Sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays

In this letter, a distributed protocol for sampled-data synchronization of coupled harmonic oscillators with controller failure and communication delays is proposed, and a brief procedure of convergence analysis for such algorithm over undirected connected graphs is provided. Furthermore, a simple yet generic criterion is also presented to guarantee synchronized oscillatory motions in coupled harmonic oscillators. Subsequently, the simulation results are worked out to demonstrate the efficiency and feasibility of the theoretical results.

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.

The Investigation of Jordanian Education Ministry Employees’ Attitude toward the Using of Cloud ERP

ERP systems have become an optimal solution for companies to perform their works with maximum advantages of Enterprise Resources Planning (ERP). Moreover, even though the cloud computing has many obstacles which need to be solved, but the enterprises always embrace the cloud. Many enterprises tend to adopt the cloud computing paradigm in order to get its leverage in successful and benefits. With the development of cloud computing technology, there is a growing orientation to move the ERP from inside boundaries of organizations into the cloud computing technologies. In addition, there were many studies that covered the cloud ERP without concentrating on acceptance the end user for this technology. Thus, this study comes to increase the understanding the attitude of the end user for cloud ERP technology. The main aim of this study is to investigate the employees’ attitude towards use of cloud ERP. The conceptual research framework of cloud ERP is prepared based on the technology acceptance model (TAM). The employees of Queen Rania Center (QRC) in Jordanian Education Ministry used the cloud ERP system which implemented according to this study. The questionnaires are distributed online to the sample. The results indicate that the QRC employees enjoyed using the cloud ERP system.

A Novel Outlier Detection with Feature Selection Enabled Streaming Data Classification

Due to the advancements in information technologies,massive quantity of data is being produced by social media,smartphones,and sensor devices.The investigation of data stream by the use of machine learning(ML)approaches to address regression,prediction,and classification problems have received consid-erable interest.At the same time,the detection of anomalies or outliers and feature selection(FS)processes becomes important.This study develops an outlier detec-tion with feature selection technique for streaming data classification,named ODFST-SDC technique.Initially,streaming data is pre-processed in two ways namely categorical encoding and null value removal.In addition,Local Correla-tion Integral(LOCI)is used which is significant in the detection and removal of outliers.Besides,red deer algorithm(RDA)based FS approach is employed to derive an optimal subset of features.Finally,kernel extreme learning machine(KELM)classifier is used for streaming data classification.The design of LOCI based outlier detection and RDA based FS shows the novelty of the work.In order to assess the classification outcomes of the ODFST-SDC technique,a series of simulations were performed using three benchmark datasets.The experimental results reported the promising outcomes of the ODFST-SDC technique over the recent approaches.

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

Deep LearningModel for Big Data Classification in Apache Spark Environment

Big data analytics is a popular research topic due to its applicability in various real time applications.The recent advent of machine learning and deep learning models can be applied to analyze big data with better performance.Since big data involves numerous features and necessitates high computational time,feature selection methodologies using metaheuristic optimization algorithms can be adopted to choose optimum set of features and thereby improves the overall classification performance.This study proposes a new sigmoid butterfly optimization method with an optimum gated recurrent unit(SBOA-OGRU)model for big data classification in Apache Spark.The SBOA-OGRU technique involves the design of SBOA based feature selection technique to choose an optimum subset of features.In addition,OGRU based classification model is employed to classify the big data into appropriate classes.Besides,the hyperparameter tuning of the GRU model takes place using Adam optimizer.Furthermore,the Apache Spark platform is applied for processing big data in an effective way.In order to ensure the betterment of the SBOA-OGRU technique,a wide range of experiments were performed and the experimental results highlighted the supremacy of the SBOA-OGRU technique.

Short Text Mining for Classifying Educational Objectives and Outcomes

Most of the international accreditation bodies in engineering education(e.g.,ABET)and outcome-based educational systems have based their assess-ments on learning outcomes and program educational objectives.However,map-ping program educational objectives(PEOs)to student outcomes(SOs)is a challenging and time-consuming task,especially for a new program which is applying for ABET-EAC(American Board for Engineering and Technology the American Board for Engineering and Technology—Engineering Accreditation Commission)accreditation.In addition,ABET needs to automatically ensure that the mapping(classification)is reasonable and correct.The classification also plays a vital role in the assessment of students’learning.Since the PEOs are expressed as short text,they do not contain enough semantic meaning and information,and consequently they suffer from high sparseness,multidimensionality and the curse of dimensionality.In this work,a novel associative short text classification tech-nique is proposed to map PEOs to SOs.The datasets are extracted from 152 self-study reports(SSRs)that were produced in operational settings in an engineering program accredited by ABET-EAC.The datasets are processed and transformed into a representational form appropriate for association rule mining.The extracted rules are utilized as delegate classifiers to map PEOs to SOs.The proposed asso-ciative classification of the mapping of PEOs to SOs has shown promising results,which can simplify the classification of short text and avoid many problems caused by enriching short text based on external resources that are not related or relevant to the dataset.

