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Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques:An Explainable Artificial Intelligence Approach

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computers, Materials & Continua 2024年第12期81卷 4033-4048页

作者： Avi Deb Raha Fatema Jannat Dihan Mrityunjoy Gain Saydul Akbar Murad Apurba Adhikary Md.Bipul Hossain Md.Mehedi Hassan Taher Al-Shehari Nasser A.Alsadhan Mohammed Kadrie Anupam Kumar Bairagi Computer Science and Engineering Discipline Khulna UniversityKhulna9208Bangladesh Information and Communication Engineering Noakhali Science and Technology UniversityNoakhali3814Bangladesh School of Computing Sciences and Computer Engineering University of Southern MississippiHattiesburgMS 39401USA Computer Skills Department of Self-Development SkillCommon First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia

Breast cancer stands as one of the world’s most perilous and formidable diseases,having recently surpassed lung cancer as the most prevalent cancer *** disease arises when cells in the breast undergo unregulated proliferation,resulting in the formation of a tumor that has the capacity to invade surrounding *** is not confined to a specific gender;both men and women can be diagnosed with breast cancer,although it is more frequently observed in *** detection is pivotal in mitigating its mortality *** key to curbing its mortality lies in early ***,it is crucial to explain the black-box machine learning algorithms in this field to gain the trust of medical professionals and *** this study,we experimented with various machine learning models to predict breast cancer using the Wisconsin Breast Cancer Dataset(WBCD)*** applied Random Forest,XGBoost,Support Vector Machine(SVM),Multi-Layer Perceptron(MLP),and Gradient Boost classifiers,with the Random Forest model outperforming the others.A comparison analysis between the two methods was done after performing hyperparameter tuning on each *** analysis showed that the random forest performs better and yields the highest result with 99.46%*** performance evaluation,two Explainable Artificial Intelligence(XAI)methods,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-Agnostic Explanations(LIME),have been utilized to explain the random forest machine learning model.

关键词： Breast cancer prediction machine learning models explainable artificial intelligence random forest hyperparameter tuning

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A Smart CIoT with Secure Healthcare Framework Using Optimized Deep Recuperator Neural Network Long Short-Term Memory

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IEEE Internet of Things Journal 2024年第6期11卷 10551-10562页

作者： Narmatha, C. Manimurugan, S. Karthikeyan, P. University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Oakland University Department of Electrical and Computer Engineering RochesterMI112345 United States National Chung Cheng University Department of Computer Science and Information Engineering Chiayi62102 Taiwan RV University School of Computer Science and Engineering Bengaluru560059 India

The healthcare system currently relies on the facility to store and process large amounts of health data, supported by efficient management. The Internet of Things (IoT) has driven the growth of Adroit Healthcare, which has vast data processing capabilities and extensive data collection. The consumer IoT (CIoT), also known as the IoT in the context of individual use cases, is dominated by personal healthcare applications. The remarkable expansion of the CIoT can be attributed to the extensive embrace of wearable technology, facilitating everyday monitoring of vital health indicators like blood pressure, heart rate, and respiration rate. However, the CIoT raises concerns about data security, confidentiality, and customer trust. This study proposes an integrating blockchain and deep learning (DL) approach to promote responsible use of CIoT. The study develops an Intelligent IoT (IIoT) and healthcare diagnostic model, named BT-PWO-DRNN-LSTM, using blockchain technology and Prairie Wolf optimization-based deep recuperator neural network (DRNN) and long short-term memory (LSTM). The BT-PWO-DRNN-LSTM approach includes three key processes: 1) encrypted transactions;2) cryptographic hash feature;and 3) medical diagnosis. The PWO technique is used for secure communication of medical images, the neighborhood catalogue disposition (NCD) technique is used in the cryptographic hash feature operation (CHFO), and the DL approach is employed as a classification algorithm for diagnosing disorders. The use of PWO methodology for secure healthcare communication and optimal parameter fine-tuning highlights the originality of the study. The BT-PWO-DRNN-LSTM prototype showed positive results with high sensitivity, specificity, and accuracy during the diagnostic process. © 2014 IEEE.

