaoguang Li, Xiaoxiao College of Computer Science TMCC SysNet DISSec GTIISC Nankai University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

Model heterogeneous federated learning (MHeteroFL) enables FL clients to collaboratively train models with heterogeneous structures in a distributed fashion. However, existing MHeteroFL methods rely on training loss to transfer knowledge between the client model and the server model, resulting in limited knowledge exchange. To address this limitation, we propose the Federated model heterogeneous Matryoshka Representation Learning (FedMRL) approach for supervised learning tasks. It adds an auxiliary small homogeneous model shared by clients with heterogeneous local models. (1) The generalized and personalized representations extracted by the two models' feature extractors are fused by a personalized lightweight representation projector. This step enables representation fusion to adapt to local data distribution. (2) The fused representation is then used to construct Matryoshka representations with multi-dimensional and multi-granular embedded representations learned by the global homogeneous model header and the local heterogeneous model header. This step facilitates multi-perspective representation learning and improves model learning capability. Theoretical analysis shows that FedMRL achieves a O(1/T) non-convex convergence rate. Extensive experiments on benchmark datasets demonstrate its superior model accuracy with low communication and computational costs compared to seven state-of-the-art baselines. It achieves up to 8.48% and 24.94% accuracy improvement compared with the state-of-the-art and the best same-category baseline, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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