Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis by S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew Jeyabose

“…Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. …”
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An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal by Mujeev Khan, Abdul Moiz, Gani Nawaz Khan, Mohd Wajid, Mohammed Usman, Jabir Ali

“…Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. …”
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Dataset of apples for grading by sweetness, ripeness and varietyMendeley Data by Shilpa Gaikwad, Sonali Kothari, Ignisha Rajathi G

“…This dataset of ours can be used by researchers to develop and test algorithms to classify apples and estimate their ripeness and the presence of diseases. …”
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Trusted measure model for the communication network system by Senxin ZHOU, Jianghong HAN, Yaohui ZHANG, Chao LI

“…The trusted measure model for communication was proposed by computing its value of security,control and survivability with continuous-time reward Markov method.The“state bomb”problem in traditional state analysis method was solved by the way of structural equation solution.The generic function optimization algorithm was used to reduce computational complexity.Simulation experiments show that the model is correct and feasible.…”
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AI-powered blockchain technology in industry 4.0, a review by Mohsen Soori, Roza Dastres, Behrooz Arezoo

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SimdMSM: SIMD-accelerated Multi-Scalar Multiplication Framework for zkSNARKs by Rui Jiang, Cong Peng, Min Luo, Rongmao Chen, Debiao He

“…First, we propose a SIMD-accelerated MSM computing architecture with no write conflicts and constant memory overheads. …”
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A Multi-Phase DRL-Driven SDN Migration Framework Addressing Budget, Legacy Service Compatibility, and Dynamic Traffic by Kai Yuan Tan, Saw Chin Tan, Teong Chee Chuah

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Design and assessment of AI-based learning tools in higher education: a systematic review by Jihao Luo, Chenxu Zheng, Jiamin Yin, Hock Hai Teo

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Research on Book Recommendation Integrating Book Category Features and User Attribute Information by Yan Chen, Eric Blancaflor, Mideth Abisado

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A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling by Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang

“…Firstly, addressing the limitations of density maps in localization performance in existing algorithms, we optimize the generation method of FIDT maps, decoupling the counting and localization tasks. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

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A Comprehensive Survey on Machine and Deep Learning for Optical Communications by Mohammad Ali Amirabadi, S. Alireza Nezamalhosseini, Mohammad Hossein Kahaei, Lawrence R. Chen

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Power Line Polarimetric Imaging by Helicopter Radars: Modeling and Experimental Validation by Masha Bortsova, Hlib Cherepnin, Volodymyr Kosharskyi, Volodymyr Ponomaryov, Anatoliy Popov, Sergiy Sadovnychiy, Beatriz Garcia-Salgado, Eduard Tserne

“…The effectiveness of the proposed algorithm was tested using computer modeling methods. …”
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Experimental Application of Predictive Controllers by C. H. F. Silva, H. M. Henrique, L. C. Oliveira-Lopes

“…The basic characteristic of these algorithms is the formulation of an optimization problem in order to compute the sequence of control moves that minimize a performance function on the time horizon with the best information available at each instant, taking into account operation and plant model constraints. …”
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Application and prospects of machine learning for rockfalls, landslides and debris flows by Jiazhu WANG, Yongbo TIE, Yongjian BAI, Yanchao GAO, Donghui WANG, Mingzhi ZHANG

“…Artificial intelligence, notably machine learning algorithms, has emerged as a hot point in this domain. …”
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Efficient quantum circuits based on the quantum natural gradient by Ananda Roy, Sameer Erramilli, Robert M. Konik

“…Efficient preparation of arbitrary entangled quantum states is crucial for quantum computation. This is particularly important for noisy intermediate-scale quantum simulators relying on variational hybrid quantum-classical algorithms. …”
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Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model by Binqing Cai, Zhukai Ye, Shiwei Chen, Xun Liang

“…To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. …”
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Marine Soundscape Monitoring Enabled by Artificial Intelligence-Aided Integrated Distributed Sensing and Communications by Juan M. Marin, Wahyu Hendra Gunawan, Alaaeddine Rjeb, Islam Ashry, Baoshi Sun, Talha Ariff, Chun Hong Kang, Tien Khee Ng, Shinkyu Park, Carlos M. Duarte, Boon S. Ooi

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Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models by Hülya Yilmaz Başer, Turan Evran, Mehmet Akif Cifci

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