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Journal articles on the topic 'TLSNN'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Winita, Sulandari, Subanar, Suhartono, Utami Herni, Hisyam Lee Muhammad, and Canas Rodrigues Paulo. "SSA-based hybrid forecasting models and applications." Bulletin of Electrical Engineering and Informatics 9, no. 5 (2020): 2178–88. https://doi.org/10.11591/eei.v9i5.1950.

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This study attempted to combine SSA (singular spectrum analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (two-level seasonal autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (two-level seasonal neural network) and TLCSNN (two-level complex seasonal neural network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural net

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Sulandari, Winita, Subanar Subanar, Suhartono Suhartono, Herni Utami, Muhammad Hisyam Lee, and Paulo Canas Rodrigues. "SSA-based hybrid forecasting models and applications." Bulletin of Electrical Engineering and Informatics 9, no. 5 (2020): 2178–88. http://dx.doi.org/10.11591/eei.v9i5.1950.

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This study attempted to combine SSA (Singular Spectrum Analysis) with other methods to improve the performance of forecasting model for time series with a complex pattern. This work discussed two modifications of TLSAR (Two-Level Seasonal Autoregressive) modeling by considering the SSA decomposition results, namely TLSNN (Two-Level Seasonal Neural Network) and TLCSNN (Two-Level Complex Seasonal Neural Network). TLSAR consisted of a linear trend, harmonic, and autoregressive component. In contrast, the two proposed hybrid approaches consisted of flexible trend function, harmonic, and neural net

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Jokhio, Rizwan, M. Munir Babar, and Pir M. Ajmal. "Modeling of Flow Rate at Sukkur Barrage using Artificial Neural Networks (ANNs)." Neutron 22, no. 2 (2023): 57–64. http://dx.doi.org/10.29138/neutron.v22i2.179.

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Modeling of flow discharge plays a significant role in effective planning, sustainable usage, development, and management of water resources in short (hourly) and long-term (monthly) temporal categories. Since the inception of managing water resources, various techniques such as conceptual, metric, and physical models have been introduced all of these require a large amount of data, labor, and expense to be incorporated to obtain reliable results, due to which Artificial Intelligence methods were introduced that require less amount of data, time, expense and as well as experience to model flow

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4

Blase, Wolfgang, and Gerhard Cordier. "Na5[TlSn3], eine Zintl-Phase mit P4analogen Tetraederanionen." Zeitschrift für Kristallographie 196, no. 1-4 (1991): 207–11. http://dx.doi.org/10.1524/zkri.1991.196.1-4.207.

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Setiawardhana, Rudy Dikairono, Djoko Purwanto, and Tri Arief Sardjono. "Navigasi Robot Penjaga Gawang Berdasarkan Prediksi Posisi dan Waktu Kedatangan Bola." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 3 (2020): 296–304. http://dx.doi.org/10.22146/.v9i3.295.

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Penelitian tentang robot sepakbola beroda telah banyak dikembangkan, terutama untuk proses akurasi pendeteksian dan klasifikasi objek. Makalah ini bertujuan untuk menyelesaikan permasalahan akurasi prediksi posisi bola dan waktu kedatangan di gawang serta navigasi robot untuk memblokade bola agar tidak melintasi gawang. Penelitian sebelumnya menggunakan metode Single Layer Neural Network (SLNN) dan Two Layer Neural Network (TLNN). Makalah ini membuat algoritme Modified Two-Layer Neural Network (MTLNN) untuk peningkatan akurasi prediksi posisi dengan waktu kedatangan bola dan Goalkeeper Robot N

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Huang, Daping, and John D. Corbett. "K4Au(TlSn3): A Novel Zintl Phase with an Anionic Chain." Inorganic Chemistry 37, no. 19 (1998): 5007–10. http://dx.doi.org/10.1021/ic980579q.

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HUANG, D., and J. D. CORBETT. "ChemInform Abstract: K4Au(TlSn3): A Novel Zintl Phase with an Anionic Chain." ChemInform 29, no. 49 (2010): no. http://dx.doi.org/10.1002/chin.199849003.

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Tziotzios, Georgios, Xanthoula Eirini Pantazi, Charalambos Paraskevas, et al. "Non-Destructive Quality Estimation Using a Machine Learning-Based Spectroscopic Approach in Kiwifruits." Horticulturae 10, no. 3 (2024): 251. http://dx.doi.org/10.3390/horticulturae10030251.

