Machine Learning-Based Intelligent Auscultation Techniques in CongenitalHeart Disease: Application and Development

Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-quently enhancing their survival rates.While cardiac auscultation offers an objective reflection of cardiac abnormalities and function,its evaluation is significantly influenced by personal experience and external factors,rendering it susceptible to misdiagnosis and omission.In recent years,continuous progress in artificial intelli-gence(AI)has enabled the digital acquisition,storage,and analysis of heart sound signals,paving the way for intelligent CHD auscultation-assisted diagnostic technology.Although there has been a surge in studies based on machine learning(ML)within CHD auscultation and diagnostic technology,most remain in the algorithmic research phase,relying on the implementation of specific datasets that still await verification in the clinical envir-onment.This paper provides an overview of the current stage of AI-assisted cardiac sounds(CS)auscultation technology,outlining the applications and limitations of AI auscultation technology in the CHD domain.The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.

Bridging the Gap:Integration of Artificial Intelligence with Organ-on-Chip(AI-OoC)

.Organ-on-Chip(OoC)has emerged as a revolutionary approach to emulate human organ function-ality in vitro,offering unparalleled insights into physiological processes and disease modeling.The integration of artificial intelligence(AI)with OoC platforms presents a transformative synergy,combining the precision of microscale organ replication with the analytical prowess of intelligent algorithms,is emerging as a transforma-tive force in harnessing the full potential of OoC.This perspective investigates the multifaceted implications of integrating AI with OoC,examining its impact on biomedical research,acknowledging the synergistic po-tential that arises from combining the precision of microscale organ replication with the analytical capabilities of intelligent algorithms,and fostering a future where the intricate workings of the technology and biology.

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

In recent years,cloud computing has provided a Software As A Service(SaaS)platform where the software can be reused and applied to fulfill compli-cated user demands according to specific Quality of Services(QoS)constraints.The user requirements are formulated as a workflow consisting of a set of tasks.However,many services may satisfy the functionality of each task;thus,searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem.This work will introduce a hybrid Artificial Bee Colony(ABC)with a Cuckoo Search(CS)algorithm to untangle service composition problem.The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance.However,the ABC suffers from a slow convergence problem.Therefore,the CS is used to overcome the ABC’s limitations by allowing the abandoned bees to enhance their search and override the local optimum.The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms(ABC and CS)and three state-of-the-art swarm-based composition algorithms.In addition,extensive parameter study experiments were conducted to set up the proposed algorithm’s parameters.The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria(bestfitness value,averagefitness value,and average execution time)overall datasets in 30 different runs.Furthermore,the proposed algorithm also exhibits better performance than the state–of–the–art algorithms in the three comparison criteria over 30 different runs.

Prediction of impedance responses of protonic ceramic cells using artificial neural network tuned with the distribution of relaxation times

A deep-learning-based framework is proposed to predict the impedance response and underlying electrochemical behavior of the reversible protonic ceramic cell(PCC) across a wide variety of different operating conditions.Electrochemical impedance spectra(EIS) of PCCs were first acquired under a variety of opera ting conditions to provide a dataset containing 36 sets of EIS spectra for the model.An artificial neural network(ANN) was then trained to model the relationship between the cell operating condition and EIS response.Finally,ANN model-predicted EIS spectra were analyzed by the distribution of relaxation times(DRT) and compared to DRT spectra obtained from the experimental EIS data,enabling an assessment of the accumulative errors from the predicted EIS data vs the predicted DRT.We show that in certain cases,although the R^(2)of the predicted EIS curve may be> 0.98,the R^(2)of the predicted DRT may be as low as~0.3.This can lead to an inaccurate ANN prediction of the underlying time-resolved electrochemical response,although the apparent accuracy as evaluated from the EIS prediction may seem acceptable.After adjustment of the parameters of the ANN framework,the average R^(2)of the DRTs derived from the predicted EIS can be improved to 0.9667.Thus,we demonstrate that a properly tuned ANN model can be used as an effective tool to predict not only the EIS,but also the DRT of complex electrochemical systems.

An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation

In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy.

Intelligent Deep Learning Enabled Human Activity Recognition for Improved Medical Services

Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.

Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

AI-Based Intelligent Model to Predict Epidemics Using Machine Learning Technique

The immediate international spread of severe acute respiratory syn-drome revealed the potential threat of infectious diseases in a closely integrated and interdependent world.When an outbreak occurs,each country must have a well-coordinated and preventative plan to address the situation.Information and Communication Technologies have provided innovative approaches to dealing with numerous facets of daily living.Although intelligent devices and applica-tions have become a vital part of our everyday lives,smart gadgets have also led to several physical and psychological health problems in modern society.Here,we used an artificial intelligence AI-based system for disease prediction using an Artificial Neural Network(ANN).The ANN improved the regularization of the classification model,hence increasing its accuracy.The unconstrained opti-mization model reduced the classifier’s cost function to obtain the lowest possible cost.To verify the performance of the intelligent system,we compared the out-comes of the suggested scheme with the results of previously proposed models.The proposed intelligent system achieved an accuracy of 0.89,and the miss rate 0.11 was higher than in previously proposed models.

Numerical Investigation on Vibration Performance of Flexible Plates Actuated by Pneumatic Artificial Muscle

This paper theoretically introduced the feasibility of changing the vibration characteristics offlexible plates by using bio-inspired,extremely light,and powerful Pneumatic Artificial Muscle(PAM)actuators.Many structural plates or shells are typicallyflexible and show highvibration sensitivity.For this reason,this paper provides a way toachieve active vibrationcontrolfor suppressing the oscillations ofthese structuresto meet strict stability,safety,and comfort requirements.The dynamic behaviors of the designed plates are modeled by using thefinite element(FE)method.As is known,the output force vs.contraction curve of PAM is nonlinear generally.In this presentfinite element model,the maximum forces provided by PAM in different air pressure are adopted as controlling forces for applying for the plate.The non-linearity between the output force and displacement of PAM is avoided in this study.The dynamic behaviors of plates with several independent groups of controlling forces are observed and studied.The results show that the natural frequencies of the plate can be varying and the max amplitude decreases significantly if the controlling forces are applied.The present work also demonstrates the potential of the PAM actuators as valid means for damping out the vibration offlexible systems.

深层—超深层海相碳酸盐岩地震勘探技术发展与攻关方向

随着塔里木盆地顺北油气区、轮南地区轮探1井在8200 m深度以下获得工业油气流,碳酸盐岩勘探迅速向深层-超深层领域迈进,向地震勘探技术提出了严峻挑战。主要分析了超深层复杂波场地震成像理论研究进展及面临的问题。在超深层储层预测关键技术方面,分析了由地震数据结构表征识别小断裂、基于数字岩心的孔隙结构定量化预测方法等现状;从勘探地质需求的角度,提出深层—超深层碳酸盐岩储层与流体预测技术发展趋势和重点攻关方向,以期为海相碳酸盐岩地震勘探的理论及技术研究提供借鉴。获得以下认识:①针对超深层低信噪比地震数据,Q叠前深度偏移和TTI介质RTM技术在碳酸盐岩储层成像中取得了一定效果,基于波动理论的层间多次波压制、各向异性Q⁃RTM、最小二乘Q⁃RTM及各向异性全方位角度域成像技术是重点攻关方向。②深层—超深层强非均质性碳酸盐岩储层地震预测技术存在欠缺理论依据、预测精度较低等问题,亟待加强理论方法探索和技术攻关。③地震岩石物理实验与储层地质的深度融合以及基于双相介质波动特征(频率、频散与衰减等)的储层敏感属性精细化地震预测技术、人工智能碳酸盐岩储层定量预测及流体检测技术等均是重要发展方向,“可靠的深层地震资料、多学科联合的储层高精度表征和深度学习人工智能”发展趋势十分明显。

人工智能技术在法医学中的应用研究进展

人工智能(artifi cial intelligence, AI)是基于大数据、机器学习等技术的一种智能化解决方案,能够提升形态学识别能力、诊断效率及质量。随着科学技术的迅猛发展,AI技术突飞猛进,成果应用日益广泛,为解决各类现实问题提供了新的方案和参考。“AI+”被广泛用于各行各业,取得了优异的成绩,其中法医学领域近年来涌现出了一批优秀的研究成果。本文对近三年来国内外法医学者发表的有关论文进行综述,希望能够为全国法医同行在病理学、临床学、人类学、毒物学等方面的研究提供新的思路。

LLM在水利政府网站公共服务中的应用研究

针对水利政府网站中传统自然语言处理技术导致的公共服务机械、低效问题,引入LLM技术和理念,对多种智能分析计算服务进行封装,设计构建一套以封装后的智能框架平台为服务支撑,以多元化的应用服务实现各业务流程,以多维度数据为资源存储的水利智能公共服务体系。提出水利智能公共服务的应用场景,提升政府网站公共服务智能化、便捷化程度,更加精准地理解民众意图,为今后水利政务服务与公共大模型的结合应用提供有益的尝试,帮助水利政府网站公共服务构建更高效的模型,全面提升公共服务的多样普适性、长期有效性、专业权威性、方便快捷性,同时为创新水利公共服务流程和模式等提供参考借鉴。

