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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2604-2614页

作者： Jahan, Chowdhury Sadman Savakis, Andreas Rochester Institute of Technology Center for Imaging Science RochesterNY14623 United States Kate Gleason College of Engineering Rochester Institute of Technology Department of Computer Engineering RochesterNY14623 United States

Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adaptation of black-box predictors becomes even more pronounced to protect intellectual property as deep learning based solutions are becoming increasingly commercialized. Current methods distill noisy predictions on the target data obtained from the source model to the target model, and/or separate clean/noisy target samples before adapting using traditional noisy label learning algorithms. However, these methods do not utilize the easy-to-hard learning nature of the clean/noisy data splits. Also, none of the existing methods are end-to-end, and require a separate fine-tuning stage and an initial warmup stage. In this work, we present Curriculum Adaptation for Black-Box (CABB) which provides a curriculum guided adaptation approach to gradually train the target model, first on target data with high confidence (clean) labels, and later on target data with noisy labels. CABB utilizes Jensen-Shannon divergence as a better criterion for clean-noisy sample separation, compared to the traditional criterion of cross-entropy loss. Our method utilizes cotraining of a dual-branch network to suppress error accumulation resulting from confirmation bias. The proposed approach is end-to-end trainable and does not require any extra fine-tuning stage, unlike existing methods. Empirical results on standard domain adaptation datasets show that CABB outperforms existing state-of-the-art black-box DA models and is comparable to white-box domain adaptation models. © 2020 IEEE.

关键词： Curricula

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Children Hematopoietic Stem Cell Transplant Survival Status Prediction using Machine Learning

Children Hematopoietic Stem Cell Transplant Survival Status ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Islam Rifat, Ariful Hossain, Mehrab Nahid, Nafiz Akter, Sharmin Islam, Ashraful American International University Department of Computer Science and Engineering Dhaka Bangladesh Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh Noakhali Science and Technology University Computer Science and Telecommunication Engineering Noakhali Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

A surgical treatment known as a bone marrow transplant (BMT) or hematopoietic stem cell transplant (HSCT) successfully treats bone marrow diseases. However, the treatment has a number of risk factors that may reduce long-term survival. This research aimed to predict the survival of children receiving Hematopoietic Stem Cell Transplantation (HSCT) or bone marrow transplant (BMT) using different machine learning classifier models, including Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), AdaBoost (AdB), Gradient Boosting Classifier (GBC), and XGBoost (XGB), using a publicly available dataset. The study involved preprocessing the data and balancing it using SMOTEENN to address the class imbalance issue. Feature selection was also performed using chi-square and correlation methods, resulting in the use of 19 out of 39 features. The data was then split into a 70-30 train-test ratio and trained using the aforementioned machine learning classifier models. The results showed that the decision tree classifier had the highest accuracy rate of 96.77%. These findings suggest that machine learning models can be utilized to predict the survival of children undergoing HSCT, with the decision tree classifier being the most accurate. So, this research provides a foundation for future studies that aim to improve the accuracy of survival predictions for children undergoing HSCT. Additionally, the findings may be utilized to aid in treatment decision-making and counseling for patients and their families. © 2023 IEEE.

关键词： Decision trees

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Correntropy-Based Data Selective Adaptive Filtering

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IEEE Transactions on Circuits and Systems I: Regular Papers 2024年第2期71卷 754-766页

作者： Chien, Ying-Ren Wu, Sheng-Teng Tsao, Hen-Wai Diniz, Paulo S.R. Adaptive and Autonomous Communication Laboratory Department of Electrical Engineering National Ilan University Yilan City260007 Taiwan College of Electrical Engineering and Computer Science Graduate Institute of Communication Engineering National Taiwan University Taipei10617 Taiwan Program of Electrical Engineering Department of Electronics and Computer Engineering COPPE/Poli Universidade Federal do Rio de Janeiro Rio de Janeiro68504 Brazil