关键词： Blockchain

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Research on network traffic intrusion detection based on GAN 9

Research on network traffic intrusion detection based on GAN

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xi, Chiming Wang, Hui Shanghai Institute of Technology School of Computer Science & Information Engineering Shanghai China

ISBN: (纸本)9798350363043

With the rapid growth of computer network devices around the world, network threats are increasing, and securing the network environment has become an urgent problem. However, the current network traffic dataset is generally characterized by unbalanced data distribution, in which certain anomalous traffic types have a relatively small number of samples, which causes the detection model to ignore traffic types with a small number of samples, thus reducing the overall accuracy of the model. To address this problem, this paper proposes an improved intrusion detection method based on generative adversarial networks (GAN). By designing a regularized bi-discriminator structure to improve the generation quality of the generator. In addition, a random mask enhancement module is integrated after the generator to supplement the detailed information of data features. Our designed GAN model is applied to Support Vector Machine (SVM) and Long Short-Term Memory Network (LSTM), the experiment results show that it significantly improves the detection accuracy. © 2024 IEEE.

关键词： Generative adversarial networks

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Banking credit qualification based on blockchain and zero-knowledge proofs 9

Banking credit qualification based on blockchain and zero-kn...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Xue, QingShui He, YiXun Shanghai Institute of Technology School of computer science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

In the context of the new era, the financing needs of enterprises grow significantly with the continuous expansion of their scale. However, in the financing process, small and micro enterprises often encounter challenges such as insufficient credit ratings, lack of working capital, difficulty in accessing information, and lack of guarantee mechanisms, while medium and large enterprises are more concerned about the security of confidential data. To address these challenges, this paper innovatively proposes a credit model based on zero-knowledge proof, ZKPrivateLoan, which combines blockchain technology with distributed zk-SNARK technology to not only efficiently generate proof of credentials of confidential data to ensure the security of data privacy, but also enhance the trust of commercial banks in the credit credentials of micro- and small enterprises. In addition, this paper also realizes the batch verification mechanism based on the ZKPrivateLoan model, which effectively reduces the verification time and blockchain uploading cost and provides a more efficient and secure solution for enterprise financing. © 2024 IEEE.

关键词： Blockchain Distributed Zero Knowledge Proof Financing Certification

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Non-revisiting Stochastic Search for Automatic Graph Learning 20th

Non-revisiting Stochastic Search for Automatic Graph Learni...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Bu, Chenyang Lu, Hezhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9789819970216

In recent years, automatic graph learning (AutoGL) has been widely concerned by academia and industry because it can significantly reduce the threshold and labor cost of graph learning. It has shown powerful functions in hyper-parameter optimization, model selection, graph neural architecture search, and feature engineering. With the development of the network structure, the time and computing resources consumed by the AutoGL process are increasing. AutoGL can be viewed as a bilevel optimization problem encompassing inner and outer optimization. The inner optimization focuses on optimizing the model parameters through techniques like stochastic gradient descent, aiming to minimize the loss function and enhance model performance. On the other hand, the outer optimization aims to identify the best configuration settings for hyperparameters and neural network structures. To address the computational cost associated with heuristic algorithms in bilevel optimization for AutoGL, the non-revisiting idea is proposed to keep a record of all previously evaluated individuals to avoid redundant search. Experiments on multiple search algorithms demonstrate that non-revisiting can improve the time performance of AutoGL. The time performance of the search algorithm with non-revisiting is improved by 40 % to 80 % compared with that without non-revisiting under the condition that the experimental accuracy remains unchanged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Heuristic algorithms

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SLOG: smartphone-based semantic lifelogging framework for digital prosthetic memory development

