Artificial intelligence in detection of small bowel lesions and their bleeding risk:A new step forward

The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate.The development of artificial intelligence(AI)in CE could simplify physicians’tasks.The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk,and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

Artificial Intelligence-Based Automated Actuarial Pricing and Underwriting Model for the General Insurance Sector

The Automated Actuarial Pricing and Underwriting Model has been enhanced and expanded through the implementation of Artificial Intelligence to automate three distinct actuarial functions: loss reserving, pricing, and underwriting. This model utilizes data analytics based on Artificial Intelligence to merge microfinance and car insurance services. Introducing and applying a no-claims bonus rate system, comprising base rates, variable rates, and final rates, to three key policyholder categories significantly reduces the occurrence and impact of claims while encouraging increased premium payments. We have enhanced frequency-severity models with eight machine learning algorithms and adjusted the Automated Actuarial Pricing and Underwriting Model for inflation, resulting in outstanding performance. Among the machine learning models utilized, the Random Forest (RANGER) achieved the highest Total Aggregate Comprehensive Automated Actuarial Loss Reserve Risk Pricing Balance (ACAALRRPB), establishing itself as the preferred model for developing Automated Actuarial Underwriting models tailored to specific policyholder categories.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models

BACKGROUND Deep learning provides an efficient automatic image recognition method for small bowel(SB)capsule endoscopy(CE)that can assist physicians in diagnosis.However,the existing deep learning models present some unresolved challenges.AIM To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks,and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups.METHODS The proposed model represents a two-stage method that combined image classification with object detection.First,we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images,normal SB mucosa images,and invalid images.Then,the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding,and the location of the lesion was marked.We constructed training and testing sets and compared model-assisted reading with physician reading.RESULTS The accuracy of the model constructed in this study reached 98.96%,which was higher than the accuracy of other systems using only a single module.The sensitivity,specificity,and accuracy of the model-assisted reading detection of all images were 99.17%,99.92%,and 99.86%,which were significantly higher than those of the endoscopists’diagnoses.The image processing time of the model was 48 ms/image,and the image processing time of the physicians was 0.40±0.24 s/image(P<0.001).CONCLUSION The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images,which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Artificial intelligence-assisted psychosis risk screening in adolescents:Practices and challenges

Artificial intelligence-based technologies are gradually being applied to psychiatric research and practice.This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents.In terms of the practice of psychosis risk screening,the application of two artificial intelligence-assisted screening methods,chatbot and large-scale social media data analysis,is summarized in detail.Regarding the challenges of psychiatric risk screening,ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence,which must comply with the four biomedical ethical principles of respect for autonomy,nonmaleficence,beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings.By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens,we propose that assuming they meet ethical requirements,there are three directions worth considering in the future development of artificial intelligenceassisted psychosis risk screening in adolescents as follows:nonperceptual realtime artificial intelligence-assisted screening,further reducing the cost of artificial intelligence-assisted screening,and improving the ease of use of artificial intelligence-assisted screening techniques and tools.

Intelligent risk identification of gas drilling based on nonlinear classification network

During the transient process of gas drilling conditions,the monitoring data often has obvious nonlinear fluctuation features,which leads to large classification errors and time delays in the commonly used intelligent classification models.Combined with the structural features of data samples obtained from monitoring while drilling,this paper uses convolution algorithm to extract the correlation features of multiple monitoring while drilling parameters changing with time,and applies RBF network with nonlinear classification ability to classify the features.In the training process,the loss function component based on distance mean square error is used to effectively adjust the best clustering center in RBF.Many field applications show that,the recognition accuracy of the above nonlinear classification network model for gas production,water production and drill sticking is 97.32%,95.25%and 93.78%.Compared with the traditional convolutional neural network(CNN)model,the network structure not only improves the classification accuracy of conditions in the transition stage of conditions,but also greatly advances the time points of risk identification,especially for the three common risk identification points of gas production,water production and drill sticking,which are advanced by 56,16 and 8 s.It has won valuable time for the site to take correct risk disposal measures in time,and fully demonstrated the applicability of nonlinear classification neural network in oil and gas field exploration and development.