Data selection can be used in conjunction with adaptive filtering algorithms to avoid unnecessary weight updating and thereby reduce computational overhead. This paper presents a novel correntropy-based data selection method as an alternative to conventional data selection mechanisms based on squared error values. We developed a variable correntropy sensing algorithm to maximize the instantaneous correntropy function for the Gaussian kernel function to mitigate the impact of impulse noise and other forms of noise that can be disregarded in data selection. The proposed data selection mechanism can be implemented with any adaptive filtering algorithm. In simulations, the proposed method (implemented with the least mean squared algorithm) outperformed comparable error-based data selection schemes in terms of hit rate and miss rate, and the resulting weight updating ratio was close to the expected weight updating ratio. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

关键词： Adaptive filters

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Content-based Image Retrieval in Cyber-Physical System Modeling of Robots 11th

Content-based Image Retrieval in Cyber-Physical System Model...

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11th International Conference on Recent Trends in Computing, ICRTC 2022

作者： Anantha Prabha, P. Subashree, B. Deva Priya, M. Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9789811988240

Cyber-Physical System (CPS) is used in industries and automated plants as they have modern administration abilities and are capable of performing real-time processing in a distributed architecture. In mechanical plants, they are expected to perform image recognition and retrieval, and other Machine Learning (ML) processes for efficient functioning and screening of assembling units and products. The existing strategies like remote sensing retrieval and the Gaussian mixture model for Image Retrieval (IR) are not suitable for huge datasets. The drawbacks of the existing strategies can be eliminated based on features of data by using Improvised Density Scale-Invariant Feature Transform (ID-SIFT). This approach mainly focuses on geometrical features like color, texture, size and shape-based image matching offering an improved accuracy of 89%. This approach is relatively efficient when compared to existing algorithms used in IR. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cyber Physical System

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Levy Swallow Swarm Optimization with Machine Learning-based Intrusion Detection in IIoT 4

Levy Swallow Swarm Optimization with Machine Learning-based ...

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Alzahrani, Abdulrahman Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin Saudi Arabia

ISBN: (数字)9781837240258

ISBN: (纸本)9781839539954

With the recent evolution ofIndustry 4.0, integrated devices and sensors started to generate dynamic, extensive, and heterogeneous in Industrial Internet of Things (IIoT) environments. The immense heterogeneous data probably raises the difficulties of data analysis procedures and security risks. As IIoT develops, cyber-attacks are more complex and diverse, making current anomaly detection methods’ operations less effective. Intrusion Detection System (IDS) had a major contribution to protecting IoT systems from cyberattacks by constructing complementing classical preventive security measures (e.g., encryption, authentication, and authorization). Therefore, in this paper, we present a Levy Swallow Swarm Optimization with Machine Learning-based Intrusion Detection (LSSOML-ID) technique in IIoTenviornments. The presented LSSOML-ID technique focuses on the identification and classification of intrusions in IIoT environments. To achieve this, the LSSOML-ID technique uses a Z-score normalization algorithm to scale the input data into a uniform format. For feature selection purposes, the LSSO algorithm was used which reduces the number of features involved in the classification process. Finally, the Aquila optimizer (AO) algorithm with an extreme learning machine-based autoencoder (ELM-AE) model is proficiently employed for intrusion classification. To exemplify the significant outcomes of the LSSOML-ID technique, a thoroughset of experiments areconducted. The comprehensive outcomes highlighted the betterment of the LSSOML-ID approach over recent methods. © The Institution of engineering & Technology 2023.

