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Multi-objective constructal design for quadrilateral heat generation body based on thermal-entransy theory and NSGA-Ⅱ

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science China(Technological sciences) 2024年第9期67卷 2777-2786页

作者： CHEN LinGen ZHU HongWei GE YanLin SHI ShuangShuang FENG HuiJun Institute of Thermal Science and Power Engineering Wuhan Institute of TechnologyWuhan 430205China Hubei Provincial Engineering Technology Research Center of Green Chemical Equipment Wuhan 430205China School of Mechanical Electrical EngineeringWuhan Institute of TechnologyWuhan 430205China

Based on thermal-entransy theory, the multi-objective constructal design of quadrilateral heat generation body(HGB) with similar shapes of leaves is studied further. The relationship between the aspect ratio of quadrilateral HGB and average temperature difference based on thermal-entransy dissipation is compared with that between the aspect ratio of quadrilateral HGB and the maximum temperature difference(MTD). The relationship between a composite function, consisting of linear weighting sum of the average temperature difference and MTD, and aspect ratio is obtained, and the optimal aspect ratios under minimum composite function with different weighting coefficients are obtained. Using the NSGA-Ⅱ algorithm, the Pareto frontier containing a series of compromise results of average temperature difference and MTD is obtained, and optimization results are compared using the deviation index. There is no aspect ratio to make both MTD and average temperature difference reach the minimum, and the optimal aspect ratio under the minimum MTD is smaller than that under the minimum average temperature difference. The optimal aspect ratio is obtained by making the composite function reach the minimum, and the optimal aspect ratios obtained by minimizing the composite function with different weighting coefficients are different. Compared with the construct of the initial design, the value of the composite function with optimal construct decreases by 1.9%, and the aspect ratio of the quadrilateral HGB decreases by 9.1%. The average temperature difference with the optimal construct increases by 2.1%, and the MTD with the optimal construct decreases by 5.6%. The deviation index under multi-objective optimization is smaller than that under single-objective optimization, and the obtained construct has better comprehensive thermal conductivity. Compared with TOPSIS and LINMAP decision-making methods, the average temperature difference with composite function optimization increases by 0.5

关键词： thermal-entransy theory constructal theory heat conduction quadrilateral heat generation body composite function NSGA-Ⅱ

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Q-Learning-Based Pesticide Contamination Prediction in Vegetables and Fruits

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computer Systems science & engineering 2023年第4期45卷 715-736页

作者： Kandasamy Sellamuthu Vishnu Kumar Kaliappan Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407Tamil NaduIndia

Pesticides have become more necessary in modern agricultural ***,these pesticides have an unforeseeable long-term impact on people's wellbeing as well as the *** to a shortage of basic pesticide exposure awareness,farmers typically utilize pesticides extremely close to *** residues within foods,particularly fruits as well as veggies,are a significant issue among farmers,merchants,and particularly *** residual concentrations were far lower than these maximal allowable limits,with only a few surpassing the restrictions for such pesticides in *** is an obligation to provide a warning about this amount of pesticide use in *** technologies failed to forecast the large number of pesticides that were dangerous to people,necessitating the development of improved detection and early warning systems.A novel methodology for verifying the status and evaluating the level of pesticides in regularly consumed veggies as well as fruits has been identified,named as the Hybrid Chronic Multi-Residual Framework(HCMF),in which the harmful level of used pesticide residues has been predicted for contamination in agro products using Q-Learning based Recurrent Neural Network and the predicted contamination levels have been analyzed using Complex Event Processing(CEP)by processing given spatial and sequential *** analysis results are used to minimize and effectively use pesticides in the agricultural field and also ensure the safety of farmers and ***,the technique is carried out in a Python environment,with the results showing that the proposed model has a 98.57%accuracy and a training loss of 0.30.

关键词： Pesticide contamination complex event processing recurrent neural network Q learning multi residual level and contamination level

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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Activation Control of Vision Models for Sustainable AI Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3470-3481页

作者： Burton-Barr, Jonathan Fernando, Basura Rajan, Deepu Institute of High-Performance Computing The Centre for Frontier AI Research Agency for Science Technology and Research 138632 Singapore Nanyang Technological University Nanyang School of Computer Science and Engineering 639798 Singapore

