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Potential evaluation of AI-based surrogate models to find the robust and optimal method for thermal performance assessment of desiccant air-conditioning systems

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ENERGY CONVERSION AND MANAGEMENT-X 2025年 25卷

作者： Hussain, Imtiyaz Sajjad, Uzair Bhat, Showkat Ahmad Abbas, Naseem Ali, Ahsan Sultan, Muhammad Ali, Hafiz Muhammad Hamid, Khalid Yan, Wei-Mon Rubab, Najaf Natl Taipei Univ Technol Dept Energy & Refrigerating Air Conditioning Engn Taipei 10608 Taiwan Natl Tsing Hua Univ Inst Commun Engn Hsinchu 300044 Taiwan Sejong Univ Dept Mech Engn Seoul 05006 South Korea King Fahd Univ Petr & Minerals KFUPM Interdisciplinary Res Ctr Hydrogen Technol & Carbo Dhahran 31261 Saudi Arabia Bahauddin Zakariya Univ Dept Agr Engn Multan 60800 Pakistan King Fahd Univ Petr & Minerals KFUPM Mech Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Sustainable Energy Syst Dhahran Saudi Arabia Norwegian Univ Sci & Technol NTNU Dept Energy & Proc Engn Proc & Power Res Grp Trondheim Norway Gachon Univ Dept Mat Sci & Engn Seongnam 13120 South Korea Natl Taipei Univ Technol Res Ctr Energy Conservat New Generat Residential C Taipei 10608 Taiwan

The thermal performance assessment of desiccant air-conditioning (DAC) systems is critical for improving energy efficiency in climate control applications. This study aims to develop an optimal deep learning model to predict the thermal performance of silica gel-based standalone and Maisotsenko cycle (M-cycle) integrated DAC systems. Fourteen different tuning models were constructed using various optimization strategies, including Bayesian optimization (GBRT and GPR), random search, dummy models, and random forest models with various acquisition functions (EI, PI, RCB, and GB hedge). These models were fine-tuned using a comprehensive set of hyperparameters, such as the learning rate, dense layers, activation function, initialization mode, optimizer, decay rate, batch size, and epochs. The performance of each model was evaluated based on the R2 values, with the optimal model achieving an R2 value of 0.998, demonstrating high prediction accuracy. This optimal model was trained on an experimental dataset of DAC systems to predict thermal performance. The findings highlight the effectiveness of using AI-based surrogate models for the robust and optimal assessment of DAC systems, contributing to more efficient design and operation strategies for climate control technologies.

关键词： Desiccant material Bayesian optimization Artificial intelligence hyper-parameter optimization Energy Efficiency

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Attention-based neural machine translation with quality analysis on low-resource Digaru-English Pairs

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International Journal of Information Technology (Singapore) 2025年第1期17卷 575-589页

作者： Rushanti, Kri Kakum, Nabam Sambyo, Koj Department of Computer science and Engineering National Institute of Technology Jote Arunachal Pradesh India

Attention-based neural machine translation (NMT) has significantly improved translation quality for well-resourced language pairs. However, enhancing translation accuracy for low-resource languages remains a challenge due to limited datasets and technical constraints. Despite these challenges, the latest NMT method has notably improved translation precision for language combinations with limited data by employing techniques aimed at boosting the training models. This study focuses on improving translations for the low-resource North-East Indian language, Digaru. We utilize attention-based NMT and the Transformer model with hyperparameter tuning, experimenting with datasets of 5k, 10k, and 50k sentences. Our optimized model shows a notable improvement in BLEU scores over the base models, resulting in significantly better translation accuracy. Additionally, human evaluations and multidimensional quality metrics (MQM) confirm the enhanced translation quality. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： BLEU Global attention hyper-parameter optimization MQM Transformer

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A comprehensive survey on optimizing deep learning models by metaheuristics

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ARTIFICIAL INTELLIGENCE REVIEW 2022年第2期55卷 829-894页

作者： Akay, Bahriye Karaboga, Dervis Akay, Rustu Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkey King Abdulaziz Univ Jeddah Saudi Arabia Erciyes Univ Dept Mechatron Engn TR-38039 Kayseri Turkey

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn higher levels of feature hierarchy established by lower level features by transforming the raw feature space to another complex feature space. Although deep networks are successful in a wide range of problems in different fields, there are some issues affecting their overall performance such as selecting appropriate values for model parameters, deciding the optimal architecture and feature representation and determining optimal weight and bias values. Recently, metaheuristic algorithms have been proposed to automate these tasks. This survey gives brief information about common basic DNN architectures including convolutional neural networks, unsupervised pre-trained models, recurrent neural networks and recursive neural networks. We formulate the optimization problems in DNN design such as architecture optimization, hyper-parameter optimization, training and feature representation level optimization. The encoding schemes used in metaheuristics to represent the network architectures are categorized. The evolutionary and selection operators, and also speed-up methods are summarized, and the main approaches to validate the results of networks designed by metaheuristics are provided. Moreover, we group the studies on the metaheuristics for deep neural networks based on the problem type considered and present the datasets mostly used in the studies for the readers. We discuss about the pros and cons of utilizing metaheuristics in deep learning field and give some future directions for connecting the metaheuristics and deep learning. To the best of our knowledge, this is the most comprehensive survey about metaheuristics used in deep learning field.

