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Personality Trait detection via Transfer Learning

Computers, Materials & Continua 2024年第2期78卷 1933-1956页

作者： Bashar Alshouha Jesus Serrano-Guerrero Francisco Chiclana Francisco P.Romero Jose A.Olivas department of Information Technologies and Systems University of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial Intelligence School of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK

Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a

关键词： Personality trait detection pre-trained language model big five model transfer learning

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Transfer learning from T1-weighted to T2-weighted Magnetic resonance sequences for brain image segmentation

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 26-39页

作者： Imene Mecheter Maysam Abbod Habib Zaidi Abbes Amira Brunel university LondonUK Geneva university Hospital GenevaSwitzerland Geneva university GenevaSwitzerland university of Groningen GroningenNetherlands university of Southern denmark OdenseDenmark university of Sharjah SharjahUAE de montfort university LeicesterUK

Magnetic resonance(MR)imaging is a widely employed medical imaging technique that produces detailed anatomical images of the human *** segmentation of MR im-ages plays a crucial role in medical image analysis,as it enables accurate diagnosis,treatment planning,and monitoring of various diseases and *** to the lack of sufficient medical images,it is challenging to achieve an accurate segmentation,especially with the application of deep learning *** aim of this work is to study transfer learning from T1-weighted(T1-w)to T2-weighted(T2-w)MR sequences to enhance bone segmentation with minimal required computation *** the use of an excitation-based convolutional neural networks,four transfer learning mechanisms are proposed:transfer learning without fine tuning,open fine tuning,conservative fine tuning,and hybrid transfer ***,a multi-parametric segmentation model is proposed using T2-w MR as an intensity-based augmentation *** novelty of this work emerges in the hybrid transfer learning approach that overcomes the overfitting issue and preserves the features of both modalities with minimal computation time and *** segmentation results are evaluated using 14 clinical 3D brain MR and CT *** results reveal that hybrid transfer learning is superior for bone segmentation in terms of performance and computation time with DSCs of 0.5393±*** T2-w-based augmentation has no significant impact on the performance of T1-w MR segmentation,it helps in improving T2-w MR segmentation and developing a multi-sequences segmentation model.

关键词： computer vision convolution image segmentation learning(artificial intelligence)

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Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

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Chinese Journal of Aeronautics 2024年第2期37卷 123-136页

作者： Zhe HUI Yinan KONG Weigang YAO Gang CHEN School of Aerospace Science and Technology Xidian UniversityXi’an 710071China Computation Aerodynamics Institution China Aerodynamics Research and Development CenterMianyang 621000China Faculty of Computing Engineering and MediaDe Montfort UniversityLeicester LE19BHUnited Kingdom School of Aerospace Engineering Xi’an Jiaotong UniversityXi’an 710049China

To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data,this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory(LSTM)network model and the Levenberg-Marquardt(LM)*** stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input-output data of the aircraft system without requiring explicit postulation of the *** LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic *** proposed method is applied by using the real flight data,generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle(UAV).The investigation reveals that for the two different flight data,the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters(i.e.,the initial weights,initial biases,number of hidden cells,time-steps,learning rate,and number of training iterations).Besides,the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.

关键词： Parameter estimation LSTM network model LM method Aerodynamic parameters Flight data Aircraft dynamics modeling Network prediction capability Network parameters

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A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser Alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim Alzahrani Ali H.Al-Bayatti Computer Science department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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Evolutionary Multi/Many-Objective Optimisation via Bilevel decomposition

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1973-1986页

作者： Shouyong Jiang Jinglei Guo Yong Wang Shengxiang Yang School of Automation Central South UniversityChangsha 410083China department of Computing Science University of AberdeenAberdeen AB243FXUK School of Computer Science Central China Normal UniversityWuhan 430079China School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK

decomposition of a complex multi-objective optimisation problem(MOP)to multiple simple subMOPs,known as M2M for short,is an effective approach to multi-objective ***,M2M facilitates little communication/collaboration between subMOPs,which limits its use in complex optimisation *** paper extends the M2M framework to develop a unified algorithm for both multi-objective and manyobjective *** bilevel decomposition,an MOP is divided into multiple subMOPs at upper level,each of which is further divided into a number of single-objective subproblems at lower *** subMOPs are allowed to share some subproblems so that the knowledge gained from solving one subMOP can be transferred to another,and eventually to all the *** bilevel decomposition is readily combined with some new mating selection and population update strategies,leading to a high-performance algorithm that competes effectively against a number of state-of-the-arts studied in this paper for both multiand many-objective *** analysis and component analysis have been also carried out to further justify the proposed algorithm.

关键词： Bilevel decomposition evolutionary algorithm many-objective optimisation multi-objective optimisation

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Effects of Enzymatic Treatments with a Protease and a Total Crude Cellulase on the Handle and Prickle of a Viscose/Wool Blend Furnishing Fabric

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Journal of China Textile university(English Edition) 2000年第2期17卷 1-6页

作者：沈劲松 de montfort univ Leicester United Kingdom

Carefully controlled treatments with a protease and acellulase were used to improve the softness and reducethe subjectively perceived prickle of a coarse wool andviscose blend fabric. The activity towards wool of a non-specific endoprotease (Novolan L) was shown to bemuch greater in buffer containing a reducing agent (so-dium sulphite). The balance between protease and sodi-um sulphite concentrations was studied in order to reducethe amount of enzyme required.

