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Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

computers, Materials & Continua 2024年第11期81卷 2819-2843页

作者： Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi Sarvenaz Sadat Khatami Diego Martín Sepehr Soltani Sina Aghakhani The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609-2280USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Department of Data Science Engineering University of HoustonHoustonTX 77204USA Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTX 77204USA Department of Industrial and Manufacturing Systems Engineering Iowa State UniversityAmesIA 50011USA

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of *** by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the *** integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization *** can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the *** compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network *** proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence *** our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation *** was accompanied by the lowes

关键词： Smart grid communication secrecy rate optimization reinforcement learning improved chimp optimization algorithm

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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Introduction to the Minitrack on Learning Analytics 2023

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2023年 2023-January卷 1269-1270页

作者： Willermark, Sara Islind, Anna Sigridur Deeva, Galina Óskarsdóttir, Mari University West School of Business Economics and IT Sweden Reykjavik University Department of Computer Science Iceland KU Leuven Research Centre for Information Systems Engineering Belgium

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection 38

FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalou...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Coursey, Austin Ji, Junyi Quinones-Grueiro, Marcos Barbour, William Zhang, Yuhang Derr, Tyler Biswas, Gautam Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems United States Department of Computer Science United States

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/to facilitate future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. This research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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Convolutional Neural Network (CNN) for Fake Logo Detection: A Deep Learning Approach Using TensorFlow Keras API and Data Augmentation 3

Convolutional Neural Network (CNN) for Fake Logo Detection: ...

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

作者： Murali, Saritha Gupta, Vaishnavi Saha, Soumyojyoti Sharan, Harsh Sinha, Prajwal Baibhav, Kumar Vellore Institute of Technology Department of Database Systems Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350372120

In numerous applications, including image retrieval, brand monitoring, and counterfeit identification, the detection and categorization of logos play pivotal roles. This study proposes a novel methodology employing convolutional neural networks (CNNs) for discerning fraudulent logos. The CNN's structure involves several convolutional layers at its core, accompanied by max-pooling layers to extract features for classification. Training the CNN model involves a varied dataset containing both authentic and fake logo images, and its effectiveness is assessed using well-established metrics like accuracy, precision, recall, and F1-score. The results of the study indicate that the proposed model is proficient in distinguishing counterfeit logos accurately. Furthermore, the study delves into potential applications and future directions of using CNN for identifying counterfeit logos. This research presents a robust methodology for counterfeit logo detection, thereby supporting endeavors aimed at curtailing the proliferation of counterfeit goods and upholding brand integrity. © 2024 IEEE.

关键词： Image classification

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Categorizing methods for integrating machine learning with executable specifications

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science China(Information sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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An effective Image-based Improved Deep Neural Network Framework for Coal and Gangue Detection 3

An effective Image-based Improved Deep Neural Network Framew...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Praveena, M. Afsana, A. Devi, T. Durga Sudharsan, S. Arulkumar, V. Dr Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore641049 India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350322842

In coal power plants, gangue is a by-product that is not wanted, even though coal is the energy source utilized the most worldwide. As a consequence of this, the removal of gangue from coal during the pre-processing stage of the mining technique is an essential step. With the development of new computer technologies, a method may be discovered that will allow for an improvement in the efficiency of gangue separation. This study presents a method for identifying gangue using improved deep neural networks. The method involves building a coal-gangue filtration system based on the distinctions between gangue and coal in terms of its rough surface and grayscale feature. In addition, this tactic will make it possible for robots, rather than people, to identify the gangue. Suppose the technology described in this article is applied in the coal industry. In that case, it will lead to the computerization of the extraction of coal gangue, an increase in the raw coal processing efficiency, and an improvement in the quality of the coal. According to the findings of experiments, the strategy enhances the accuracy results of coal and gangue materials by as much as 98.87% compared to the currently used approaches. © 2023 IEEE.

关键词： Coal

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