关键词： Deep neural networks Metaheuristics Training hyper-parameter optimization Architecture optimization Feature extraction

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Data Augmented Hardware Trojan Detection Using Label Spreading Algorithm Based Transductive Learning for Edge Computing-Assisted IoT Devices

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IEEE ACCESS 2022年 10卷 102789-102803页

作者： Sankar, Vaishnavi Nirmala Devi, M. Jayakumar, M. Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Elect & Commun Engn Coimbatore 641112 Tamil Nadu India

IoT devices handle a large amount of information including sensitive information pertaining to the deployed application. Such a scenario, makes IoT devices susceptible to various attacks. In addition to securing IoT devices, it is equally important to secure communication among devices and with the outside world. RS232 is a common communication protocol used in IoT and embedded devices. Hence ensuring, Trojan detection in RS232 plays a major role in providing secured communication among edge assisted IoT devices. The inclusion of malicious circuits known as hardware Trojans can occur at any stage of the IC design and manufacturing. Existing pre-silicon detection schemes with static features is limited by the number of features that are learned by the detection scheme. In contrast, machine learning allows enhanced Trojan space exploration. Existing machine learning-based Trojan detection consists primarily of supervised algorithms that rely on high-quality labeled datasets for efficient Trojan detection. Unsupervised methods, on the other hand, underperform due to limited training data and severe imbalance within the available data. To handle such a situation, a semi-supervised hardware Trojan detection has been proposed. In this work, permutation importance guided principal component analysis, correlation aware data augmentation, and hyper-parameter optimization using genetic algorithm aid in optimal dataset and model generation. Pseudo label generation using semi-supervised schemes is utilized to handle partially labeled datasets. For the Trust-HUB benchmarks, the proposed methodology achieves an average of 88.48% true positive rate and 95.77% true negative rate which, clearly indicates the effectiveness and feasibility of semi-supervised hardware Trojan detection.

关键词： Trojan horses Hardware Internet of Things Feature extraction Machine learning algorithms Prediction algorithms Data models Semi-supervised algorithm hardware Trojan detection correlation-aware data augmentation hyper-parameter optimization genetic algorithm permutation importance XGBoost

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A GPSO-optimized convolutional neural networks for EEG-based emotion recognition

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NEUROCOMPUTING 2020年第0期380卷 225-235页

作者： Gao, Zhongke Li, Yanli Yang, Yuxuan Wang, Xinmin Dong, Na Chiang, Hsiao-Dong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

An urgent problem in the field of deep learning is the optimization of model construction, which frequently hinders its performance and often needs to be designed by experts. Optimizing the hyperparameters remains a substantial obstacle in designing deep learning models, such as CNNs, in practice. In this paper, we propose an automatical optimization framework using binary coding system and GPSO with gradient penalties to select the structure. Such swarm intelligence optimization approaches have been used but not extensively exploited, and the existing work focuses on models with a fixed depth of networks. We design an experiment to arouse three types of emotion states for each subject, and simultaneously collect EEG signals corresponding to each emotion category. The GPSO-based method efficiently explores the solution space, allowing CNNs to obtain competitive classification performance over the dataset. Results indicate that our method based on the GPSO-optimized CNN model enables us to achieve a prominent classification accuracy, and the proposed method provides an effective automatic optimization framework for CNNs of the emotion recognition task with an uncertain depth of networks. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Convolutional neural networks (CNNs) hyper-parameter optimization Electroencephalography (EEG) analysis Particle swarm optimization (PSO)

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Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2021年第2期18卷 1803-1816页

作者： Injadat, Mohammad Noor Moubayed, Abdallah Nassif, Ali Bou Shami, Abdallah Univ Western Ontario Dept Elect & Comp Engn London ON N6A 5B9 Canada Univ Sharjah Dept Comp Engn Sharjah U Arab Emirates