Governance of artificial intelligence applications in a business audit via a fusion fuzzy multiple rule‑based decision‑making model

A broad range of companies around the world has welcomed artificial intelligence(AI)technology in daily practices because it provides decision-makers with comprehensive and intuitive messages about their operations and assists them in formulating appropriate strategies without any hysteresis.This research identifies the essential components of AI applications under an internal audit framework and provides an appropriate direction of strategies,which relate to setting up a priority on alternatives with multiple dimensions/criteria involvement that need to further consider the interconnected and intertwined relationships among them so as to reach a suitable judgment.To obtain this goal and inspired by a model ensemble,we introduce an innovative fuzzy multiple rule-based decision making framework that integrates soft computing,fuzzy set theory,and a multi-attribute decision making algorithm.The results display that the order of priority in improvement—(A)AI application strategy,(B)AI governance,(D)the human factor,and(C)data infrastructure and data quality—is based on the magnitude of their impact.This dynamically enhances the implementation of an AI-driven internal audit framework as well as responds to the strong rise of the big data environment.Highlights Artificial intelligence(AI)promotes the sustainability development of audit tasks.A fuzzy MRDM model extracts key factors from large amounts of data.Fuzzy decision-making trial and evaluation laboratory analysis accounts for dependence and feedback among factors.An effective framework of AI-driven business audit is proposed in which“AI cognition of senior executives”is the most important criterion.

An Artificial Intelligence Algorithm for the Real-Time Early Detection of Sticking Phenomena in Horizontal Shale Gas Wells

Sticking is the most serious cause of failure in complex drilling operations.In the present work a novel“early warning”method based on an artificial intelligence algorithm is proposed to overcome some of the known pro-blems associated with existing sticking-identification technologies.The method is tested against a practical case study(Southern Sichuan shale gas drilling operations).It is shown that the twelve sets of sticking fault diagnostic results obtained from a simulation are all consistent with the actual downhole state;furthermore,the results from four groups of verification samples are also consistent with the actual downhole state.This shows that the pro-posed training-based model can effectively be applied to practical situations.

Unveiling significant risk factors for intensive care unit-acquired weakness:Advancing preventive care

In this editorial,we discuss an article titled,“Significant risk factors for intensive care unit-acquired weakness:A processing strategy based on repeated machine learning,”published in a recent issue of the World Journal of Clinical Cases.Intensive care unit-acquired weakness(ICU-AW)is a debilitating condition that affects critically ill patients,with significant implications for patient outcomes and their quality of life.This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors.Data from a cohort of 1063 adult intensive care unit(ICU)patients were analyzed,with a particular emphasis on variables such as duration of ICU stay,duration of mechanical ventilation,doses of sedatives and vasopressors,and underlying comorbidities.A multilayer perceptron neural network model was developed,which exhibited a remarkable impressive prediction accuracy of 86.2%on the training set and 85.5%on the test set.The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Intelligent Decision Support System for Bank Loans Risk Classification

Intelligent Decision Support System (IISS) for Bank Loans Risk Classification (BLRC), based on the way of integration Artificial Neural Network (ANN) and Expert System (ES), is proposed. According to the feature of BLRC, the key financial and non-financial factors are analyzed. Meanwhile, ES and Model Base (MB) which contain ANN are designed . The general framework,interaction and integration of the system are given. In addition, how the system realizes BLRC is elucidated in detail.

