Model train environment. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

The structure of FasterNext. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

The structure of MLCA mechanism. by Junjie Lu (160350)

“…To address this issue, this paper proposes FCMI-YOLO, a real-time fire detection algorithm optimized for edge devices. …”

Impact of class imbalance. by GaoXiang Zhao (21499525)

Detection results of YOLOv5s and FCMI-YOLO under different exposure levels. by Junjie Lu (160350)

Comparison of detection results of different methods in FasterNext. by Junjie Lu (160350)

Antivirus Engines (PowerPoint) by Paul A. Gagniuc (1818325)

“…[EN] The presentation provides a systematic overview of the architecture and functionality of antivirus engines, covering classical detection methods based on signatures as well as advanced techniques involving heuristics, behavioral analysis, and modern innovations in cybersecurity. …”

Data Sheet 1_Machine learning models predict coagulopathy in traumatic brain injury patients in ER.csv by Haoyu Wang (429641)

“…<p>Traumatic brain injury (TBI) is a critical emergency condition, with 15–35% of patients developing coagulopathy, increasing risks of secondary brain injury and mortality. …”

Table 1_Identifying pyroptosis-hub genes and immune infiltration in neonatal hypoxic-ischemic brain injury.docx by Chi Qin (10001651)

“…Background<p>Hypoxic-ischemic encephalopathy (HIE) is a leading cause of neonatal brain injury and neurodevelopmental disorders. …”

SHAP values for chromatin-associated proteins profiled in integrative model from Hughes et al. [21] and Wang et al. [22] (A) Heatmap of absolute mean SHAP values for different algo... by Kashyap Chhatbar (9515350)

MWOA-BiLSTM machine fault detection process. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

WOA process. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

LSTM regulation principle. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

BiLSTM internal network update method. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Fault classification proportion. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Fault interval. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Scenarios of the tuning parameters. by Yi-Qiang Xia (20161326)

“…In essence, it prevents algorithms from settling for suboptimal solutions too soon, encouraging exploration of a broader solution space before converging, by incorporating cauchy variation and a perturbation term, MWOA achieve optimization over a wide search space. …”

Fig 1 - by Michiel Stock (1627249)

Table 8_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 9_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”