关键词： enzymes wool prickle handle

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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A review of salivary composition changes induced by fasting and its impact on health

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Food Science and Human Wellness 2024年第1期13卷 50-64页

作者： Nurul Fadhilah Kamalul Aripin NIdayu Zahid Mohd Aizat Abdul Rahim Hashim Yaacob Parvez I.Haris Zubaidah Haji AbdRahim Rauzah Hashim School of Chemical Engineering College of EngineeringUniversiti Teknologi MARAShah Alam 40450Malaysia Centre for Fundamental and Frontier Sciences in Nanostructure Self-Assembly Department of ChemistryFaculty of ScienceUniversiti MalayaKuala Lumpur 50603Malaysia department of Oral and Craniofacial Sciences Faculty of DentistryUniversiti MalayaKuala Lumpur 50603Malaysia UMSC dental Specialist Clinic UM Dentistry TowerUniversiti MalayaMedical CentreKuala Lumpur 50603Malaysia Leicester School of Allied Health Sciences De Montfort UniversityLeicester LE19BHUnited Kingdom Chancellery(Research and Innovation) Universiti MalayaKuala Lumpur 50603Malaysia

Human saliva is an indispensable fluid that maintains a healthy oral cavity which otherwise can lead to oral diseases(dental caries and periodontitis).In addition,salivary metabolites and microbiome profile provide early detection of systemic diseases such as cancer and *** diagnostic has gained popularity due to its non-invasive sampling ***(abstinence from food or drink or both)research for weight loss and improve health is common,but studies using fasting saliva are *** metabolites in fasting saliva have been reported with interesting results,which can be enhanced by considering different confounding *** example,fasting saliva contains higher salivary nitrite,which is related to nitric oxide(NO).NO is a vasodilator supporting the healthy function of endothelial cells and its deficiency is connected to many *** timely supply of NO through exogenous and endogenous means is highlighted and the potential advantage of fasting salivary composition changes in relation to COVID-19 infection is *** review aims to provide a general discussion on the salivary composition,properties,and functions of the whole saliva,including the health benefits of fasting.

关键词： Saliva Fasting Nitrate Nitrite Nitric oxide

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Output Feedback Stabilization for MIMO Semi-linear Stochastic Systems with Transient Optimisation

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International Journal of Automation and computing 2020年第1期17卷 83-95页

作者： Qi-Chun Zhang Liang Hu John Gow School of Engineering and Sustainable development De Montfort UniversityLeicester LE19BHUK School of Computer Science and Informatics De Montfort UniversityLeicester LE19BHUK

This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output(MIMO)semi-linear stochastic systems.A control algorithm is presented via an m-block backstepping controller design where the closed-loop system has been stabilized in a probabilistic sense and the transient performance is optimisable by optimised by searching the design parameters under the given *** particular,the transient randomness and the probabilistic decoupling will be investigated as case *** that the presented control algorithm can be potentially extended as a framework based on the various performance *** evaluate the effectiveness of this proposed control framework,a numerical example is given with simulation *** summary,the key contributions of this paper are stated as follows:1)one block backstepping-based output feedback control design is developed to stabilize the dynamic MIMO semi-linear stochastic systems using a linear estimator;2)the randomness and probabilistic couplings of the system outputs have been minimized based on the optimisation of the design parameters of the controller;3)a control framework with transient performance enhancement of multi-variable semi-linear stochastic systems has been discussed.

关键词： Multi-input-multi-output(MIMO)stochastic systems output feedback stabilisation block backstepping randomness attenuation probabilistic decoupling mutual information

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Towards a Service-Oriented Architecture for a Mobile Assistive System with Real-time Environmental Sensing

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Tsinghua Science and Technology 2016年第6期21卷 581-597页

作者： Darpan Triboan Liming Chen Feng Chen Zumin Wang the Context Intelligence and Interaction Research Group (CIIRG) De Montfort University the department of Information Engineering Dalian University

With the growing aging population, age-related diseases have increased considerably over the *** response to these, Ambient Assistive Living（AAL） systems are being developed and are continually evolving to enrich and support independent living. While most researchers investigate robust Activity Recognition（AR）techniques, this paper focuses on some of the architectural challenges of the AAL systems. This work proposes a system architecture that fuses varying software design patterns and integrates readily available hardware devices to create Wireless Sensor Networks（WSNs） for real-time applications. The system architecture brings together the Service-Oriented Architecture（SOA）, semantic web technologies, and other methods to address some of the shortcomings of the preceding system implementations using off-the-shelf and open source components. In order to validate the proposed architecture, a prototype is developed and tested positively to recognize basic user activities in real time. The system provides a base that can be further extended in many areas of AAL systems,including composite AR.

关键词： Activities of Daily Living (ADL) Service-Oriented Architecture (SOA) semantic web ontology modeling Web Ontology Language (OWL) Activity Recognition (AR) Smart Homes (SH) Wireless Sensor Networks (WSNs)

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