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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

Multimedia Tools and Applications 2024年第42期83卷 90511-90532页

作者： Mehmood, Atif Shahid, Farah Khan, Rizwan Ahmed, Shahzad Ibrahim, Mostafa M. Zheng, Zhonglong School of Computer Science and Technology Zhejiang Normal University Jinhua321002 China Zhejiang Institute of Photoelectronics & Zhejiang Institute for Advanced Light Source Zhejiang Normal University Zhejiang Jinhua321004 China Faculty of Information Technology Beijing University of Technology Beijing100024 China Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt

The metaverse concept extends beyond virtual worlds and can be applied to collaborative analysis environments. Data analysts worldwide may read academic article extracts in real-time in a shared digital workplace to mine and analyze data using the metaverse. Furthermore, a semantic metaverse in natural language processing might also involve creating a digital environment with linguistic and semantic connections. Many dedicated researchers navigating the complexities of natural language processing in the metaverse era have spent considerable time searching for relevant papers. However, online reviews and evaluations of articles are helpful for their assistance and may save the researcher time. In this work, human specialists manually produced a dataset from four conferences and evaluated subjectively using rule-based techniques. Subsequently, we aim to evaluate the effectiveness of pre-trained word embeddings and pre-trained BERT models seamlessly integrated with convolutional neural networks. This endeavor focuses on the subjective analysis and classification of contributions and previous work sentences extracted from academic literature. For comparison, various deep learning architectures were systematically employed, including long short-term memory-GloVe and bi-directional long short-term memory-GloVe, alongside classical machine learning methods. Our findings show that the proposed BERT model achieved state-of-the-art performance in classification and subjective analysis tasks with an accuracy of 91.50% and F1 score of 91.00%. Finally, we plan to utilize sentence similarity to identify contributions within abstracts, thus outlining potential avenues for future research. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Natural language processing systems

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Aloe Vera Tissue Modeling and Parameter Identification Using Meta-heuristic Optimization Algorithm

Aloe Vera Tissue Modeling and Parameter Identification Using...

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2023 International Conference on Microelectronics, ICM 2023

作者： Ghoneim, Mohamed S. Fathi, Dalia A. Said, Lobna A. Madian, Ahmed H. Bayoumi, Magdy A. University of Louisiana at Lafayette Department of Electrical and Computer Engineering LA United States Cairo Egypt Ncrrt Egyptian Atomic Energy Radiation Engineering Dept. Cairo Egypt University of Louisiana Center for Advanced Computer Studies LafayetteLA United States

ISBN: (纸本)9798350380828

The agricultural industry's use of non-invasive bioimpedance monitoring methods is expanding quickly. These measured impedance fluctuations reflect imperceptible biophysical and biochemical changes in living and non-living tissues. Bioimpedance circuit modeling is a valuable method for fitting the measured impedance in biology and medicine. A study on two samples of Aloe Vera leaves is conducted to identify the best model representing Aloe Vera leaves, and two different interelectrode spacing distances are used to measure each sample. An electrochemical station (SP150) is used to detect bio-impedance at frequencies between 80 Hz and 200 kHz. The effectiveness of the employed models is evaluated by fitting them to the measured data. Among all models used, the medical plant stem and simplified plant stem models show the best results for all inter-electrode spacing distances. © 2023 IEEE.

关键词： Timing circuits

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3D Robotic Control of Micro-Scale Optical Swarms at an Interface

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IEEE Robotics and Automation Letters 2025年第7期10卷 6712-6719页

作者： Carlisle, Nicholas Nock, Volker Williams, Martin A.K. Whitby, Catherine P. Chen, Jack L.Y. Avci, Ebubekir University of Canterbury Department of Electrical and Computer Engineering Christchurch New Zealand MacDiarmid Institute for Advanced Materials and Nanotechnology Wellington New Zealand Massey University School of Fundamental Sciences Palmerston North New Zealand Auckland University of Technology School of Science Auckland New Zealand