Internet of Vehicles in Big Data Era

As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

Infinite Gaussian mixture process is a model that computes the Gaus-sian mixture parameters with order.This process is a probability density distribu-tion with adequate training data that can converge to the input density curve.In this paper,we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling.A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling.The solution uses data mining techniques to link datum in groups by linking their components.The learning techniques are novel presentations of Gaussian model-ling that use prior knowledge of the representation hyper-parameters and approx-imate them in a closed form.Experiments are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Ara-bic patterns.Experiments are also performed on the application of facial expres-sion recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.

Optimal Deep Belief Network Enabled Malware Detection and Classification Model

Cybercrime has increased considerably in recent times by creating new methods of stealing,changing,and destroying data in daily lives.Portable Docu-ment Format(PDF)has been traditionally utilized as a popular way of spreading malware.The recent advances of machine learning(ML)and deep learning(DL)models are utilized to detect and classify malware.With this motivation,this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification(MFODBN-MDC)technique.The major intention of the MFODBN-MDC technique is for identifying and classify-ing the presence of malware exist in the PDFs.The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets.In addition,Adamax optimizer with the DBN model is used for PDF malware detection and classification.The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classi-fication demonstrates the novelty of the work.For demonstrating the improved outcomes of the MFODBN-MDC model,a wide range of simulations are exe-cuted,and the results are assessed in various aspects.The comparison study high-lighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision,recall,and F1 score of 97.42%,97.33%,and 97.33%,respectively.

Identifying Cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.Cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classification(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classifier produces higher classification accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

人工智能赋能大学治理:多重效应与治理效能转化

当人工智能与大学治理相遇后,数字化不仅成为提升大学治理能力和治理水平现代化的客观要求,还成为推进大学治理创新和制度变迁的重要途径。根据大学制度的层级结构、治理结构的复杂性程度以及治理事项的数字化程度,建构制度嵌入下人工智能技术赋能大学治理的解释模型,并借此模型着重分析人工智能技术对大学治理影响的多重效应及其治理效能转化机制/过程。研究发现,在制度嵌入和人工智能技术的共同影响下,现代大学治理随着大学制度外显性的不断加强而愈显复杂,在此渐进过程中,人工智能技术的影响力和渗透力也就愈加微弱,由此产生了人工智能技术对大学治理影响的多重效应及其治理效能的不同转化和提升机制:大学治理结构的复杂性程度越低,大学制度的层级结构越低,治理领域可数字化的程度就越高,人工智能技术对大学治理的积极效应越显著,大学具体制度得以更迭的可能性越大,也就愈容易借助“技术—制度”协同机制将中国特色现代大学的制度优势转化为治理效能;大学治理结构的复杂化程度越高,大学制度的层级结构越高,治理领域可数字化的程度越低,人工智能技术对大学治理的抑制效应越明显,其所依靠的(部分)基本制度和基础制度被替代/更迭的速度越慢,而被同化的可能性越大,也就越容易走上模仿西方大学制度建设的路径依赖。为此,一方面,要注重人工智能技术对大学治理的积极效应,充分发挥人工智能技术对于大学常规性治理、(部分)决策性治理的积极作用,通过大学具体制度和基本制度的不断健全和完善,推动现代大学制度的标准化建设和精细化发展;另一方面,要警惕人工智能技术对大学治理带来的潜在危险,充分考虑现代大学的组织特性,避免过度技术化而导致大学治理中技术应用的无限拓展和无序泛化。