Cyber-security garnered significant attention due to the increased dependency of individuals and organizations on the Internet and their concern about the security and privacy of their online activities. Several previous machine learning (ML)-based network intrusion detection systems (NIDSs) have been developed to protect against malicious online behavior. This paper proposes a novel multi-stage optimized ML-based NIDS framework that reduces computational complexity while maintaining its detection performance. This work studies the impact of oversampling techniques on the models' training sample size and determines the minimal suitable training sample size. Furthermore, it compares between two feature selection techniques, information gain and correlation-based, and explores their effect on detection performance and time complexity. Moreover, different ML hyper-parameter optimization techniques are investigated to enhance the NIDS's performance. The performance of the proposed framework is evaluated using two recent intrusion detection datasets, the CICIDS 2017 and the UNSW-NB 2015 datasets. Experimental results show that the proposed model significantly reduces the required training sample size (up to 74%) and feature set size (up to 50%). Moreover, the model performance is enhanced with hyper-parameter optimization with detection accuracies over 99% for both datasets, outperforming recent literature works by 1-2% higher accuracy and 1-2% lower false alarm rate.

关键词： Feature extraction Intrusion detection Training optimization Correlation Machine learning Organizations Network intrusion detection machine learning hyper-parameter optimization Bayesian optimization particle swarm optimization genetic algorithm

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An Enhanced Hybrid Intrusion Detection Using Mapreduce-Optimized Black Widow Convolutional LSTM Neural Networks

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WIRELESS PERSONAL COMMUNICATIONS 2024年第4期138卷 2407-2445页

作者： Kanna, P. Rajesh Santhi, P. Bannari Amman Inst Technol Dept Comp Sci & Engn Erode Tamil Nadu India Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci & T Dept Comp Sci & Engn Chennai Tamil Nadu India

Recent advancements in information and communication technologies have led to a proliferation of online systems and services. To ensure these systems' trustworthiness and prevent cybersecurity threats, Intrusion Detection Systems (IDS) are essential. Therefore, developing advanced and intelligent IDS models has become crucial. However, most existing IDS models rely on traditional machine learning algorithms with shallow learning behaviours, resulting in less efficient feature selection and classification performance for new attacks. Another issue is that these approaches are either network-based or host-based, often leading to the detection module missing many known attacks. Additionally, they struggle to handle the massive amounts of network traffic data flexible and scalable due to high model complexity. To address these challenges, an efficient hybrid IDS model is introduced, utilizing a MapReduce-based Black Widow Optimized Convolutional-Long Short-Term Memory (BWO-CONV-LSTM) network. The first stage of this IDS model involves feature selection using the Artificial Bee Colony (ABC) algorithm. The second stage employs a hybrid deep learning classifier model of BWO-CONV-LSTM on a MapReduce framework for intrusion detection from system traffic data. The proposed BWO-CONV-LSTM network combines Convolutional and LSTM neural networks, with hyper-parameters optimized by BWO to achieve the ideal architecture. The BWO-CONV-LSTM-based IDS model performance evaluations were conducted on the NSL-KDD, ISCX-IDS, UNSWNB15, and CSE-CIC-IDS2018 datasets. The results show that the proposed model achieves high intrusion detection performance, with accuracy rates of 98.67%, 97.003%, 98.667%, and 98.25% for the NSL-KDD, ISCX-IDS, UNSWNB15, and CSE-CIC-IDS2018 datasets, respectively. It also demonstrates fewer false values, reduced computation time, and improved classification coefficients.

关键词： Intrusion detection systems Deep learning Black widow optimization Convolutional-long short-term memory hyper-parameter optimization NSL-KDD ISCX-IDS UNSWNB15 CSE-CIC-IDS2018

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Automated Filter Pruning Based on High-Dimensional Bayesian optimization

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IEEE ACCESS 2022年 10卷 22547-22555页

作者： Kim, Taehyeon Choi, Heungjun Choe, Yoonsik Yonsei Univ Dept Elect & Elect Engn Seoul 03722 South Korea

Filter pruning is necessary to efficiently deploy convolutional neural networks on edge devices that have limited computational resources and power budgets. With conventional filter pruning techniques, the same pruning rate is manually specified for different convolutional layers, which is suboptimal and time-consuming. To extract the features from the coarse level to the fine level, the number of filters in each layer has various distributions. Therefore, it is unsuitable to utilize the same pruning rate for different functional layers. To address this issue, we propose a high-dimensional Bayesian optimization-based filter pruning (HDBOFP) algorithm, which aims to automatically determine the most appropriate pruning rate for each convolutional layer. In addition, the proposed method can automatically identify optimal pruning-rate combinations without a time-consuming retraining phase. Compared with conventional filter pruning methods, this automated filter pruning technique exhibits a higher efficiency, which improves accuracy and reduces the required human labor. The effectiveness of our automated filter pruning algorithm is validated through two major computer vision applications, namely image classification and object detection. Specifically, when used in combination with ResNet-110 to classify the CIFAR-10 dataset, HDBOFP reduces the required number of float point operations (FLOPs) by more than 62% without affecting accuracy. Similarly, when HDBOFP is added to the YOLOv5l framework to run detection experiments on the MS-COCO 2017 dataset, FLOPs decrease by more than 43% with only a 1.2% loss in mean average precision, which has advanced the previous studies.