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Personal and Ubiquitous Computing 2023年第6期27卷 2071-2102页

作者： Ali, Shaukat Khusro, Shah Khan, Akif Khan, Inayat Alam, Iftikhar Solehria, Salman Faiz Department of Computer Science University of Peshawar Peshawar25120 Pakistan Department of Computer Science Sarhad University of Information Technology Peshawar25000 Pakistan Department of Computer Science University of Engineering and Technology Mardan Mardan23200 Pakistan

The advancements in sensors, processing, storage and networking technologies have turned smartphone into de facto lifelogging device. Realizing the lifelogging potential of smartphone, researchers have postulated with smartphone-based lifelogging systems with varying capabilities of capturing and storing information about peoples’ daily life activities, behaviors, interactions, locations, environments, etc., into their lifelog archives. However, the systems are lacking with several capturing, storing, retrieving and design issues, importantly not organizing the lifelog information with the same semantics as they exist in the real world and encoded in human episodic memory. The semantic organization of lifelog information will develop a true digital prosthetic memory to solve problems in different real-world domains including memory augmentation. In this paper, we have presented the architecture and implementation of Semantic LifelOGging (SLOG) framework for smartphone-based semantic lifelogging using Semantic Web technologies. The SLOG framework incorporates smartphone sensors and processing capabilities, and Semantic Web technologies to develop a semantically enriched digital prosthetic memory on smartphone. The semantic lifelog data model is developed by extending SmartOntoSensor ontology with relevant concepts and properties to represent, store and relate lifelog and contextual information. The semantic lifelog data model will provide contextual semantics to organize, annotate and relate lifelog information like human episodic memory and provide enough contextual cues to recall lifelog information like associative recall in human memory. The proof-of-concept applications are developed to show practicality of the SLOG framework. The quantitative and empirical evaluations have shown the SLOG framework and system has improved features and is acceptable in usability and effectiveness. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part

关键词： Smartphones

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Video Segment Time-Channel Frequency Attention Network for Remote Photoplethysmography 5

Video Segment Time-Channel Frequency Attention Network for R...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Hu, Min Gao, Dongming Wang, Xiaohua Tang, Yiming Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Remote Photoplethysmography (rPPG) is a technique for remotely measuring physiological signals such as (HR). Compared to traditional contact-based measurements, HR estimation based on video recording of human face offers significant convenience and wide-ranging application prospects. This paper proposes an end-to-end model named VSTCFANet. Firstly, a video segment attention module (VSA) is constructed to mitigate redundancy in facial videos. The facial videos are divided into multiple subsegments and passed through corresponding subpaths to extract spatial and temporal information. Secondly, a time-channel frequency attention module (TCFA) is designed to extract features in the channel frequency domain. The channel frequency domain features of the video segments are aggregated to enhance the model's robustness, thereby reducing the effection caused by changes in distance and head movements. Additionally, an emotion recognition module is built to utilize the obtained rPPG signals to identify the emotion of experimental subjects. Lastly, two loss functions are employed to mitigate the interference from other physiological signals on the rPPG signals. Extensive experiments are conducted on the PURE, COHFACE, and MAHNOB-HCI datasets to validate the effectiveness of the proposed model, the experimental results show that the proposed model has made significantly better performances in measurement accuracy than the state-of-the-art baselines. © 2024 IEEE.

关键词： Emotion Recognition

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AnyComputing: A Novel Architecture for Orchestrating Various Computing Paradigms for Adaptive Workload Management 21

AnyComputing: A Novel Architecture for Orchestrating Various...

