Results 61 to 70 of about 95,729 (269)

Lithology Recognition Research Based on Wavelet Transform and Artificial Intelligence

Cejing jishu, 2023
Lithology identification is one of the main application directions of deep learning in oil and gas field development. Artificial intelligence models can effectively improve the efficiency of oil and gas field development and on-site construction.
FANG Dazhi, MA Weijun, YAN Xu, MAO Zheng, GAO Yang   +4 more
doaj   +1 more source

Predicting customer's gender and age depending on mobile phone data

, 2019
In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place.
Aljoumaa, Kadan, AlZuabi, Ibrahim Mousa, Jafar, Assef   +2 more
core   +1 more source

XGBoost-Powered Ransomware Detection

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Ransomware remains a rapidly evolving cyber threat, causing substantial financial and operational disruptions globally. Traditional signature-based detection systems are ineffective against sophisticated, zero-day attacks due to their static nature. Consequently, machine learning-based approaches offer a more effective and adaptive alternative.
Wildanil Ghozi, Heru Lestiawan, Ramadhan Rakhmat Sani, Jassim Nadheer Hussein, Fauzi Adi Rafrastara   +4 more
openaire   +1 more source

Multicohort Validation of Gut Microbiome Signatures for Cholangiocarcinoma Diagnosis and Functional Characterization of Bifidobacterium Pseudocatenulatum

Advanced Science, EarlyView.
This study analyzes gut bacteria in cholangiocarcinoma patients, revealing distinct microbial signatures that enable accurate disease detection. Species‐based diagnostic models achieved over 98% accuracy in identifying cholangiocarcinoma and distinguished it from other liver diseases. The research demonstrates that specific beneficial bacteria suppress
Benchen Rao, Jianwen Jiang, Ruiqing Zhang, Dongya Zhang, Chenchen Zhang, Ang Li, Haifeng Lu, Hua Zhang, Lin Zhou, Wenzhi Guo, Peihao Wen, Jianfeng Xue, Jie Pan, Tuerganaili Aji, Zhou Lan, Xianzhi Jiang, Shusen Zheng, Zujiang Yu, Zhigang Ren   +18 more
wiley   +1 more source

NanoLoop: A Deep Learning Framework Leveraging Nanopore Sequencing for Chromatin Loop Prediction

Advanced Science, EarlyView.
Chromatin loops are central to gene regulation and 3D genome organization. Leveraging Nanopore sequencing's ability to jointly capture DNA sequence and methylation, we present NanoLoop, the first framework for genome‐wide chromatin loop prediction using Nanopore data.
Wenjie Huang, Li Tang, Matthew C. Hill, Yonghao Zhang, Jun Xie, Min Li   +5 more
wiley   +1 more source

Long‐Tea‐CLIP: An Expert‐Level Multimodal AI Framework for Fine‐Grained Green Tea Grading Across Five Sensory Dimensions

Advanced Science, EarlyView.
Long‐Tea‐CLIP (Contrastive Language‐Image Pre‐training) presents a multimodal AI framework that integrates visual, metabolomic, and sensory knowledge to grade green tea across appearance, soup color, aroma, taste, and infused leaf. By combining expert‐guided modeling with CLIP‐supervised learning, the system delivers fine‐grained quality evaluation and
Yanqun Xu, Zhengfang Xue, Qing Luo, Xu Liu, Yongquan Xu, Huang Jing, Xingcai Zhang, Xinyuan Zhang, Zhonghua Liu, Zisheng Luo   +9 more
wiley   +1 more source

Health Risk Classification Using XGBoost with Bayesian Hyperparameter Optimization

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Health risk classification is important. However, health risk classification is challenging to address using conventional analytical techniques.
Syaiful Anam, Imam Nurhadi Purwanto, Dwi Mifta Mahanani, Feby Indriana Yusuf, Hady Rasikhun   +4 more
doaj   +1 more source

Comparing XGBoost and ARCH-LM Models for IPO Valuation in the Iranian Capital Market [PDF]

International Journal of Management, Accounting and Economics
The main objective of this research is to conduct a rigorous comparative analysis of the performance of the eXtreme Gradient Boosting (XGBoost) algorithm against the traditional Generalized Autoregressive Conditional Heteroskedasticity (ARCH-LM) model in
Fatemeh Malmir, Farshid Kheirollahi, Hossein Yarahmadi, Farid Sefaty   +3 more
doaj   +1 more source

Intrinsic PPG–ECG Coupling for Accurate and Low‐Power Blood Pressure Monitoring

Advanced Science, EarlyView.
A PPG–ECG coupling strategy for continuous blood pressure monitoring that intrinsically synchronizes signals within a single waveform is demonstrated, minimizing synchronization errors and hardware complexity. This approach halves power consumption while maintaining high accuracy, enabling compact, energy‐efficient wearable devices for personalized ...
Sitong Chen, Hua Luo, Zhentao Yao, Zhou Jiang, Xiaodong Wu, Hanmin Liu   +5 more
wiley   +1 more source

Emergency and routine presentation of neuroendocrine neoplasia in England: determinants of late presentation and survival outcomes

Endocrine Oncology
Objective: The time from onset of symptoms of neuroendocrine neoplasia (NEN) to diagnosis ranges between 5 and 7 years. Risk factors associated with this and the difference in overall survival (OS) between routine and emergency presentation (RP and EP ...
Marie Line El Asmar, Mohamed Mortagy, Benjamin E White, Dan Burns, John Ramage   +4 more
doaj   +1 more source