关键词： Filtering algorithms Bayes methods optimization Information filters Linear programming Degradation Convolutional neural networks Bayesian optimization convolutional neural network acceleration dimensionality reduction filter pruning hyper-parameter optimization

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Safeguarding Online Spaces: A Powerful Fusion of Federated Learning, Word Embeddings, and Emotional Features for Cyberbullying Detection

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IEEE ACCESS 2023年 11卷 124524-124541页

作者： Samee, Nagwan Abdel Khan, Umair Khan, Salabat Jamjoom, Mona M. Sharif, Muhammad Kim, Do Hyuen Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 11671 Saudi Arabia AIR Univ Islamabad Dept Comp Sci Aerosp & Aviat Campus Kamra Pakistan COMSATS Univ Islamabad Dept Comp Sci Attock Campus Attock 43600 Punjab Pakistan Jeju Natl Univ Dept Comp Engn Jeju Si 63243 Jeju Do South Korea Jeju Natl Univ Big Data Res Ctr Jeju 63243 South Korea Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11671 Saudi Arabia

Cyberbullying has emerged as a pervasive issue in the digital age, necessitating advanced techniques for effective detection and mitigation. This research explores the integration of word embeddings, emotional features, and federated learning to address the challenges of centralized data processing and user privacy concerns prevalent in previous methods. Word embeddings capture semantic relationships and contextual information, enabling a more nuanced understanding of text data, while emotional features derived from text extend the analysis to encompass the affective dimension, enhancing cyberbullying identification. Federated learning, a decentralized learning paradigm, offers a compelling solution to centralizing sensitive user data by enabling collaborative model training across distributed devices, preserving privacy while harnessing collective intelligence. In this study, we conduct an in-depth investigation into the fusion of word embeddings, emotional features, and federated learning, complemented by the utilization of BERT, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) models. hyperparameters and neural architecture are explored to find optimal configurations, leading to the generation of superior results. These techniques are applied in the context of cyberbullying detection, using publicly available multi-platform (social media) cyberbullying datasets. Through extensive experiments and evaluations, our proposed framework demonstrates superior performance and robustness compared to traditional methods. The results illustrate the enhanced ability to identify and combat cyberbullying incidents effectively, contributing to the creation of safer online environments. Particularly, the BERT model consistently outperforms other deep learning models (CNN, DNN, LSTM) in cyberbullying detection while preserving the privacy of local datasets for each social platform through our improved federated learning setup. We

关键词： Cyberbullying Federated learning Data models Data privacy Training Privacy Adaptation models Multisensory integration Cyberbullying detection federated learning multi-platforms privacy preservation decentralized edge intelligence hyper-parameter optimization neural architecture search

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Linear Multiple Low-Rank Kernel Based Stationary Gaussian Processes Regression for Time Series

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 5260-5275页

作者： Yin, Feng Pan, Lishuo Chen, Tianshi Theodoridis, Sergios Luo, Zhi-Quan Tom Zoubir, Abdelhak M. Chinese Univ Hong Kong Shenzhen CUHKSZ Shenzhen 518172 Peoples R China Shenzhen Res Inst Big Data SRIBD Shenzhen 518172 Guangdong Peoples R China Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 15784 Attica Greece Tech Univ Darmstadt ETIT Signal Proc Grp Inst Telecommun D-64283 Darmstadt Hesse Germany

Gaussian processes (GPs) for machine learning have been studied systematically over the past two decades. However, kernel design for GPs and the associated hyper-parameters optimization are still difficult, and to a large extent open problems. We consider GP regression for time series modeling and analysis. The underlying stationary kernel is approximated closely by a new grid spectral mixture (GSM) kernel, which is a linear combination of low-rank sub-kernels. In the case where a large number of the involved sub-kernels are used, either the Nystrom or the random Fourier feature approximations can be adopted to reduce the required computer storage. The unknown GP hyper-parameters consist of the nonnegative weights of all sub-kernels as well as the noise variance, and they are determined through the maximum-likelihood estimation method. Two optimization methods for solving the unknown hyper-parameters are introduced, including a sequential majorization-minimization (MM) method and a nonlinearly constrained alternating direction method of multipliers (ADMM). Experimental results, based on various time series datasets, corroborate that the proposed GSM kernel-based GP regression model outperforms several benchmarks in terms of prediction accuracy and numerical stability.

关键词： Kernel GSM Gaussian processes Time series analysis Computational modeling optimization Bayes methods ADMM Gaussian processes hyper-parameter optimization linear multiple kernel low-rank kernels majorization-minimization prediction time series

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