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Time and Space Efficient Multi-Model Convolution Vision Transformer for Tomato Disease Detection from Leaf Images with Varied Backgrounds

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Computers, Materials & Continua 2024年第4期79卷 117-142页

作者： Ankita Gangwar Vijaypal Singh Dhaka Geeta Rani Shrey Khandelwal Ester Zumpano Eugenio Vocaturo Department of Computer and Communication Engineering Manipal University JaipurJaipurIndia Department of IoT and Intelligent Systems Manipal University JaipurJaipurIndia Department of Computer Engineering ModellingElectronics and Systems(DIMES)University of CalabriaRende(Cosenza)Italy national Research Council Institute of Nanotechnology(NANOTEC)Rende(Cosenza)Italy

A consumption of 46.9 million tons of processed tomatoes was reported in 2022 which is merely 20%of the total *** increase of 3.3%in consumption is predicted from 2024 to *** are also rich in iron,potassium,antioxidant lycopene,vitamins A,C and K which are important for preventing cancer,and maintaining blood pressure and glucose ***,tomatoes are globally important due to their widespread usage and nutritional *** face the high demand for tomatoes,it is mandatory to investigate the causes of crop loss and minimize *** are one of the major causes that adversely affect crop yield and degrade the quality of the tomato *** leads to financial losses and affects the livelihood of ***,automatic disease detection at any stage of the tomato plant is a critical *** learning models introduced in the literature show promising results,but the models are difficult to implement on handheld devices such as mobile phones due to high computational costs and a large number of ***,most of the models proposed so far work efficiently for images with plain backgrounds where a clear demarcation exists between the background and leaf ***,the existing techniques lack in recognizing multiple diseases on the same *** address these concerns,we introduce a customized deep learning-based convolution vision transformer *** achieves an accuracy of 93.51%for classifying tomato leaf images with plain as well as complex backgrounds into 13 *** requires a space storage of merely 5.8 MB which is 98.93%,98.33%,and 92.64%less than stateof-the-art visual geometry group,vision transformers,and convolution vision transformermodels,*** training time of 44 min is 51.12%,74.12%,and 57.7%lower than the above-mentioned ***,it can be deployed on(Internet of Things)IoT-enabled devices,drones,or mobile devices to assist farmers in the real-time monitoring of tomato *** p

关键词： Tomato disease transformer deep learning mobile devices

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Emerging roles of microRNAs as diagnostics and potential therapeutic interest in type 2 diabetes mellitus

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World Journal of Clinical Cases 2024年第3期12卷 525-537页

作者： Dharmsheel Shrivastav Desh Deepak Singh Amity Institute of Biotechnology Amity University RajasthanJaipur 303002India

BACKGROUND Type 2 diabetes mellitus(T2DM)is a metabolic disease of impaired glucose *** high sugar levels lead to advanced glycation end products(AGEs),which affects several metabolic pathways by its receptor of advanced glycation end products(RAGE)and causes diabetic *** are small RNA molecules which regulate genes linked to diabetes and affect AGEs pathogenesis,and target tissues,influencing health and disease *** To explore miRNA roles in T2DM's metabolic pathways for potential therapeutic and diagnostic advancements in diabetes *** We systematically searched the electronic database PubMed using *** included free,full-length research articles that evaluate the role of miRNAs in T2DM and its complications,focusing on genetic and molecular disease *** assessing the full-length papers of the shortlisted articles,we included 12 research *** Several types of miRNAs are linked in metabolic pathways which are affected by AGE/RAGE axis in T2DM and its ***-96-5p,miR-7-5p,miR-132,has_circ_0071106,miR-143,miR-21,miR-145-5p,and more are associated with various aspects of T2DM,including disease risk,diagnostic markers,complications,and gene *** Targeting the AGE/RAGE axis,with a focus on miRNA regulation,holds promise for managing T2DM and its *** have therapeutic potential as they can influence the metabolic pathways affected by AGEs and RAGE,potentially reducing inflammation,oxidative stress,and vascular ***,miRNAs may serve as early diagnostic biomarkers for *** research in this area may lead to innovative therapeutic strategies for diabetes and its associated complications.