关键词： Network intrusion

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IoT-Based Intelligent Energy Management for EV Charging Stations

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第11期51卷 1853-1861页

作者： Dasi, Swathi Bondalapati, Swarupa Rani Subbaraju, Mummidi P. Nimma, Divya Jangir, Pradeep Reddy, R. Vijaya Kumar Zareena, N. Electronics and Communication Engineering Department Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Electrical and Electronics Engineering Department Aditya University Andhra Pradesh Surampalem India University of Southern Mississippi United States Biosciences Department Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India associated with Applied Science Research Center Applied Science Private University Amman Jordan. Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Computer Science and Engineering Department R.V.R. & J. C. College of Engineering Guntur India

The electronics panels and controllers used in electric vehicle (EV) charging stations need to function securely and optimally in countries such as India, where the external temperature remains high for a significant part of the year. This study is the first to use a solar-battery storage integrated switchable glazing architecture to supply HVAC (heating, ventilation, and air conditioning) in the control rooms of electric vehicle charging stations throughout the day. The EV charging station has been equipped with a rooftop-mounted solar PV source as part of an initiative to promote renewable energy and sustainable forms of mobility. The utilization of a vanadium redox flow battery (VRFB) by the system ensures energy security through the provision of a durable solution for storing energy over an extended period of time. A smart scheduling system utilizing IoT technology was demonstrated to effectively meet the glazing load need of buildings in real-time, taking into account dynamic climatic conditions. This system incorporates solar PV, VRFB storage, and the neighborhood power grid. The efficiency of the suggested method was validated using four separate transitory scenarios: sunny, intermittently cloudy, prolongedly cloudy, and low solar radiation with frequent grid outages. Due to its versatility to be applied in various situations, the suggested method has the potential to greatly enhance capacity. © (2024), (International Association of Engineers). All rights reserved.

关键词： Air conditioning

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Language Models over Large-Scale Knowledge Base: on Capacity, Flexibility and Reasoning for New Facts 31

Language Models over Large-Scale Knowledge Base: on Capacity...

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31st International Conference on Computational Linguistics, COLING 2025

作者： He, Qiyuan Wang, Yizhong Yu, Jianfei Wang, Wenya College of Computing and Data Science Nanyang Technological University Singapore Paul G. Allen School of Computer Science & Engineering University of Washington United States School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798891761964

Advancements in language models (LMs) have sparked interest in exploring their potential as knowledge bases (KBs) due to their high capability for storing huge amounts of factual knowledge and semantic understanding. However, existing studies face challenges in quantifying the extent of large-scale knowledge packed into LMs and lack systematic studies on LMs' structured reasoning capabilities over the infused knowledge. Addressing these gaps, our research investigates whether LMs can effectively act as large-scale KBs after training over an expansive set of world knowledge triplets via addressing the following three crucial questions: (1) How do LMs of different sizes perform at storing world knowledge of different frequencies in a large-scale KB? (2) How flexible are these LMs in recalling the stored knowledge when prompted with natural language queries? (3) After training on the abundant world knowledge, can LMs additionally gain the ability to reason over such information to infer new facts? Our findings indicate that while medium-scaled LMs hold promise as world knowledge bases capable of storing and responding with flexibility, enhancements in their reasoning capabilities are necessary to fully realize their potential. © 2025 Association for Computational Linguistics.

关键词： Structured Query Language

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Anomaly detection using deep learning approach for IoT smart city applications

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Multimedia Tools and Applications 2025年第17期84卷 17929-17949页

作者： Shibu, S. Kirubakaran, S. Remamany, Krishna Priya Ahamed, Suhail Chitra, L. Kshirsagar, Pravin R. Tirth, Vineet Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad501401 India Engineering Department College of Engineereing and Technology University of Technology an Applied Sciences PC Musandam811 Oman Machine Learning Software Engineer Paragon Semvox GmbH Saarland Kirkel Germany Department of Electrical and Electronics Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai603104 India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Maharashtra Nagpur India Department of Mechanical Engineering College of Engineering King Khalid University Asir Abha61421 Saudi Arabia