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The current study investigates the use of a non-destructive hyperspectral imaging approach for the evaluation of kiwifruit cv. “Hayward” internal quality, focusing on physiological traits such as soluble solid concentration (SSC), dry matter (DM), firmness, and tannins, widely used as quality attributes. Regression models, including partial least squares regression (PLSR), bagged trees (BTs), and three-layered neural network (TLNN), were employed for the estimation of the above-mentioned quality attributes. Experimental procedures involving the Specim IQ hyperspectral camera utilization and so

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Brahmaiah, Veeramosu Priyanka, Yarlagadda Padma Sai, and Mahendra N. Giri Prasad. "Accurate and Efficient Differentiation Between Normal and Epileptic Seizure of Eyes Using 13 Layer Convolution Neural Network." Traitement du Signal 38, no. 4 (2021): 1161–69. http://dx.doi.org/10.18280/ts.380427.

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Epileptic seizure is one which affects the normal brain activities of human being and considered to be a risky disease. The eye ball movement signals pattern plays a significant role in determining the epileptic seizure in precise manner. In addition to it, EOG signals has its influence in detecting epileptic seizure through assessment of eye ball movement signals precisely. Detecting Epilepsy using genetical based Convolutional Neural Network plays a major role in the previous research works. Conversely, the existence of background noise on eye ball signals may impact on the outcome failure.

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Liu, Ting-Yu, Peng Zhang, Juan Wang, and Yi-Feng Ling. "Compressive Strength Prediction of PVA Fiber-Reinforced Cementitious Composites Containing Nano-SiO2 Using BP Neural Network." Materials 13, no. 3 (2020): 521. http://dx.doi.org/10.3390/ma13030521.

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In this study, a method to optimize the mixing proportion of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites and improve its compressive strength based on the Levenberg-Marquardt backpropagation (BP) neural network algorithm and genetic algorithm is proposed by adopting a three-layer neural network (TLNN) as a model and the genetic algorithm as an optimization tool. A TLNN was established to implement the complicated nonlinear relationship between the input (factors affecting the compressive strength of cementitious composite) and output (compressive strength). An orthogonal e

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Zhang, Jianqing, Dongjing Wang, and Dongjin Yu. "TLSAN: Time-aware long- and short-term attention network for next-item recommendation." Neurocomputing 441 (June 2021): 179–91. http://dx.doi.org/10.1016/j.neucom.2021.02.015.

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12

Furukawa, Yoshihiro, and Daiyu Nakamura. "Thallium Nuclear Magnetic Relaxation in Solid Thallium (I) Thiocyanate TlSCN: Phase Transition and Ionic Motion." Zeitschrift für Naturforschung A 45, no. 9-10 (1990): 1211–16. http://dx.doi.org/10.1515/zna-1990-9-1022.

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Abstract The NMR spin-lattice relaxation time (T1) and linewidth parameter (T2*) of 203Tl and 205T1 in solid T1SCN were measured from 290 K up to the melting point (Tm = 507 K). The nonexponential magnetization recovery of could be characterized by a short (T1s) and a long (T11) component. T11 showed a T-2 dependence below ca. 350 K in the orthorhombic phase (T<Tc = 371 K) and a minimum in the tetragonal phase (Tc<T<Tm), which were interpreted in terms of lattice vibrations and head-to-tail flips of the linear SCN- ions, respectively. A broad minimum of 77 around 360 K was explained i

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Yan, Shubin, Pengwei Liu, Zhanbo Chen, et al. "High-Property Refractive Index and Bio-Sensing Dual-Purpose Sensor Based on SPPs." Micromachines 13, no. 6 (2022): 846. http://dx.doi.org/10.3390/mi13060846.

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A high-property plasma resonance-sensor structure consisting of two metal-insulator-metal (MIM) waveguides coupled with a transverse ladder-shaped nano-cavity (TLSNC) is designed based on surface plasmon polaritons. Its transmission characteristics are analyzed using multimode interference coupling mode theory (MICMT), and are simulated using finite element analysis (FEA). Meanwhile, the influence of different structural arguments on the performance of the structure is investigated. This study shows that the system presents four high-quality formants in the transmission spectrum. The highest s

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Cao, Dawei, Ziyang Liu, Hechuan Lin, Gaoyang Chen, Xinzhong Zhu, and Huiying Xu. "Diagnosis and staging of cervical cancer using label-free surface-enhanced Raman spectroscopy and BWRPCA-TLNN model." Vibrational Spectroscopy 128 (September 2023): 103587. http://dx.doi.org/10.1016/j.vibspec.2023.103587.