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21st International Conference on Electrical engineering, Computing science and Automatic Control, CCE 2024

作者： Taghizadeh, Seyedreza Yousefi, Sara Engineering and Information Technology Shiraz University Department of Computer Science Shiraz Iran

ISBN: (纸本)9798350377545

The proliferation of physical objects and intelligent systems, along with their inherent differences, such as levels of device mobility, generated data volume, required storage memory, and complexity in data processing, presents a significant challenge in achieving real-Time and optimal responsiveness to the Massive volume of sent requests from these systems. Depending on the nature of each request and its requirements, effective processing must be by available and suitable computational platforms. This situation necessitates the urgent development of novel ideas and frameworks in request orchestration and processing by suitable computational platforms to enhance system performance and efficiently utilize available computational resources. In this article, we propose an integrated computational architecture spanning from edge devices of users and intelligent things to cloud servers. This design encompasses the reception, processing, and response to various computational and storage requests, considering specific requirements, through computational units deployed within this span. Our objective in devising this architecture is to establish mechanisms for categorizing diverse computational requests from users and things, effectively allocating resources to them, thereby reducing response time, optimizing bandwidth utilization and other available resources, managing latency for real-Time requests, and minimizing energy consumption in user devices and intelligent objects. © 2024 IEEE.

关键词： Memory management units

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An Intelligent Intrusion Detection using Rule-based CNN with PSO Optimizer 3

An Intelligent Intrusion Detection using Rule-based CNN with...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Karpurasundharapondian, P. Selvi, M. Thangaramya, K. Santhosh Kumar, S.V.N. Kannan, A. Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India

ISBN: (纸本)9798350372120

Security and secure routing are important design issues in the design of Wireless Sensor Networks (WSNs). Intrusion Detection Systems (IDSs) are useful for securing the communication in WSNs. An IDS can be developed by using machine learning based approach. On the other hand, it can be developed using a deep learning classification algorithm. Optimization techniques including Particle Swarm Optimization (PSO) are useful for choosing optimal features from given dataset. This paper proposes an extended CNN algorithm by extending the existing CNN classifier with rules, SVM and PSO named Rule Extended CNN with SVM (RE-SVM-CNN) for developing this IDS which reduces the false alarm rate by enhanced detection accuracy. © 2024 IEEE.

关键词： Wireless sensor networks

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Dynamic Routing of Multiple QoS-Required Flows in Cloud-Edge Autonomous Multi-Domain Data Center Networks

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computers, Materials & Continua 2024年第2期78卷 2287-2308页

作者： Shiyan Zhang Ruohan Xu Zhangbo Xu Cenhua Yu Yuyang Jiang Yuting Zhao School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing100876China College of Computer Science and Technology Tianjin UniversityTianjin300072China Institute of Artificial Intelligence Nankai UniversityTianjin300071China College of Information Science and Engineering East China University of Science and TechnologyShanghai200237China

The 6th generation mobile networks(6G)network is a kind of multi-network interconnection and multi-scenario coexistence network,where multiple network domains break the original fixed boundaries to form connections and *** this paper,with the optimization objective of maximizing network utility while ensuring flows performance-centric weighted fairness,this paper designs a reinforcement learning-based cloud-edge autonomous multi-domain data center network architecture that achieves single-domain autonomy and multi-domain *** to the conflict between the utility of different flows,the bandwidth fairness allocation problem for various types of flows is formulated by considering different defined reward *** the tradeoff between fairness and utility,this paper deals with the corresponding reward functions for the cases where the flows undergo abrupt changes and smooth changes in the *** addition,to accommodate the Quality of Service(QoS)requirements for multiple types of flows,this paper proposes a multi-domain autonomous routing algorithm called LSTM+*** a Long Short-Term Memory(LSTM)layer in the actor and critic networks,more information about temporal continuity is added,further enhancing the adaptive ability changes in the dynamic network *** LSTM+MADDPG algorithm is compared with the latest reinforcement learning algorithm by conducting experiments on real network topology and traffic traces,and the experimental results show that LSTM+MADDPG improves the delay convergence speed by 14.6%and delays the start moment of packet loss by 18.2%compared with other algorithms.

关键词： Multi-Domain data center networks autonomous routing

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