PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…PLL-VO system are evaluated in multiple datasets under various lighting conditions, demonstrating a 6.3% reduction in absolute trajectory error for pose estimation compared to state-of-the-art (SOTA) algorithms, the average computation time for visual odometry (43 ms) shows a 29.74% decrease compared to the state-of-the-art (SOTA) algorithms.…”
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An AIoT Architecture for Structural Testing: Application to a Real Aerospace Component (Embraer E2 Model Aircraft Flag Track) by Pablo Venegas, Unai Virto, Isidro Calvo, Oscar Barambones

“…In order to illustrate the integration of different AI techniques in the AIoT architecture, ARIMA and LSTM algorithms were used to analyze the data captured with three sensors. …”
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Image Super Resolution Reconstruction Based on Multi-Level Residual Skip Connection Network by WANG Xiaoyu, LU Jianyu, WEI Yuxin, YU Yue

“…Compared with other commonly used super-resolution algorithms, this algorithm can better retain texture details during image reconstruction, resulting in clearer image details, better visual effects and effective reduction of the number of parameters in the network. …”
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Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

“…It is significant that the reduction of number of seed set selections T not only keeps the quality of solutions, but lowers the algorithm’s computational cost.…”
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Polychromatic Iterative Statistical Material Image Reconstruction for Photon-Counting Computed Tomography by Thomas Weidinger, Thorsten M. Buzug, Thomas Flohr, Steffen Kappler, Karl Stierstorfer

“…Parallel updates can compensate for the rather slow convergence that is intrinsic to statistical algorithms. We investigate the accuracy of the algorithm for ideal photon-counting detectors. …”
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Coverage optimization and node minimization in WSNs: an enhanced hybrid PSO approach with spatial position encoding by Yinghua Tong, Lianhai Lin, Liqin Tian, Zhigang Wang, Wenxing Wu, Junyi Wu

“…Results demonstrate improvements in coverage optimization and reduction of node deployment compared to existing methods. …”
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Improved speech enhancement based on spectral subtraction and auditory masking effect by ZHAO Xiao-qun, HUANG Xiao-shan

“…Taking into account the masking properties of human auditory system and the uncertainty of the speech pres-ence,the novel algorithm modifies the log-spectral amplitude estimation and the speech absence probability（SAP） pa-rameter.Then,the smoothing parameters in noise estimation and prior SNR estimation are adaptively adjusted to modi-fied SAP parameter which reflects the degree of stationary of the measured signal.The change of smoothing parameters allows for automatic adaptation with various noisy environments and obtains the best trade-off.Experimental results demonstrate that the proposed algorithm has better performance of speech articulation without any perceptional musical-ity,compared to another modified spectral subtraction algorithms under the same level of noise reduction,and this supe-riority is more significant at very low SNR.…”
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Normal forms for retarded functional differential equations associated with zero-double-Hopf singularity with $1:1$ resonance by Houssem Achouri

“…Subsequently, by utilizing center manifold reduction and normal form techniques, we derive a reduced form of parameterized retarded differential systems up to third-order terms.…”
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Anomaly detection of large scale network based on data streams by ZHENG Jun, HU Ming-zeng, YUN Xiao-chun, ZHENG Zhong

“…The anomaly detection algorithms of the large scale network（LSN） were required to analysis the vast network traffic of G bit level in real-time and on-the-fly.A novel monitoring mechanism of LSN anomaly detection based on the data stream approach was proposed.The main contributions included: the sketch data structure and the frequent sketch algorithm of data streams were designed for anomaly detection of LSN.Optimized query methods were designed for customizing the security monitoring and detection policy with the correlations of multi data streams.The data reduction was proposed to make it possible that the whole network traffic character could be got using a few of special data streams.The experiments of the real networking environments validate the effectivity of LSN anomaly detection methods.…”
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Block diagonalization of (p, q)-tridiagonal matrices by Manjunath Hariprasad

“…We also examine special cases such as bidiagonal and triangular block reductions along with supporting algorithms and numerical examples.…”
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Improved speech enhancement based on spectral subtraction and auditory masking effect by ZHAO Xiao-qun, HUANG Xiao-shan

“…Taking into account the masking properties of human auditory system and the uncertainty of the speech pres-ence,the novel algorithm modifies the log-spectral amplitude estimation and the speech absence probability（SAP） pa-rameter.Then,the smoothing parameters in noise estimation and prior SNR estimation are adaptively adjusted to modi-fied SAP parameter which reflects the degree of stationary of the measured signal.The change of smoothing parameters allows for automatic adaptation with various noisy environments and obtains the best trade-off.Experimental results demonstrate that the proposed algorithm has better performance of speech articulation without any perceptional musical-ity,compared to another modified spectral subtraction algorithms under the same level of noise reduction,and this supe-riority is more significant at very low SNR.…”
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New Formulae for the High-Order Derivatives of Some Jacobi Polynomials: An Application to Some High-Order Boundary Value Problems by W. M. Abd-Elhameed

“…A numerical example is presented hoping to ascertain the validity and the applicability of the proposed algorithms.…”
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Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
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Assessing perioperative risks in a mixed elderly surgical population using machine learning: A multi-objective symbolic regression approach to cardiorespiratory fitness derived fro... by Pietro Arina, Davide Ferrari, Maciej R Kaczorek, Nicholas Tetlow, Amy Dewar, Robert Stephens, Daniel Martin, Ramani Moonesinghe, Mervyn Singer, John Whittle, Evangelos B Mazomenos

“…Preoperative cardiorespiratory fitness data from cardiopulmonary exercise testing (CPET), demographic and clinical data were extracted and integrated into advanced machine learning (ML) algorithms. Multi-Objective-Symbolic-Regression (MOSR), a novel algorithm utilizing Genetic Programming to generate mathematical formulae for learning tasks, was employed to predict patient morbidity at Postoperative Day 3, as defined by the PostOperative Morbidity Survey (POMS). …”
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Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment by Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar

“…The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. …”
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Artificial intelligence survival models for identifying relevant risk factors for incident diabetes in Azar cohort population by Neda Gilani, Mohammadhossein Somi, Farzaneh Hamidi, Pasqualina Santaguida, Elnaz Faramarzi, Reza Arabi Belaghi

“…In contrast, the RF analysis identified 21 important variables predicting a higher probability of having diabetes. …”
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Applications of artificial intelligence and computational intelligence in hydraulic optimization of centrifugal pumps: a comprehensive review by Yuanhui Xu, Xingcheng Gan, Ji Pei, Wenjie Wang, Jia Chen, Shouqi Yuan

“…Ultimately, such progress will contribute to more sustainable and reliable energy utilisation in diverse industrial applications.Highlights Comprehensive review of AI and CI applications in centrifugal pump hydraulic optimisation.Analysis of machine learning methods for predictive modelling and optimisation.Insights into integrating intelligent algorithms with CFD for high-dimensional, multi-objective optimisation.Identification of future research directions to enhance precision and efficiency in pump design methodologies.…”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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Frequency-Domain Finite Element Modeling of Seismic Wave Propagation Under Different Boundary Conditions by Ying Zhang, Haiyang Liu, Shikun Dai, Herui Zhang

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Assessment of MGMT promoter methylation status in glioblastoma using deep learning features from multi-sequence MRI of intratumoral and peritumoral regions by Xuan Yu, Jing Zhou, Yaping Wu, Yan Bai, Nan Meng, Qingxia Wu, Shuting Jin, Huanhuan Liu, Panlong Li, Meiyun Wang

“…The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. …”
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