As artificial intelligence (AI) systems become more complex and widespread, they require significant computational power, increasing energy consumption. Addressing this challenge is essential for ensuring the long-term sustainability of AI technology. AI-on-AI control refers to a system with a set of AI functions controlled by an upper-level AI model. Previous work in AI-on-AI control focuses on boosting accuracy or expanding system capability by increasing overall system cost. Alternatively, we focus on applying AI-on-AI control to decrease system cost and increase the sustainability and viability of a system with multiple AI functions. Our supervised image classification evaluative controller (SICEC) is a cost-reduction oriented AI-on-AI controller that learns when vision models within an AI system should be activated based on input features. The function controller (FC) preprocesses an input and activates relevant functions, functions being distinct units of AI functionality within the system. Some functions have a set of same functional models (SFMs). These models take the same input and produce the same output but have architectural differences. We introduce a same functional controller to select a SFM using the FC's decision confidence. Results are promising, with a decrease of up to 48.9% in inference time, 67.8% in floating point operations (FLOPs), and 66.4% in energy usage. With SICEC showing significant reductions in inference time and energy cost, our work contributes to limited resource computing and sustainable AI technology. © 2020 IEEE.

关键词： Energy utilization

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Twin attention based multi-task convolutional bidirectional long short term memory for facial expression recognition

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Multimedia Tools and Applications 2025年第10期84卷 8037-8070页

作者： Sreenivas, Velagapudi Sivaneasan, B. Vani, K. Suvarna Chakrabarti, Prasun Singapore Institute of Technology Singapore138683 Singapore Engineering Cluster Singapore Institute of Technology Singapore138683 Singapore Department of Computer Science and Engineering AI/ML Research Group Head VR Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

Facial Expression Recognition (FER) aims to detect the emotional state of facial images. It is playing an increasingly important role in several application areas, including human–computer interaction (HCI), video transcriptions, and social communications. This article provides an adequate attention-based multi-task deep learning method for facial expression recognition. First, the input videos are collected from the RAVDESS and MELD datasets. Then, the input videos are converted using a threshold-based keyframe extraction algorithm. Next, the input data is pre-processed using the Adaptive Pixel Density Median Filtering (A-PDMF) method. Key features such as shape, color and texture are extracted from the pre-processed images. Finally, the facial expressions are recognized by proposing a novel twin attention-based multi-task convolutional bidirectional long-short-term memory method (TA-MC-BiLSTM). In addition, the classification parameters are optimally tuned using the EX-AHA method (Extended Artificial Hummingbird Algorithm). The proposed model reduces the size of facial features while accurately identifying the wide range of facial expressions. For simulation, the proposed method prefers python tool and results are analyzed using RAVDESS and MELD datasets. The simulation results show that the proposed model provides better results than other existing models in terms of accuracy of 99.6% for RAVEDESS and 99.4% for MELD. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Improving predictive performance in e-learning through hybrid 2-tier feature selection and hyper parameter-optimized 3-tier ensemble modeling

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International Journal of Information technology (Singapore) 2024年第8期16卷 5429-5456页

作者： Tirumanadham, N S Koti Mani Kumar S, Thaiyalnayaki M, Sriram Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Department of Information Technology Bharath Institute of Higher Education and Research

The paper presents a new feature selection technique developed in detail here to address improved prediction accuracy not only for the machine-learning algorithm but also for student outcomes related to the learning environment. It is a feature selection methodology, combined with Ridge (L2) regularization and the Boruta optimization clubbed in a two-tier approach named as BR²-2 T, which results in the most effective feature set selection process. To be specific, the present study developed a three-tier ensemble model that consolidates Random Forest with Bayesian Optimization methods Support Vector Machine with random search, and Gradient Boosting with Particle Swam Optimization (PSO) for performing hyperparameter tuning. Some sophisticated techniques applied in this study are the use of Z-score normalization so as to standardize variable distribution, Synthetic Minority Over-sampling Technique (SMOTE) for handling imbalanced dataset and Multiple Imputation by Chained Equations (MICE) for handling messiness among items. The evaluation of the projected approach shows improvement in accuracies, which can attain a maximum of 98.74%. The comparative assessment of the proposed strategy unveiled that predictive modelling of educational outcomes is effectively enhanced more than common methods of topics related to education. The research sharpens student success strategies and educational pedagogy by using powerful algorithms in its direction of decision-making. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Boruta optimization Ensemble model Gradient boosting L2 regularization Multiple imputation by chained equations (MICE) Random forest Support Vector Machine Synthetic minority over-sampling technique (SMOTE)

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Coordination-environment regulation of atomic Co-Mn dual-sites for efficient oxygen reduction reaction

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Nano research 2024年第8期17卷 6841-6848页

