Enhanced early detection of dysarthric speech disabilities using stacking ensemble deep learning model by Jagat Chaitanya Prabhala, Ravi Ragoju, Venkatanareshbabu Kuppili, Christophe Chesneau

“…Unlike conventional stacking methods that use fixed meta-classifiers, this study employs a Genetic Algorithm (GA)-based optimization strategy to dynamically determine optimal weight contributions of the base models, enhancing classification robustness and adaptability.The preprocessing pipeline converts speech signals from the time domain to the frequency domain by using a Short-Time Fourier Transform (STFT). …”
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Research Progress and Prospect of Multi-robot Collaborative SLAM in Complex Agricultural Scenarios by MA Nan, CAO Shanshan, BAI Tao, KONG Fantao, SUN Wei

“…Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. …”
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Enhancing Fault Detection in AUV-Integrated Navigation Systems: Analytical Models and Deep Learning Methods by Huibao Yang, Bangshuai Li, Xiujing Gao, Bo Xiao, Hongwu Huang

“…Specifically, the particle swarm optimization (PSO) algorithm was employed to optimize the hyperparameters of a long short-term memory (LSTM) neural network, leading to the development of a PSO-LSTM fault detection model. …”
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Microgrid Energy Management Considering Energy Storage Degradation Cost by Yiming Zhao, Hongrui Li, Changsheng Wan, Dong Du, Bo Chen

“…The improved sine cosine algorithm (SCA) is used to simulate the energy output optimization of various distributed generators in the microgrid. …”
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Machine learning-based prediction method for open-pit mining truck speed distribution in manned operation by Changyou XU, Gang CHEN, Qiuxia ZHANG, Bo WANG, Hongwang ZHANG, Hongrui LI, Weiwei QIN, Muyang LI

“…Among these models, the Random Forest-based model exhibited lower mean squared error and a higher coefficient of determination, outperforming the XGBoost-based model. …”
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Enhanced Conformer-Based Speech Recognition via Model Fusion and Adaptive Decoding with Dynamic Rescoring by Junhao Geng, Dongyao Jia, Zihao He, Nengkai Wu, Ziqi Li

“…In this paper, we propose improvements to the model structure fusion and decoding algorithms. …”
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A systematic review on the integration of explainable artificial intelligence in intrusion detection systems to enhancing transparency and interpretability in cybersecurity by Vincent Zibi Mohale, Ibidun Christiana Obagbuwa

“…Explainable Artificial Intelligence (XAI) offers a promising solution by providing interpretability and transparency, enabling security professionals to understand better, trust, and optimize IDS models. This paper presents a systematic review of the integration of XAI in IDS, focusing on enhancing transparency and interpretability in cybersecurity. …”
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Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning by Yuanxi Han, Liang Li, Siyuan Jiang, Pengpeng Sun, Wenliang Wu, Zhendong Liu

“…After data preprocessing, all models' accuracies and stability improved. Specifically, the logistic regression model was best for jerky identification, with 85.78 %–100.00 % accuracy. …”
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Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains by Joonyeol Yang , Minhyeong Kang , Seulchan Lee, Sanghyun Kim

“…This article proposes the Hybrid A*-guided Model Predictive Path Integral (MPPI) algorithm augmented with traversability estimation to address the challenges of path planning on uneven terrains. …”
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FPGA Acceleration With Hessian-Based Comprehensive Intra-Layer Mixed-Precision Quantization for Transformer Models by Woohong Byun, Jongseok Woo, Saibal Mukhopadhyay

“…It is implemented on a single Xilinx ZCU102 FPGA board, operating at 200MHz with a power consumption of 15.08W during inference on the 110-million-parameter BERT-Base and 345-million-parameter GPT-2 Medium transformer models. Coupled with the proposed algorithm and dataflow optimization, it enables on-chip storage of all necessary parameters, minimizing off-chip memory access. …”
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The synergistic effects of circuit training and diet on weight loss, body fat percentage, and BMI in obese Indonesian females: a randomized controlled trial by Moch Yunus, Nanang Tri Wahyudi, Ronal Surya Aditya, Alifia Candra Puriastuti, Daifallah M. Alrazeeni, Gadis Meinar Sari, Farsya Hidayah Septiananda, Lintang Nirmalasari Gemalochaya Manggolono

“…Conclusion: Smartwatch apps have great potential in optimizing cardiovascular fitness training. Future research needs to focus on improving sensor accuracy, refining AI interventions, and long-term user engagement to maximize their impact.…”
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An echo state network based on enhanced intersecting cortical model for discrete chaotic system prediction by Xubin Wang, Pei Ma, Jing Lian, Jizhao Liu, Yide Ma

“…This efficiency gain during optimization is attributed to the model's intrinsic stability, which reduces the number of divergent trials encountered by the search algorithm.DiscussionThe results indicate that the ESN-EICM framework is a viable method for the prediction of the tested chaotic time series. …”
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A hybrid deep learning-based intrusion detection system for EV and UAV charging stations by Rosebell Paul, Mercy Paul Selvan

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A Quality Soft Sensing Method Designed for Complex Multi-process Manufacturing Procedures by Kaixiang PENG, Xin QIN, Jiahao WANG, Hui YANG

“…Additionally, an iterative optimization search is performed to determine the final optimal feature subset, thus deriving the auxiliary variable set for modeling. …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…The findings specify a substantial improvement in accuracy in prediction, emphasizing how the model can be used for improving solar power grid integration and energy for improved RE management.…”
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A robust and statistical analyzed predictive model for drug toxicity using machine learning by Deepak Rawat, Rohit Bajaj, Rachit Manchanda, Ankush Mehta, Prabhu Paramasivam, Suraj Kumar Bhagat, Abinet Gosaye Ayanie

“…Artificial intelligence and machine learning provide a platform to study toxicity prediction more accurately with a reduced time span. An optimized ensembled model is used to contrast the results of seven machine learning algorithms and three deep learning models with regard to state-of-the-art parameters. …”
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A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in M... by Abhijeet Das

“…Our research in Mahanadi River Basin, Odisha, presents an enhanced methodology based on data, specifically designed to be beneficial for Water Quality (WQ) based on Synthetic Pollution Index (SPI) and machine learning models such as Long Short-Term Memory (LSTM) and Sparrow Search Algorithm (SSA), for its analysis and interpretation of extensive, intricate data sets on water quality, as well as the allocation of pollution sources or contributing elements, in order to improve knowledge of the water quality and the planning of monitoring networks for efficient water resource management. …”
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A prediction method for radiation proctitis based on SAM-Med2D model by Ning Zhang, Haifeng Ling, Wenyu Zhang, Mei Zhang

“…Accurate diagnosis are crucial for optimizing treatment strategies and improving patient outcomes. …”
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Hand gestures classification of sEMG signals based on BiLSTM-metaheuristic optimization and hybrid U-Net-MobileNetV2 encoder architecture by Khosro Rezaee, Safoura Farsi Khavari, Mojtaba Ansari, Fatemeh Zare, Mohammad Hossein Alizadeh Roknabadi

“…Bayesian optimization is employed as the metaheuristic approach to optimize the BiLSTM model’s architecture. …”
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Efficient and Stable Learning for Distribution Network Operation: A Model-Based Reinforcement Learning Approach by Dong Yan, Zhan Shi, Xinying Wang, Yiying Gao, Tianjiao Pu, Jiye Wang

“…This study introduces a reward evaluation system to assess the effectiveness of various strategies and proposes an enhanced algorithm based on the Model-based DRL approach. Incorporating a state transition model, the proposed algorithm augments data and enhances dynamic deduction, improving training efficiency. …”
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