Optical force-induced assembly is a promising yet scarcely explored approach for developing functional tools and objects at the microscale, with a wide range of potential applications. Our previous work was the first to investigate the manipulation of these assemblies in the XY plane. Here, we expand on these techniques by systematically exploring optical trap manipulation with the addition of Z-axis control. Manipulation of the Z-axis is referred to as axial displacement and is a viable approach for actively manipulating the assembly morphology. Experiments are conducted for the first time to explore and detail the response of the assembly during active 3D trap manipulation, informing the development of an autonomous control algorithm over the 2D area of the assembly during motion. This control presents techniques to increase assembly stability or alter the area of the assembly for tasks such as passing through constrictions. This work aims to develop the control techniques required to create a unique micromanufacturing approach inspired by the Kilobot thousand-robot swarm. © 2016 IEEE.

关键词： Microrobots

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Performance Analysis of AODV Routing forWireless Sensor Network in FPGA Hardware

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computer Systems Science & engineering 2022年第3期40卷 1073-1084页

作者： Namit Gupta Kunwar Singh Vaisla Arpit Jain Adesh Kumar Rajeev Kumar Department of Computer Science&Engineering Uttarakhand Technical UniversityDehradun248007India Department of Computer Science B T Kumaon Institute of Technology DwarahatIndia Faculty of Engineering&CS Teerthanker Mahaveer UniversityMoradabadIndia Department of Electrical&Electronics Engineering School of EngineeringUniversity of Petroleum and Energy StudiesDehradun248007India

Wireless sensor network(WSN)is a group of interconnected sensor nodes that work wirelessly to capture the information of *** routing of the network is a challenging *** routing of WSN is classified as proactive,reactive,and *** on-demand distance vector(AODV)routing is an example of reactive routing based on the demand route formations among different nodes in the *** research article emphasizes the design and simulation of the AODV routing hardware chip using very-high-speed integrated circuit hardware description language(VHDL)programming in Xilinx integrated synthesis environment(ISE)14.7 *** performance of the chip is studied based on the field-programmable gate array(FPGA)hardware parameters such as slices,lookup table(LUTs),input/output block(IOB),flipflops,and memory for the different configurations of the network(N=10,20….100).The delay and frequency are also estimated on the Virtex-5 *** performance of the WSN with AODV routing is also analyzed based on the packet delivery ratio,throughput,delay,and control *** simulation test cases verified the 8-bit,64-bit,and 128-bit data communication within the nodes.

关键词： Wireless sensor network FPGA synthesis AODVrouting simulation and synthesis VHDL simulation

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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A novel method for the detection and classification of multiple diseases using transfer learning-based deep learning techniques with improved performance

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Neural Computing and Applications 2024年第30期36卷 18979-18997页

作者： Natarajan, Krishnamoorthy Muthusamy, Suresh Sha, Mizaj Shabil Sadasivuni, Kishor Kumar Sekaran, Sreejith Charles Gnanakkan, Christober Asir Rajan A.Elngar, Ahmed School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Tamil Nadu Perundurai Erode India Centre for Advanced Materials Qatar University Doha Qatar Department of Mechanical and Industrial Engineering Qatar University PO Box 2713 Doha Qatar Department of Electrical Engineering National Institute of Technology Assam Silchar India Department of Electrical and Electronics Engineering Puducherry Technological University Puducherry India Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef City62511 Egypt