“三融五链”理念下职业教育技能型人才培养路径研究

随着产业数字化转型和智能化改造的不断深入,加快培养适应新业态、新工艺和新流程的高素质技术技能型人才,成为赋能区域经济高质量发展的关键所在。为了改善职业教育在人才培养过程中的单一育人主体、校企联动不够深入、人才成长通道不畅、社会认可度不高等现实困境,以技能型人才培养为主线,以职普融通、产教融合、科教融汇的“三融”理念为契机,以推动教育链、产业链、供应链、人才链和价值链的“五链”衔接为目标,突破职业院校管理边界,构建起现代化、系统化的技能型人才培养体系。具体措施包括:以搭建产教融合多方育人框架为前提,以凝聚科教融汇一体化育人模式为载体,以健全职普融通人才成长体系为保障,以筑牢社会技能训练路径为引擎,以促进社会认同为助力,实现技能型人才培养全流程重构。逐步为区域经济提供源源不断的人力资源支撑和应用型技术积累,提升区域产业供应链韧性水平,促进区域产业攀升到价值链高端位置。

基于“五寓”课程思政理念的中医药人才培养策略研究——以“中药炮制学专论”课程为例

中药学类研究生课程重视中药学思维的培养,具有课程思政挖掘的良好基础,但课程思政缺乏系统设计,思政教育与专业教育融入未形成长效机制,专业课程考核和评价欠缺“课程思政”相关指标,“两张皮”问题仍然存在.为此以“中药炮制学专论”课程为例,课程团队践行“五寓”课程思政理念,通过对教学内容进行重构,改进教学方法,拓宽教育场域,实现思政目标与知识目标、能力目标和素质目标“同频共振”.“五寓”课程思政理念的凝练针对性地解决了研究生课程思政教与学中的问题,为中药学学科课程思政建设提供了保障.

多元智能视角下地方高校“实践基地+创新社团”工程教育模式探究

以霍华德·加德纳多元智能理论为理论基础,以地方应用型高校学生工程教学实践创新能力培养为目标,提出了“实践基地+创新社团”工程教育模式。在该模式下,以项目目标为驱动力,实践基地平台的实验设备、实验项目等为学生创新活动提供优良的环境和机会,在教师的组织、协调和引导下,通过创新社团的形式组织大学生以小组合作的形式进行自主创新。“实践基地+创新社团”工程教育模式是对课堂教学的一种有效补充,也是大学生自主创新和协作能力培养的有效手段。

立足中国,面向世界:新文科建设理论研究回顾与展望

新文科建设恰逢全面推进中国式现代化的新征程,肩负着建构中国自主知识体系与人才自主培养体系的重大使命,迄今已经走过五年历程。五年来,新文科建设理论研究正在经历一个从概念理念的厘清建构到实践落地、经验凝练、模式形成的发展过程。在此基础上,本文从新文科建设理论研究进展(2018—2023)和文科创新发展海外研究动态(1980—2023)两个维度分别进行历时分析与重点解读。展望未来,新文科建设理论研究应融入时代,发出新文科建设的中国之声;以世界为参照,建构文科教育现代化的中国学派;以强国建设为目标,加快文科理论创新与实践应用。

课程思政视域下大学英语写作困难探析

依据写作错误分析和批判思辨能力的文化和教育属性,通过定量和定性相结合的混合研究方法,探索思政视角下的大学英语写作困难。学生在语法、词汇、句法和语义等语言层面上存在较多写作错误,而语内迁移和语际迁移是造成写作错误的主要原因;学生缺乏一定的写作积极性,写作内容缺乏一定的思想内涵和批判性思维品质。大学英语写作困难的探析应该兼顾语言和认知,以课程思政为抓手,提高学生写作水平,引导学生用英语展示文化自信和家国情怀。

外语专业研究生“六位一体”系统性课程思政与德育培养策略探究

作为“三全育人”的重要举措,课程思政在研究生培养过程中发挥着重要作用。外语专业研究生由于其专业特性有着独特的德育教育特点,因此,在研究生培养过程中要针对其特点做好人才培养方案和教学大纲的修订,在教材改编、课堂教学、课后活动、专业实践和科学研究等教学活动中融入思政元素,六位一体进行系统性的课程思政,全面多元地推进研究生的德育培养工作。

农业高校大数据管理与应用专业本科人才培养探析

本科生教育是国家高等教育的重要组成部分。随着近年来我国新农科建设目标逐步明确,培养农业高校大数据管理与应用专业本科人才对农业经济与社会发展意义重大。为全面了解我国大数据管理与应用专业未来发展趋势和特征,本文运用CiteSpace对2005—2022年CNKI收录的500篇相关主题文献进行可视化知识图谱分析,发现该领域整体发文量呈上升趋势;我国学者主要在大数据、人才培养、创新应用、专业建设等方面开展研究;研究机构主要为大学和科技公司;不同时期的研究热点有所变化。最后,本文提出针对性的人才培养思路,以期培养出具有农业特色的大数据专业人才。