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Rafi, Taki Hasan, and Rehnuma Karim Rinky. "Time Series Analysis- A Comparative Analysis Between ANN and RNN." DIU Journal of Science & Technology 15, no. 2 (2024): 20–24. https://doi.org/10.5281/zenodo.13826979.

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Time series analysis is a significant undertaking in time series data mining and has pulled in extraordinary interests and huge endeavours during the most recent decades. However, data handling is a prior task in time series data. The main objective of time series analysis is to get intuition of the data. In recent years, AI models come with some enormous results in time series analysis. The motivation of this study is to determine a suitable artificial intelligence-based model for time-series related analysis. In this comparative analysis, authors utilize a publically accessible dataset to co

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16

Ganguli, A. K., V. Manivannan, A. K. Sood та C. N. R. Rao. "New family of thallium cuprate superconductors not containing calcium or barium: TlSrn+1−xLnxCunO2n+3+δ(Ln=La, Pr, or Nd)". Applied Physics Letters 55, № 25 (1989): 2664–66. http://dx.doi.org/10.1063/1.102255.

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17

Ning, Meng, Fan Zhou, Wei Wang, Shaoqiang Wang, Peiying Zhang, and Jian Wang. "AbFTNet: An Efficient Transformer Network with Alignment before Fusion for Multimodal Automatic Modulation Recognition." Electronics 13, no. 18 (2024): 3725. http://dx.doi.org/10.3390/electronics13183725.

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Multimodal automatic modulation recognition (MAMR) has emerged as a prominent research area. The effective fusion of features from different modalities is crucial for MAMR tasks. An effective multimodal fusion mechanism should maximize the extraction and integration of complementary information. Recently, fusion methods based on cross-modal attention have shown high performance. However, they overlook the differences in information intensity between different modalities, suffering from quadratic complexity. To this end, we propose an efficient Alignment before Fusion Transformer Network (AbFTN

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18

Kadhim, Dhirgaam A., Mithaq N. Raheema, and Jabbar S. Hussein. "Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1735–43. https://doi.org/10.11591/ijai.v12.i4.pp1735-1743.

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Control strategies of smart hand prosthesis-based myoelectric signals in recent years don't provide the patients with the sensation of biological control of prostheses hand fingers. Therefore, in current work hyperparameters optimization in machine learning algorithm and hand gesture recognition techniques were applied to the myoelectric signal-based on residual muscles contraction of the amputees corresponding to intact forearm limb movement to improve their biological control. In this paper, myoelectric signals are extracted using the MYO armband to recognize ten gestures from ten volunteers

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19

Sun, Cunwei, Yuxin Yang, Chang Wen, Kai Xie, and Fangqing Wen. "Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning." Sensors 18, no. 7 (2018): 2399. http://dx.doi.org/10.3390/s18072399.

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The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint

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20

Pingak, Redi Kristian, Soukaina Bouhmaidi, Amine Harbi, et al. "A DFT investigation of lead-free TlSnX₃ (X = Cl, Br, or I) perovskites for potential applications in solar cells and thermoelectric devices." RSC Advances 13, no. 48 (2023): 33875–86. http://dx.doi.org/10.1039/d3ra06685a.

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21

Marjaoui, Adil, Mohamed Ait Tamerd, Hamza Rghioui, Mustapha Diani, and Mohamed Zanouni. "First-Principles Investigations of Mechanical, Electronic and Optical Properties of Heavy Thallium Perovskites TlSnX<sub>3</sub> (X = F, Cl, Br and I)." Advanced Materials Research 1183 (March 31, 2025): 3–13. https://doi.org/10.4028/p-9bzecc.

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In this paper, the mechanical, elastic, electronic and optical properties of thallium based-perovskites TlSnX3 (X = F, Cl, Br and I) were investigated using the first-principles calculations. The elastic parameters calculations show that the perovskites are ductile, anisotropic, and mechanically stables. The cohesive energy calculations indicate that the evaluated perovskites are thermodynamically stable. Moreover, the band calculations with HSE06 method reveal that all perovskites TlSnX3 (X = F, Cl, Br and I) present a semiconductor feature. Further, the optical properties such as reflectivit

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22

Kadhim, Dhirgaam A., Mitha N. Raheema, and Jabbar S. Hussein. "Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (2023): 1735. http://dx.doi.org/10.11591/ijai.v12.i4.pp1735-1743.