简历分析法:一种教育实证研究新方法

虽然教育学属于独立的一级学科,但十分缺乏对本学科发展起支撑作用的、区别于其他学科的专门研究方法,而且由于大量引入其他学科的研究方法,又进一步限制了其学科方法体系、知识体系和理论体系的发展。近年来,简历分析法在全球兴起,试图通过对简历数据的分析,融合大数据等方法,综合研判人才的教育与成长规律、项目/政策实施成效等,这既为教育科学研究提供了实证新方法,也为教育学科建设提供了新契机。进入以ChatGPT为代表的人工智能时代,生成式人工智能模型+计算力(超级计算)+应用场景的新研究范式逐步成熟,简历分析法也在教育研究应用上迎来新突破,有望成为教育学科的专门研究方法。在中国教育场域中,蕴含着各级各类教育主体的简历和其他大数据资源,借助简历分析法的技术突破,有望全面解构本土教育规律,构建具有中国特色、符合中国国情的教育学科研究新范式,甚至有望通过碎片化专门知识累积,形成新的本土学科体系。最近十年,学术界涌现出不少基于简历分析法的研究成果,但已有研究对简历分析法这一方法本身的研究并不充分,尤其是对这一方法的学科归属、是不是教育学科的专门研究方法、如何与传统研究方法以及大数据、人工智能等新研究方法的融合等探讨不够。同时,我国已有研究在简历分析法概念使用、研究流程方面也存在很大差异,这表明简历分析法在中国教育学科的应用仍不成熟。为此,研究从方法论层面展开对简历分析法的剖析,通过大量研究案例尝试全面解构这一研究方法,并尝试对其内涵与外延、具体操作流程等进行规范,对简历分析法在我国使用中存在的问题进行梳理,最后对该方法在我国的应用前景进行探讨,以期促进简历分析法在我国教育学科的规范使用和全面推广。

基于LoRA模型的非遗数字化传承:以楚漆器为例

在智能化时代的浪潮下,人工智能技术逐渐渗透至各行各业,也为非物质文化遗产的保护与传承带来前所未有的新机遇。本文旨在探索人工智能在非物质文化遗产传承中的新应用。首先,梳理了人工智能赋能非物质文化遗产传承的新思路,并以楚漆器为研究对象,剖析其发展历程和艺术风格。其次,对楚漆器的资源进行了系统的数字化采集和处理,构建了一个模型训练的专用数据集。随后,利用LoRA模型成功训练出能够反映楚漆器风格特点的高效智能模型。该模型进一步被导入Stable Diffusion完成智能设计实践。通过这一系列的技术应用和创新实践,不仅为楚漆器的保护和发展开辟了新视角,同时也为非物质文化遗产的数字化传承提供了新的思路和方法 。此外,本文成果对其他非物质文化遗产的数字化传承工作同样具有一定的参考价值和借鉴意义。

智能化时代的设计技术与感性审美嬗变

设计思维由单一线性转向人机协同的创新模式,智能化技术的运用打破了传统设计美学的边界,为感性设计审美带来了技术性变革。文章整理归纳了智能技术在设计创作中的应用案例,对智能化时代的社会背景、设计技术、感性审美进行了分析。

人工智能支持下高职院校规范化教学以及质量提升探究

人工智能背景下培养双师型教学人才有利于提高高职院校教学质量。本文总结了人工智能背景下高职院校提升教学质量以及保障规范化教学存在的问题,从完善培养条件、创新双师型教学培训内容、建立健全质量诊改制度以及整改规范化教学管理策略的角度提出了人工智能背景下高职院校实施规范化教学、提升教学质量的措施。

人工智能大模型发展趋势及电信运营商应对策略

以ChatGPT为代表的大规模预训练模型引发了AI的新一轮技术革命,可能成为新型信息产业的关键能力底座,对电信运营商数字化转型战略带来重大影响。本文从人工智能大模型发展现状、核心要素、技术趋势和关键影响进行分析,结合电信运营商策略动向,通过SWOT分析,提出了电信运营商在人才、技术、数据和商业模式等方面面临的问题,并提出加强自主技术攻关研发、推进大模型生态合作等应对策略建议。

人工智能技术在数字媒体艺术中的应用与创新

随着科学技术的进步和创新,人工智能技术在数字媒体艺术的相关领域展开应用,让数字媒体艺术站在更大的舞台上绽放耀眼的光芒。文章通过实践研究探讨了AI技术在数字媒体艺术具体领域中应用和创新发展的策略。