Intelligent Feature Selection with Deep Learning Based Financial Risk Assessment Model

Due to global financial crisis,risk management has received significant attention to avoid loss and maximize profit in any business.Since the financial crisis prediction(FCP)process is mainly based on data driven decision making and intelligent models,artificial intelligence(AI)and machine learning(ML)models are widely utilized.This article introduces an intelligent feature selection with deep learning based financial risk assessment model(IFSDL-FRA).The proposed IFSDL-FRA technique aims to determine the financial crisis of a company or enterprise.In addition,the IFSDL-FRA technique involves the design of new water strider optimization algorithm based feature selection(WSOA-FS)manner to an optimum selection of feature subsets.Moreover,Deep Random Vector Functional Link network(DRVFLN)classification technique was applied to properly allot the class labels to the financial data.Furthermore,improved fruit fly optimization algorithm(IFFOA)based hyperparameter tuning process is carried out to optimally tune the hyperparameters of the DRVFLN model.For enhancing the better performance of the IFSDL-FRA technique,an extensive set of simulations are implemented on benchmark financial datasets and the obtained outcomes determine the betterment of IFSDL-FRA technique on the recent state of art approaches.

An intelligent identification method of safety risk while drilling in gas drilling

In view of the shortcomings of current intelligent drilling technology in drilling condition representation, sample collection, data processing and feature extraction, an intelligent identification method of safety risk while drilling was established. The correlation analysis method was used to determine correlation parameters indicating gas drilling safety risk. By collecting monitoring data in the safety risk period of more than 20 wells, a sample database of a variety of safety risks in gas drilling was established, and the number of samples was expanded by using the method of few-shot learning. According to the forms of gas drilling monitoring data samples, a two-layer convolution neural network architecture was designed, and multiple convolution cores of different sizes and weights were set to realize the vertical and horizontal convolution computations of samples to extract and learn the variation law and correlation characteristics of multiple monitoring parameters. Finally, based on the training results of neural network, samples of different kinds of safety risks were selected to enhance the recognition accuracy. Compared with the traditional BP(error back propagation) full-connected neural network architecture, this method can more deeply and effectively identify safety risk characteristics in gas drilling, and thus identify and predict risks in advance, which is conducive to avoid and quickly solve safety risks while drilling. Field application has proved that this method has an identification accuracy of various safety risks while drilling in the process of gas drilling of about 90% and is practical.

Probabilistic Rationale of Actions for Artificial Intelligence Systems Operating in Uncertainty Conditions

The approach for probabilistic rationale of artificial intelligence systems actions is proposed.It is based on an implementation of the proposed interconnected ideas 1-7 about system analysis and optimization focused on prognostic modeling.The ideas may be applied also by using another probabilistic models which supported by software tools and can predict successfulness or risks on a level of probability distribution functions.The approach includes description of the proposed probabilistic models,optimization methods for rationale actions and incremental algorithms for solving the problems of supporting decision-making on the base of monitored data and rationale robot actions in uncertainty conditions.The approach means practically a proactive commitment to excellence in uncertainty conditions.A suitability of the proposed models and methods is demonstrated by examples which cover wide applications of artificial intelligence systems.

Research on Active Safety Methodologies for Intelligent Railway Systems

Safety is essential when building a strong transportation system.As a key development direction in the global railway system,the intelligent railway has safety at its core,making safety a top priority while pursuing the goals of efficiency,convenience,economy,and environmental friendliness.This paper describes the state of the art and proposes a system architecture for intelligent railway systems.It also focuses on the development of railway safety technology at home and abroad,and proposes the active safety method and technology system based on advanced theoretical methods such as the in-depth integration of cyber–physical systems(CPS),data-driven models,and intelligent computing.Finally,several typical applications are demonstrated to verify the advancement and feasibility of active safety technology in intelligent railway systems.