With the advancements of IoT devices, many smart applications start to rule this era. In particular, smart cities has been adapted and realized by many countries around the world. In smart cities, vas amount of data is generated at every second. This vast data need a transmission medium which could be wireless standard. However, security is the main concern in such applications since the smart transmission always binds with anomalies. The existing anomaly detection systems need improvement in accuracy due to inefficient feature extraction and selection procedure. This paper proposes an accurate anomaly detection technique that built upon deep learning approach. We proposed a Combined Deep Q-Learning (CDQL) algorithm for anomaly detection. Priory, optimal features are selected by using Spider Monkey Optimizer (SMO). With the optimal features, CDQL detects anomalies accurately. In addition, the CDQL algorithm learns the environment in order to monitor the network data continuously. This continuous monitoring and optimum features helps in accuracy improvement up to 98%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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Nature Prognosis and CV Analysis 5

Nature Prognosis and CV Analysis

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Brindha, C.S. Deepthi, L.S. Murugesan, Anitha Subramani, Kavitha Panimalar Engineering College Department Of Computer Science Chennai India

ISBN: (纸本)9798350330861

The introduction of the Job Characteristics Model appeared to be useful and to create new possibilities human resource management is built around the concept of current job design. The human resource management has been led in the proper direction and is made more applicable because of advancements in digital technologies, information systems, electronic technology and the accessibility of the internet. In order to provide a new template for an effective HR management solution in the Internet Age, the proposed system aims to build a strategy for integrating the model of Job characteristics into the E-HR system. Whenever there is an apparent increase in employment searchers but a drop in the total of open positions, it is challenging to manually choose the ideal applicant for an appropriate job by reviewing the CV. Here, the suggested system aids in hiring the best candidates by analyzing the information contained in resume and administering personality quizzes. The model that will analyze the data is constructed using logistic regression. It also makes use of Pyresparser to extract data out of a resume. In this approach, the model aids in identifying the personality and other information about the candidate, such as their qualifications and background. Organizations can use this approach to locate qualified candidates and streamline the job of the hiring team consideration © 2023 IEEE.

关键词： Human resource management

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Image Hiding with High Robustness Based on Dynamic Region Attention in the Wavelet Domain

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computer Modeling in engineering & sciences 2024年第10期141卷 847-869页

作者： Zengxiang Li Yongchong Wu Alanoud Al Mazroa Donghua Jiang Jianhua Wu Xishun Zhu Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang332020China School of Information Engineering Gongqing Institute of Science and TechnologyJiujiang332020China Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou511400China School of Information Engineering Nanchang UniversityNanchang330031China School of Mathematics and Statistics Hainan Normal UniversityHaikou571158China

Hidden capacity,concealment,security,and robustness are essential indicators of hiding ***,hiding algorithms tend to focus on algorithmic capacity,concealment,and security but often overlook the robustness of the *** practical applications,the container can suffer from damage caused by noise,cropping,and other attacks during transmission,resulting in challenging or even impossible complete recovery of the secret *** image hiding algorithm based on dynamic region attention in the multi-scale wavelet domain is proposed to address this issue and enhance the robustness of hiding *** this proposed algorithm,a secret image of size 256×256 is first decomposed using an eight-level Haar wavelet *** wavelet transform generates one coefficient in the approximation component and twenty-four detail bands,which are then embedded into the carrier image via a hiding *** the recovery process,the container image is divided into four non-overlapping parts,each employed to reconstruct a low-resolution secret *** lowresolution secret images are combined using densemodules to obtain a high-quality secret *** experimental results showed that even under destructive attacks on the container image,the proposed algorithm is successful in recovering a high-quality secret image,indicating that the algorithm exhibits a high degree of robustness against various *** proposed algorithm effectively addresses the robustness issue by incorporating both spatial and channel attention mechanisms in the multi-scale wavelet domain,making it suitable for practical *** conclusion,the image hiding algorithm introduced in this study offers significant improvements in robustness compared to existing *** ability to recover high-quality secret images even in the presence of destructive attacksmakes it an attractive option for various *** research and experimentation can explore the algorith

关键词： Image hiding robustness wavelet transform dynamic region attention

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