作者： Caiting Sun Yarong Liu Zunhang Lv Rui Liu Changli Wang Liuhua Li Jinming Wang Yu Zhang Wenxiu Yang Bo Wang Key Laboratory of Cluster Science(Ministry of Education) Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion MaterialsAdvanced Technology Research Institute(Jinan)Advanced Research Institute of Multidisciplinary ScienceSchool of Chemistry and Chemical EngineeringBeijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Library Beijing Institute of TechnologyBeijing 100081China

Precisely designing atomic metal-nitrogen-carbon(M-N-C)catalysts with asymmetric diatomic configurations and studying their structure–activity relationships for oxygen reduction reaction(ORR)are important for zinc-air batteries(ZABs).Herein,a dualatomic-site catalyst(DASC)with CoN_(3)S-MnN_(2)S_(2) configuration was prepared for the cathodes of *** with Co-N-C(Mn-free)and CoMn-N-C(S-free doping),CoMn-N/S-C exhibits excellent half-wave potential(0.883 V)and turnover frequency(1.54 e·s^(−1)·site^(−1)),surpassing most of the reported state-of-the-art Pt-free ORR *** CoMn-N/S-C-based ZABs achieve extremely high specific capacity(959 mAh·g^(−1))and good stability(350 h@5 mA·cm^(−2)).Density functional theory(DFT)calculation shows that the introduction of Mn and S can break the electron configuration symmetry of the original Co 3d orbital,lower the dband center of the Co site,and optimize the desorption behavior of*OH intermediate,thereby increasing the ORR activity.

关键词： metal organic frameworks oxygen reduction reaction dual-atom catalyst coordination-environment regulation zinc-air batteries

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A lightweight approach for intrusion detection in WSNs based on DCGAN

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International Journal of Information technology (Singapore) 2025年第2期17卷 951-957页

作者： Devi, Manu Nandal, Priyanka Sehrawat, Harkesh Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India

Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredictable environments that characterize WSNs frequently provide challenges for traditional intrusion detection systems (IDS). To overcome these issues, this research introduces a unique lightweight intrusion detection system that utilizes Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed system employs wireless sensor network blackhole flooding selective forwarding (WSN-BFSF) dataset for training and evaluation. The suggested approach improves the IDS’s detection capabilities in addition to reducing computing overhead. The findings show that the lightweight DCGAN-based IDS provides an improved defense against new security threats, making it a good fit for real-time deployment in WSNs. Here, the performance metrics like receiver operating characteristic (ROC) is evaluated to check the efficiency of the model. The proposed model is assessed with the existing studies based on metrics such as accuracy, precision, recall and f1-score. The results show that the proposed approach attains the highest accuracy (94%) thus outperforming the existing methods and studies. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep Learning (DL) Generative Adversarial Networks (GAN’s) Intrusion Detection System (IDS) Lightweight DCGAN Wireless Sensor Networks (WSN’s)

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Exploiting Retina Biometric Fused with Encoded Hash for Designing Watermarked Convolutional Hardware IP Against Piracy

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SN computer science 2024年第8期5卷 1-17页

作者： Chaurasia, Rahul Sengupta, Anirban Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal India Department of Computer Science and Engineering Indian Institute of Technology Indore India

The convolution layer in a convolutional neural network (CNN) is highly computationally intensive. It is crucial to design reusable low-cost hardware IP for convolutional layer for enabling hardware-based feature extraction. However, the involvement of fake IP vendor/untrustworthy broker in the integrated circuit (IC) supply chain, makes these IPs susceptible to the threat of piracy. The proposed approach presents high- level synthesis (HLS) driven watermarking methodology for designing low-cost and secure convolutional hardware IP. The presented watermarking approach employs complier-driven high-level transformation and exploits retinal signature fused with the encoded hash for piracy detective countermeasure. The proposed approach, therefore, firstly performs compiler-driven high-level transformation in order to optimize the design latency, followed by embedding the watermark of an authentic IP vendor. The generated watermark in the form of encoded hardware watermarking constraints (digital evidence) is covertly embedded into the resulting optimized design during the register allocation module of HLS. The proposed approach achieves the following: (i) optimized and secure design for convolutional hardware IP, (ii) robust detection of pirated IP at zero design cost overhead, (iii) significantly lower probability of coincidence (in the range of 1.3E−06 to 1.2E−09) indicating stronger digital evidence and higher tamper tolerance (in the range of 2.64E+460 to 9.60E+698) than recent approaches. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional layer Encoded hash Hardware security HLS IP design Retina biometrics

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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