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<p>Control strategies of smart hand prosthesis-based myoelectric signals in<br />recent years don't provide the patients with the sensation of biological<br />control of prostheses hand fingers. Therefore, in current work<br />hyperparameters optimization in machine learning algorithm and hand<br />gesture recognition techniques were applied to the myoelectric signal-based<br />on residual muscles contraction of the amputees corresponding to intact<br />forearm limb movement to improve their biological control. In this paper,<br />myoelectric sig

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23

Steed, Nell Rose E., Omar Kasshout, Katja Bryant, et al. "Abstract T MP51: Concordance of Emergency Medical Services and Neurology Times Last Seen Normal in Acute Ischemic Stroke Patients." Stroke 45, suppl_1 (2014). http://dx.doi.org/10.1161/str.45.suppl_1.tmp51.

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Background and Purpose: The establishment of a patient’s time last seen normal (TLSN) is an important step for medical decision making in the current treatment paradigm of acute ischemic stroke patients. While both emergency medical services (EMS) and neurologists evaluate stroke patients, there is limited data on the concordance of TLSN as determined by the two groups. The purpose of our study was to identify the frequency of clinically significant differences between reported TLSN by EMS and neurology providers. Methods: We performed a retrospective chart review of acute ischemic stroke pati

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"Forecasting Reference Evapotranspiration Using Time Lagged Recurrent Neural Network." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 16 (October 23, 2020). http://dx.doi.org/10.37394/232015.2020.16.72.

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The aim of this study is to employ a Time Lagged Recurrent Neural Network (TLRNN) model for forecasting near future reference evapotranspiration (ETo) values by using climate data taken from meteorological station located in Velestino, a village near the city of Volos, in Thessaly, centre of Greece. TLRNN is Multilayer Perceptron Neural Network (MLP-NN) with locally recurrent connections and short-term memory structures that can learn temporal variations from the dataset. The network topology is using input layer, hidden layer and a single output with the ETo values. The network model was trai

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Meng, Xiaojing, Wenjie Zhuo, Peng Ge, et al. "Diagnostic model optimization method for ADHD based on brain network analysis of resting-state fMRI images and transfer learning neural network." Frontiers in Human Neuroscience 16 (October 14, 2022). http://dx.doi.org/10.3389/fnhum.2022.1005425.

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Introduction: Attention deficit and hyperactivity disorder (ADHD) is a common inherited disease of the nervous system whose cause(s) and pathogenesis remain unclear. Currently, the diagnosis of ADHD is mainly based on clinical experience and guidelines that have laid out some diagnostic standards. Our study aimed to apply a learning-based classification method to assist the ADHD diagnosis based on high-dimensional resting-state fMRI.Methods: Our study selected the ADHD-200 Peking dataset of resting-state fMRI, which has an ADHD patient (n = 142) group and a typically developing control (TDC) h

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26

Cordier, Gerhard, and Wolfgang Blase. "Na5[TlSn3], eine Zintl-Phase mit P4 analogen Tetraederanionen." Zeitschrift für Kristallographie - Crystalline Materials 196, no. 1-4 (1991). http://dx.doi.org/10.1524/zkri.1991.196.14.207.

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27

Zhang, Dawei, Peijuan Xu, Yiyang Tian, Chen Zhong, and Xu Zhang. "Ballasted Track Behaviour Induced by Absent Sleeper Support and its Detection Based on a Convolutional Neural Network Using Track Data." Urban Rail Transit, March 20, 2023. http://dx.doi.org/10.1007/s40864-023-00187-0.

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AbstractWith development of the heavy-haul railway, the increased axle load and traction weight bring a significant challenge for the service performance and safety maintenance of the railway track. Conducting defect recognition on concrete sleepers and ballast using big data is vital. This paper focused on the detection of absent sleeper support in a ballasted track with an emphasis on the integration of model-based and data-driven methods. To this end, a mathematical model consisting of the wagon, track and wheel–rail contact subsystems was first established to acquire the necessary raw data

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28

Liu, Weihong, Yanbo Zhao, Shuai Zhang, Duan Xie, and Haoqian Wu. "Behaviour Prediction of Via‐Holes Transition Based on Transfer Learning." IET Microwaves, Antennas & Propagation 19, no. 1 (2025). https://doi.org/10.1049/mia2.70035.