ChatGPT技术在生物医药领域的应用潜力与风险

本文系统梳理了ChatGPT的发展历程与现状,分析ChatGPT技术与生物医药领域的耦合情况。从知识创新与模型创新两个维度,划分生物医药领域ChatGPT技术应用的类型,并分析其应用焦点、应用场景和特征;从科学研究与消费终端应用的角度阐述各项ChatGPT技术类型在生物医药领域中的应用价值;剖析各项ChatGPT技术类型在生物医药领域中存在的潜在风险。ChatGPT技术将加快生物医药领域的研究与服务,扩大而非取代研究人员的专业知识。未来应加强微调挖掘型GPT工具与部署型服务设施的建设,推动ChatGPT技术在生物医药领域的有效应用。

面向智慧教育的技术伦理取向与风险规约

当前,智能技术与教育的全过程融合正推动教育方式发生深刻转变,同时衍生出算法歧视、技术滥用、隐私泄露和教育主体错位等技术伦理风险,如何规避风险以最大化享受智能技术红利成为智慧教育发展的重要议题。为此,文章首先分析技术伦理相关报告文件,刻画智慧教育在智能技术、教育主体、教育数据和教育监管四个向度的技术伦理取向;然后,文章剖析“教-学-管-评-练-研”六大智慧教育典型场景下的差异化技术伦理风险表征,将其产生根源归为技术工具失信、教育实践失范和师生素养不足三个层面;最后,文章从技术升级、风险管理、问责监管、数据流动和素养提升角度给出风险防范与治理建议,助力智慧教育健康可持续发展。

人工智能治理中“基于风险的方法”:理论、实践与反思

以生成式人工智能为代表的新一轮人工智能技术带来了一系列规制挑战,而传统的“基于权利的方法”难以有效因应。“基于风险的方法”主张根据人工智能所带来的不同类型和等级的风险,采取相应的治理措施和方式,平衡技术创新与有效治理的关系。这是一种符合人工智能风险特性和发展规律的治理方法,以风险社会、适应性治理和回应性规制等理论为指引,以风险识别与分析、风险评估与分类、风险控制与监督、风险沟通与参与作为制度构成。在基于风险的人工智能治理实践中,欧盟、美国和中国分别采取“自上而下模式”“自下而上模式”“混合模式”,并取得了初步成效。然而,人工智能治理中“基于风险的方法”也面临批判和质疑,应当构建多元共治的人工智能风险治理体系,并建立动态适应的人工智能风险治理机制,同时搭建开放协作的人工智能风险治理平台。

大数据下企业供应链风险管理与竞争情报融合模型构建--以华为公司为例

[研究目的]国际经贸对抗环境下,中美贸易摩擦将持续升级,国家层面的技术创新竞争使得企业面临的供应链风险大幅提升。同时,大数据改变了供应链风险管理模式,而竞争情报可以从理论、技术和方法等方面提升大数据下企业供应链风险管理能力,因此大数据下企业供应链风险管理与竞争情报的融合研究具有重要意义。[研究方法]采用文献分析、案例研究等方法,分析供应链风险管理与竞争情报的异同点,继而论证大数据下供应链风险管理与竞争情报融合的可行性和必要性,再辅之以标杆企业华为的供应链中断风险事件验证,最终构建大数据下华为供应链风险管理与竞争情报融合模型,并分析运行维护此模型的保障要素。[研究结论]研究表明,基于战略、组织、制度、技术和资源五要素的共同保障,大数据下供应链风险管理与竞争情报的融合能够实现供应链风险管理的全面性、及时性和有效性。

论生成式人工智能的技术创新伦理周期——以ChatGPT为例

生成式人工智能在推动技术创新突破性应用的同时,也带来了许多社会伦理风险:隐私暴露与信息安全控制风险;虚假信息派生所带来的信息传播风险;算法偏见所带来的决策和政策风险以及责任界定模糊风险。运用“索洛悖论”理论,将伦理进步看作是科技创新投入的增函数,可以发现生成式人工智能对伦理推动的“技术创新索洛周期”。基于此,要跨越生成式人工智能“伦理风险鸿沟”可以采取以下对策:健全生成式人工智能法律规制,推动构建生成式人工智能治理体系;加强平台监管,完善流程标准化管理,规范保障生成式人工智能安全发展;落实优化生成式人工智能可持续发展环境,探索更多行业重塑的可能。