关键词： Type 2 diabetes mellitus MicroRNAs Advanced glycation end products Receptor for advanced glycation end products

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Energy-Efficient Implementation of BCD to Excess-3 Code Converter for Nano-Communication Using QCA Technology

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China Communications 2024年第6期21卷 103-111页

作者： Nuriddin Safoev Angshuman Khan Khudoykulov Zarif Turakulovich Rajeev Arya Department of Cybersecurity&Criminology Tashkent University of Information TechnologiesTashkent 100084Uzbekistan Department of Electronics and Communication Engineering University of Engineering and ManagementJaipurRajasthan 303807India Department of Cryptology Tashkent University of Information TechnologiesTashkent 100084Uzbekistan Department of Electronics and Communication Engineering National Institute of Technology PatnaBihar 800005India

Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique was adopted to investigate integration with other complicated *** a unique XOR gate,the recommended circuit’s cell complexity has been *** findings produced using the QCADesigner-2.0.3,a reliable simulation tool,prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed,the area occupied,and the latency as design *** addition,the popular tool QCAPro was used to estimate the energy dissipation of the proposed *** proposed technique reduces the occupied space by∼40%,improves cell complexity by∼20%,and reduces energy dissipation by∼1.8 times(atγ=1.5EK)compared to the current scalable *** paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.

关键词： BCD code converter Excess-3 nano communication QCA circuits

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Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida Syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature ***,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and ***,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer *** proposed model was evaluated on two challenging datasets,namely the Massey university Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning

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Sparse Reconstructive Evidential Clustering for Multi-View Data

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 459-473页

作者： Chaoyu Gong Yang You School of Computing National University of SingaporeSingapore

Although many multi-view clustering(MVC) algorithms with acceptable performances have been presented, to the best of our knowledge, nearly all of them need to be fed with the correct number of clusters. In addition, these existing algorithms create only the hard and fuzzy partitions for multi-view objects,which are often located in highly-overlapping areas of multi-view feature space. The adoption of hard and fuzzy partition ignores the ambiguity and uncertainty in the assignment of objects, likely leading to performance degradation. To address these issues, we propose a novel sparse reconstructive multi-view evidential clustering algorithm(SRMVEC). Based on a sparse reconstructive procedure, SRMVEC learns a shared affinity matrix across views, and maps multi-view objects to a 2-dimensional humanreadable chart by calculating 2 newly defined mathematical metrics for each object. From this chart, users can detect the number of clusters and select several objects existing in the dataset as cluster centers. Then, SRMVEC derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering. Ablation studies show the benefits of adopting the sparse reconstructive procedure and evidence theory. Besides,SRMVEC delivers effectiveness on benchmark datasets by outperforming some state-of-the-art methods.

关键词： Evidence theory multi-view clustering(MVC) optimization sparse reconstruction

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Span-based joint entity and relation extraction augmented with sequence tagging mechanism

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Science China(Information Sciences) 2024年第5期67卷 84-98页

作者： Bin JI Shasha LI Hao XU Jie YU Jun MA Huijun LIU Jing YANG College of Computer National University of Defense Technology

Span-based joint extraction simultaneously conducts named entity recognition(NER) and relation extraction(RE) in a text span form. However, since previous span-based models rely on span-level classifications, they cannot benefit from token-level label information, which has been proven advantageous for the task. In this paper, we propose a sequence tagging augmented span-based network(STSN), a spanbased joint model that can make use of token-level label information. In STSN, we construct a core neural architecture by deep stacking multiple attention layers, each of which consists of three basic attention *** the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE. Experimental results on three benchmark datasets show that STSN consistently outperforms the strongest baselines in terms of F1,creating new state-of-the-art results.

关键词： joint extraction named entity recognition relation extraction span sequence tagging mechanism

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High mechanical strength Si anode synthesis with interlayer bonded expanded graphite structure for lithium-ion batteries

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Journal of Energy Chemistry 2024年第6期93卷 253-263,I0007页

作者： Wenhui Lai Jong Hak Lee Lu Shi Yuqing Liu Yanhui Pu Yong Kang Ong Carlos Limpo Ting Xiong Yifan Rao Chorng Haur Sow Barbaros Ozyilmaz Department of Material Science&Engineering National University of SingaporeSingapore 117575Singapore Center for Advanced 2D Materials National University of SingaporeSingapore 117546Singapore Department of Physics National University of SingaporeSingapore 117551Singapore Institute forFunctional Intelligent Materials National University of SingaporeSingapore 117544Singapore

Despite advancements in silicon-based anodes for high-capacity lithium-ion batteries,their widespread commercial adoption is still hindered by significant volume expansion during cycling,especially at high active mass loadings crucial for practical *** root of these challenges lies in the mechanical instability of the material,which subsequently leads to the structural failure of the ***,we present a novel synthesis of a composite combining expanded graphite and silicon *** composite features a unique interlayer-bonded graphite structure,achieved through the application of a modified spark plasma sintering ***,this innovative structure not only facilitates efficient ion and electron transport but also provides exceptional mechanical strength(Vickers hardness:up to658 MPa,Young's modulus:11.6 GPa).This strength effectively accommodates silicon expansion,resulting in an impressive areal capacity of 2.9 mA h cm^(-2)(736 mA h g^(-1)) and a steady cycle life(93% after 100cycles).Such outsta nding performance is paired with features appropriate for large-scale industrial production of silicon batteries,such as active mass loading of at least 3.9 mg cm^(-2),a high-tap density electrode material of 1.68 g cm^(-3)(secondary clusters:1.12 g cm^(-3)),and a production yield of up to 1 kg per day.

