Results 131 to 140 of about 2,085,409 (278)

Artificial intelligence and liquidation: Reality, destiny and fantasy

International Insolvency Review, EarlyView.
Abstract Artificial intelligence (AI) is increasingly reshaping the administration of corporate liquidation. Beyond its established role in financial prediction and data analytics, AI is now assisting insolvency practitioners in identifying the onset of financial distress, managing creditor communications, tracing and valuing assets and enhancing ...
Kai Zhang, Jingchen Zhao
wiley   +1 more source

eXplainable AI (XAI) - Lecture 1-

, 2021
Primera sessió del seminari impartit pel professor convidat Sebastian Lapuschkin, de l'Institut Fraunhofer de Berlin, sobre Explainable AI6583.mp4 6583 ...
Lapuschkin, Sebastian
core   +1 more source

Explainable machine learning methods to predict postpartum depression risk

Systems Science & Control Engineering
Postpartum depression (PPD) is a type of depression that mothers have following childbirth due to hormonal changes, psychological transition to parenting, and exhaustion. This depression strikes either during/or in the first year following childbirth. It
Susmita Shivaprasad, Krishnaraj Chadaga, Niranjana Sampathila, Srikanth Prabhu, Rajagopala Chadaga P, Swathi K S   +5 more
doaj   +1 more source

Beyond omics: From descriptive profiling to causal and scalable design of fermentation microbiomes

iMeta, EarlyView.
Fermentation microbiomes play essential roles in food production, feed preservation, waste valorization, and diverse sustainable industrial processes. Although multi‐omics and systems biology have substantially advanced our understanding of their assembly, interactions, and functional dynamics, industrial translation remains constrained by fragmented ...
Dongze Niu, Huili Pang, Xuekai Wang, Liang Song, Jing Zhang, Ansah Terry, Junfeng Li, Jingwen Cao, Qinhua Liu, Dayong Han, Jidong Wang, Jong Geun Kim, Kazuo Ataku, Smerjai Bureenok, Zhongfang Tan, Fengyan Bai, Tao Shao, Taoli Huhe, Lifeng Wang, Peijie Han, Chuncheng Xu, Fuyu Yang, Yimin Cai   +22 more
wiley   +1 more source

eXplainable AI (XAI) -Lecture 3-

, 2021
Tercera sessió del seminari impartit pel professor convidat Sebastian Lapuschkin, de l'Institut Fraunhofer de Berlin, sobre Explainable AI6585.mp4 6585 ...
Lapuschkin, Sebastian
core   +1 more source

Multi‐omics–driven precision medicine

iMeta, EarlyView.
Multi‐omics‐driven precision medicine (MODPM) provides a multiscale, continuously learnable framework that integrates genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiome profiles, and clinical data. Powered by artificial intelligence and foundation models, MODPM enables cross‐modal representation learning, contextual modeling ...
Huibo Li, Zhe Zhao, Yifan Zhang, Yao Ma, Gaofei Hu, Min Zeng, Zhanqun Yang, Zixuan Zhao, Xin Zhou, Wei Hu, Yuxuan Sun, Meng Su, Jun Li, Matthew Whiteman, Wei Fu, Chao Zhong, Lemin Zheng, Long Chen, Hairong Lv, Rongsheng Zhao, Yi Zhun Zhu   +20 more
wiley   +1 more source

Identification of kidney stones in KUB X-ray images using VGG16 empowered with explainable artificial intelligence

Scientific Reports
A kidney stone is a solid formation that can lead to kidney failure, severe pain, and reduced quality of life from urinary system blockages. While medical experts can interpret kidney-ureter-bladder (KUB) X-ray images, specific images pose challenges for
Fahad Ahmed, Sagheer Abbas, Atifa Athar, Tariq Shahzad, Wasim Ahmad Khan, Meshal Alharbi, Muhammad Adnan Khan, Arfan Ahmed   +7 more
doaj   +1 more source

Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review

Journal of Medical Radiation Sciences, EarlyView.
This systematic review summarizes current evidence on machine and deep learning algorithms for MRI‐based cervical cancer segmentation, demonstrating promising diagnostic performance and potential for workflow automation. However, methodological heterogeneity, limited external validation, and low certainty of evidence highlight the need for standardized
Somayeh Haji Ahmadi, Sina Soltan Mohammadi, Abolfazl Koozari, Mohammadreza Elhaie   +3 more
wiley   +1 more source

XAI-IDS: Toward Proposing an Explainable Artificial Intelligence Framework for Enhancing Network Intrusion Detection Systems

The exponential growth of network intrusions necessitates the development of advanced artificial intelligence (AI) techniques for intrusion detection systems (IDSs).
Mustafa Abdallah, Osvaldo Arreche, Tanish Guntur   +2 more
core   +1 more source

Image‐based and biochemical multimodal phenotyping for explainable classification of chia (Salvia hispanica L.) genotypes

Journal of the Science of Food and Agriculture, EarlyView.
Abstract BACKGROUND This study developed an explainable machine learning framework integrating morphological, color, and biochemical characteristics for classifying chia (Salvia hispanica L.) genotypes. A dataset was assembled from 1200 seed images spanning four genotypes, from which 17 morphological and color features were extracted.
Sevim Akcura, Irem Poyraz, Beyza Ciftci, Mahmut Kaplan   +3 more
wiley   +1 more source