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ABSTRACTVia‐holes transition is an important component in multi‐layer microwave and millimetre wave circuit systems, directly affecting signal transmission performance. In order to improve the millimetre wave performance of via‐holes transition, the electromagnetic design automation software has been used to optimise the circuits design, which could consume a plenty of computer resources. In recent years, deep neural network (DNN) has been widely applied in the research of microwave component and is expected to solve this challenging and time‐consuming problem. Employing large labelled dataset

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29

Tan, Linlin, Yinghong Cao, Santo Banerjee, and Jun Mou. "Multi-medical image protection: compression–encryption scheme based on TLNN and mask cubes." Journal of Supercomputing 81, no. 1 (2024). http://dx.doi.org/10.1007/s11227-024-06624-6.

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30

Dixit, Abhishek, and Ashish Mani. "Smote-Tlnn-Depso: Sampling Technique for Noisy and Borderline Examples Problems in Imbalanced Classification." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4207517.

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31

Nguyễn, Thị Thu Hương. "CÁC BIỆN PHÁP NGĂN CHẶN THUỐC LÁ ĐIỆN TỬ". Tạp chí Y học Việt Nam 543, № 2 (2024). http://dx.doi.org/10.51298/vmj.v543i2.11480.

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Sử dụng thuốc lá gây ra gánh nặng về bệnh tật, tử vong và kinh tế không chỉ với người sử dụng mà còn với cả gia đình và toàn xã hội. Theo WHO, sử dụng thuốc lá là nguyên nhân gây ra 25 nhóm bệnh khác nhau bao gồm nhiều nhóm bệnh nguy hiểm như ung thư, các bệnh tim mạch, các bệnh hô hấp và ảnh hưởng tới sức khỏe sinh sản ở cả 2 giới. Ở Việt Nam mỗi năm có ít nhất là 40.000 người tử vong vì các bệnh liên quan thuốc lá, trong đó đột quỵ, mạch vành, bệnh phổi tắc nghẽn mạn tính, ung thư phổi là những nguyên nhân chính. Đặc biệt, thuốc lá điện tử còn có nguy cơ gây ra nhiều ảnh hưởng cấp tính nguy

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Cao, Dawei, Hechuan Lin, Ziyang Liu, et al. "PCA-TLNN-based SERS analysis platform for label-free detection and identification of cisplatin-treated gastric cancer." Sensors and Actuators B: Chemical, October 2022, 132903. http://dx.doi.org/10.1016/j.snb.2022.132903.

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Bakar, Abu, Muhammad Ahmed, Yasmin Khairy, E. O. Shalenov, M. M. Seisembayeva, and K. N. Dzhumagulova. "Structural, Elastic, Mechanical, Electronic and Optical Properties of $${{{{\text {TlSnX}}}}}_{3}\, ({{\text {X}}}={{\text {Cl}}}, {{\text {Br}}}, {{\text {I}}})$$ for Sustainable Energy Applications." Journal of Inorganic and Organometallic Polymers and Materials, July 31, 2024. http://dx.doi.org/10.1007/s10904-024-03269-z.

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Senanayaka, Ayantha, Abdullah Al Mamun, Glenn Bond, et al. "Similarity-based Multi-source Transfer Learning Approach for Time Series Classification." International Journal of Prognostics and Health Management 13, no. 2 (2022). http://dx.doi.org/10.36001/ijphm.2022.v13i2.3267.

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This study aims to develop an effective method of classification concerning time series signals for machine state prediction to advance predictive maintenance (PdM). Conventional machine learning (ML) algorithms are widely adopted in PdM, however, most existing methods assume that the training (source) and testing (target) data follow the same distribution, and that labeled data are available in both source and target domains. For real-world PdM applications, the heterogeneity in machine original equipment manufacturers (OEMs), operating conditions, facility environment, and maintenance record

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"The LSU School Adopts New Name: The School of Renewable Natural Resources." Society of Wetland Scientists Bulletin 19, no. 2 (2002): 28. http://dx.doi.org/10.1672/0732-9393(2002)019[0028:tlsann]2.0.co;2.

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Zhang, Huizhong, Fanrong Meng, and Qinyong Wang. "Computer Network Security System Optimization Based on Improved Neural Network Algorithm and Data Search." Journal of Cyber Security and Mobility, February 28, 2025, 75–100. https://doi.org/10.13052/jcsm2245-1439.1414.

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Frequent hacker attacks and network viruses pose a serious threat to network security, and traditional static and passive defense technologies can no longer meet the high security demands of networks. This paper proposes a computer network security intrusion detection algorithm based on the optimization of convolutional neural networks through transfer learning. Initially, a one-dimensional convolutional neural network (1D-CNN(T)) is trained to form a stable model. Subsequently, in the transfer learning phase, a target function is constructed, experimental data is acquired, and 1D-CNN(E) is tr

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