A disease is a distinct abnormal state that significantly affects the functioning of all or part of an individual and is not caused by external harm. Diseases are frequently understood as medical conditions connected with distinct indications and symptoms. According to a fairly wide categorization, diseases can also be categorized as mental disorders, deficient diseases, genetic diseases, degenerative diseases, self-inflicted diseases, infectious diseases, non-infectious diseases, social diseases, and physical diseases. Prevention of the diseases is of multiple instances. Primary prevention seeks to prevent illness or harm before it ever happens. Secondary prevention tries to lessen the effect of an illness or damage that has already happened. This is done through diagnosing and treating illness or injury as soon as feasible to stop or delay its course, supporting personal ways to avoid recurrence or reinjury, and implementing programs to restore individuals to their previous health and function to prevent long-term difficulties. Tertiary prevention tries to lessen the impact of a continuing sickness or injury that has enduring repercussions. Diagnosis of the disease at an earlier stage is important for the treatment of the disease. Hence, in this study, deep learning algorithms, such as VGG16, EfficientNetB4, and ResNet, are utilized to diagnose various diseases, such as Alzheimer's, brain tumors, skin diseases, and lung diseases. Chest X-rays, MRI scans, CT scans, and skin lesions are used to diagnose the mentioned diseases. Transfer learning algorithms, such as VGG16, VGG19, ResNet, InceptionV3, and EfficientNetB4, are utilized to categorize various diseases. EfficientNetB4 with the learning rate annealing, having obtained an accuracy of 94.04% on the test dataset, is observed. As a consequence, we observed that every network has unique particular skills on the multi-disease dataset, which includes chest X-rays, MRI scans, etc., © The Author(s) 2024.

关键词： Diseases

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Near-Field Communications: Shape and Structure Design for Uniform Planar Array

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IEEE Internet of Things Journal 2025年第11期12卷 15151-15165页

作者： Chen, Siyu Gu, Juping Duan, Wei Zhang, Lei Dang, Shuping Wen, Miaowen Ding, Zhiguo Ho, Pin-Han Nantong University School of Information Science and Technology Nantong226019 China Suzhou University of Science and Technology China Hubei Polytechnic University Department of Electronics and Information Engineering Huangshi435003 China University of Bristol School of Electrical Electronic and Mechanical Engineering BristolBS8 1UB United Kingdom South China University of Technology School of Electronic and Information Engineering Guangzhou510640 China Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

With the flourishing development of 6G wireless networks, the demand of spectrum efficiency rapidly increases, in order to support the demands of high quality data transmission and connections of massive users. Among various promising technologies, the technology of near-field communications provides a great potential to address such an issue due to the unique spherical-wave channels for electromagnetic (EM) propagation. In this paper, multiple-input single-output (MISO) near-field communications is comprehensively studied to clarify the influences of the shape and structure of uniform planar array (UPA) on the system performance, considering three cases with uniform linear array (ULA), rectangular UPA, and circular UPA. In particular, we reveal the properties of rapid deterioration for signal-to-noise ratio (SNR) from the reduced projection aperture in near-field communications and investigate the single spherical crown antenna design and spherical crown antenna array design in order to address this issue. Moreover, we also characterize the role of antenna projection aperture in details, and theoretically analyze the shape of UPA, yielding the corresponding exact closed-form expressions for SNR and outage probability (OP). Based on these above analytical results, we find out that adjusting the spacing between adjacent antennas to control the relative angle between user and selected antennas is an efficient way to improve the projection aperture of antenna and SNR. Simulation results are shown to well match analytical results, which validate the correctness of our analysis, clarifying that our proposed designs outperform the conventional works and illustrating a better stability for angle variations. © 2025 IEEE.

关键词： Spheres

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S²RNN: Self-Supervised Reconfigurable Neural Network Hardware Accelerator for Machine Learning Applications

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IEEE Internet of Things Journal 2024年第6期12卷 6708-6720页

作者： Kasem Khalil Bappaditya Dey Magdy Bayoumi Electrical and Computer Engineering Department University of Mississippi Oxford MS USA Electrical Engineering Department Assiut University Asyut Egypt Angstrom Patterning Department Interuniversity Microelectronics Centre (Belgium) Leuven Belgium Center for Advanced Computer Studies University of Louisiana Lafayette LA USA Department of Electrical and Computer Engineering University of Louisiana Lafayette LA USA