关键词： Lithium-ion battery Silicon anode Spark plasma sintering Interlayer bonding Mechanical strength Tap density

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Tuning of Optical Properties via Annealing of Bismuth Ferrite (BiFeO3) Thin Films

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Open Journal of Composite Materials 2024年第3期14卷 124-131页

作者： Harish Kumar Meena Khushbu Meena Reena Verma Karishma Jain Sushil Kumar Jain Kedar Babu Sharma Balram Tripathi Department of Physics S S Jain Subodh PG College Jaipur India Department of Physics University of Rajasthan Jaipur India Department of Physics School of Physical Sciences Manipal University Jaipur Jaipur India

In this study we are reporting annealing induced optical properties of bismuth ferrite (BiFeO3) thin films deposited on glass substrate via spin coating at 5000 rpm. The structural, optical and surface morphology of BiFeO3 (BFO) thin films have been studied via X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Optical absorption (UV-Vis) and Photoluminescence (PL) spectroscopy. XRD spectra confirm annealing induced phase formation of BiFeO3 possessing a rhombohedral R3c structure. The films are dense and without cracks, although the presence of porosity in BFO/glass was observed. Moreover, optical absorption spectra indicate annealing induced effect on the energy band structure in comparison to pristine BiFeO3. It is observed that annealing effect shows an intense shift in the UV-Vis spectra as diffuse absorption together with the variation in the optical band gap. The evaluated optical band gap values are approximately equal to the bulk band gap value of BiFeO3.

关键词： Optical Properties Annealing Ferroelectrics Photo Luminescence BFO Thin Films

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Adaptive and augmented active anomaly detection on dynamic network traffic streams

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Frontiers of Information Technology & Electronic Engineering 2024年第3期25卷 446-460页

作者： Bin LI Yijie WANG Li CHENG national Key Laboratory of Parallel and Distributed Computing College of ComputerNational University of Defense TechnologyChangsha 410073China College of System Engineering National University of Defense TechnologyChangsha 410073China

Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model,and has been widely adopted in detecting network ***,existing methods cannot achieve desirable performance on dynamic network traffic streams because(1)their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and(2)their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on *** address these issues,we propose an active tree based model,adaptive and augmented active prior-knowledge forest(A3PF),for anomaly detection on network trafic streams.A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal *** one hand,to make the model adapt to the evolving stream,a novel adaptive query strategy is designed to sample informative instances from two aspects:the changes in dynamic data distribution and the uncertainty of *** the other hand,based on the similarity of instances in the neighborhood,we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances,which enables usage of the enormous unlabeled instances during model *** experiments on two benchmarks,CIC-IDS2017 and UNSW-NB15,demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve(AUC-ROC)(20.9%and 21.5%)and the area under the precision-recall curve(AUC-PR)(44.6%and 64.1%).

关键词： Active anomaly detection Network traffic streams Pseudo labels Prior knowledge of network attacks

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Multiscale observation in wide-spatial radar surveillance based on coherent FDA design

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Science China(Information Sciences) 2024年第2期67卷 232-252页

作者： Lei YU Feng HE Yi SU College of Electronics Science National University of Defense Technology

Wide-spatial radar surveillance missions are challenging tasks, requiring an increased power budget and agility in transmission aimed at extracting information from multiple targets in different environments. These requirements necessitate high transmitting degrees of freedom(DOF) to achieve the objective of multiscale observation for specific tasks in specialized regions of interest. Herein, we exploit the multiscale observation ability in wide-spatial radar surveillance based on frequency diverse array(FDA)radar. The proposed method facilitates spatial anisotropic control of multiple radar resources, including the transmitting waveforms, beampattern, and bandwidth. By utilizing a coherent FDA radar, we offer principles for the selection of baseband waveforms, in addition to the quantitative design of the beampattern gain and optimal bandwidth from the perspective of detection. The feasibility of the proposed method is validated through numerical experiments, thus indicating the potential in wide-spatial radar surveillance. Moreover,this work can be regarded as a preliminary attempt to gauge the efficacy of the computational array, a novel academic concept.

关键词： multi-scale observation wide-spatial surveillance frequency diverse array (FDA) beampattern synthesis frequency diversity gain computational array

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