Hardware implementation of neural networks (NNs) is challenging due to varying application requirements. This often necessitates creating specific field programmable gate arrays (FPGAs) configurations from scratch for each application. This article proposes a flexible, self-supervised reconfigurable method to fit several application requirements by providing only the maximum available computational nodes a priori. The proposed method dynamically reconfigures the required number of hidden layers and nodes based on the application. The goal is to automatically determine the optimal NN configuration through reconfigurability to achieve maximum accuracy. Optimality is demonstrated through minimum average power, average delay, and area overhead, as well as maximum throughput and accuracy. Experimental results show that the proposed approach significantly reduces the optimized architecture search cost (the number of online training iterations) and associated average power consumption for successive datasets/applications. The method’s effectiveness is shown both quantitatively and qualitatively, verified against the MNIST and CIFAR-10 classification problems. Our reconfigurable method demonstrates stable accuracy of 98.97% and 98.95% compared to state-of-the-art NNs with fixed configurations (98.85% and 73.0% for MNIST and 93.47% and 70.21% for CIFAR-10, respectively). Additionally, the proposed method shows a 20.9% reduction in average power dissipation compared to state-of-the-art methods. Implemented and tested using VHDL and Altera FPGA, the results indicate resource utilization comparable with the state-of-the-art method. This reconfigurability is especially advantageous for Internet of Things applications where power efficiency and adaptability to different tasks are critical.

关键词： Hardware computer architecture Neural networks Biological neural networks Artificial neural networks Internet of Things Neurons Field programmable gate arrays Accuracy Resource management

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What Drives I4.0 Adoption? Establishing the Importance of Strategy

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IEEE engineering Management Review 2023年第3期51卷 33-41页

作者： Arcidiacono, Francesco Ancarani, Alessandro Di Mauro, Carmela Schupp, Florian University of Catania Department of Electrical Electronic and Computer Engineering Catania95124 Italy Schaeffler Automotive Bühl Bühl77815 Germany University of Catania Department of Civil Engineering and Architecture Catania95124 Italy Corvinus University of Budapest Corvinus Institute for Advanced Studies Budapest1093 Hungary Constructor University School of Business Social and Decision Sciences Bremen28759 Germany

This article investigates the adoption patterns of Industry 4.0 (I4.0) technologies in the automotive component sector and links I4.0 adoption to competitive strategies. Analysis of survey data concerning 288 automotive suppliers reveals that most firms currently focus on implementing real-time collection and analysis of production data (i.e., the Internet of Things, Big Data, and Artificial Intelligence), while other technologies, such as additive manufacturing and blockchain, are infrequently adopted. I4.0 adoption is associated with a simultaneous strategic emphasis on cost, quality, and delivery, thus suggesting automotive suppliers look at I4.0 as a mean to overcome performance tradeoffs. Findings also confirm that small- and medium-sized firms are less likely to adopt I4.0. Based on these results, implications for academics, managers, and policymakers are suggested. © 1973-2011 IEEE.

关键词： Supply chains

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Efficient simulations of Hartree-Fock equations by an accelerated gradient descent method

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Physical Review E 2024年第5期110卷 055304页

作者： Y. Ohno A. Del Maestro T. I. Lakoba Department of Mathematics and Statistics Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials & Manufacturing

We develop convergence acceleration procedures that enable a gradient descent-type iteration method to efficiently simulate Hartree-Fock equations for many particles interacting both with each other and with an external potential. Our development focuses on three aspects: (i) optimization of a parameter in the preconditioning operator; (ii) adoption of a technique that eliminates the slowest-decaying mode to the case of many equations (describing many particles); and (iii) a novel extension of the above technique that allows one to eliminate multiple modes simultaneously. We illustrate performance of the numerical method for the two-dimensional model of the first layer of helium atoms above a graphene sheet. We demonstrate that incorporation of aspects (i) and (ii) above into the “plain” gradient descent method accelerates it by at least two orders of magnitude, and often by much more. Aspect (iii)—the multiple-mode elimination—may bring further improvement to the convergence rate compared to aspect (ii), the single-mode elimination. Both single- and multiple-mode elimination techniques are shown to significantly outperform the well-known Anderson Acceleration. We believe that our acceleration techniques can also be employed by other iterative methods, especially those handling hard-core-type interaction of many particles.

关键词： First-principles calculations Hartree-Fock methods Numerical techniques

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