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Novel soft robotic finger model driven by electrohydrodynamic(EHD)pump

Journal of Zhejiang University-Science A(Applied Physics & engineering) 2024年第7期25卷 596-604页

作者： Xuehang BAI Yanhong PENG Dongze LI Zhuochao LIU Zebing MAO Department of Mechanical Automotive and Materials EngineeringFaculty of EngineeringUniversity of WindsorWindsorN9B 3P4Canada Department of Information and Communication Engineering Graduate School of EngineeringNagoya UniversityNagoya464-8601Japan Department of Intelligent Science and Technology College of Computer Science and TechnologyQingdao UniversityQingdao266071China Design and Research Institute of University of Science and Technology Beijing Co.Ltd. Beijing100083China Faculty of Engineering Yamaguchi UniversityYamaguchi755-8611Japan

1Introduction In the field of robotic-human interactions,soft robotics offers enhanced safety and adaptability.A major challenge in this area is the integration of soft actuators with pump systems,which often increases the system volume and *** study presents the development and testing of a robotic finger powered by electrohydrodynamic(EHD)*** leveraging the electric field-induced flow of dielectric fluids.

关键词： robotic complexity. pump

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Deep Learning Approaches for Malware Detection in the Industrial Internet of Things: A Comprehensive Analysis 25

Deep Learning Approaches for Malware Detection in the Indust...

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25th International Arab Conference on information technology, ACIT 2024

作者： Samara, Ghassan Fadda, Mohammad Abu Al-Mohtaseb, Abeer Alazaidah, Raed Alrefai Mohamed, N. Odeh, Mahmoud Aljaidi, Mohammad Shuaib, Mohammed Mahmod Kanan, Mohammad Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Science Department of Mathematics Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This survey aims to identify hazards associated with the Internet of Things (IoT), focusing on malware that can infiltrate various devices, applications, and systems within the Industrial Internet of Things (IIoT). Such malware enables malicious actors to manipulate these systems. This research utilizes deep learning as a method for detecting malicious programs. Additionally, it provides hypotheses and background information on deep learning and the IIoT, along with an overview of the types of malware that can compromise IoT applications. Finally, the study presents findings related to the research hypotheses and relevant topics. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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A Meta-Classifier Link Prediction Model for False Profile Identification in Facebook 1

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4th International Conference on Inventive Computation and information Technologies, ICICIT 2022

作者： Saranya, S. Rajalakshmi, M. Devi, S. Suruthi, R.M. Faculty of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India Faculty of Information Technology Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (数字)9789811974021

ISBN: (纸本)9789811974014

Nowadays, social networks have become part of our everyday routine to connect and communicate to diverse communities. The detection of abnormal vertices in these networks has become more vital in exposing network intruders or malicious users. In this paper, we have included a novel meta-classifier which will notice anomalous vertices in complicated interactive network topology by extracting useful patterns. We identify that a vertex with several link connections will incorporate a higher probability of being abnormal. Henceforth, we choose to apply the link prediction model on the Facebook dataset with high interconnectivity. In each step, we determine abnormal vertices are detected with minimal false positive estimates and better accuracy when compared to alternative prevailing methods. Hence, the method incorporated reveals the outliers in the social networks. We proposed an approach that can identify the false profiles with 93% accuracy and lower false positive rates with sustainable accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network topology

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Diagnostic Classification of Heart Disease Using Boosting Algorithms 4th

Diagnostic Classification of Heart Disease Using Boosting Al...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Srivastava, Dolley Pandey, Himanshu Agarwal, Ambuj Kumar Sharma, Richa Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819937158

Heart disease is rapidly overtaking other causes of death in India, and it is a major threat to both men and women. Among the top causes of mortality throughout the globe, heart disease ranks first. Therefore, it is crucial to accurately and rapidly forecast cardiac problems at an early stage to guarantee the lives of millions of people. In the realm of medicine, machine learning has become more important. The realm of machine learning is where the idea of boosting first appeared. The underlying premise is that by merging multiple cases into a more accurate forecast, the accuracy of a rather ineffective classifying tool may be improved. Subsequently, this overarching idea was implemented in statistical modeling. The purpose of this research is to evaluate and contrast the performance of four popular boosting algorithms for machine learning in the context of cardiac illness diagnosis and prediction. Test results show that among the models evaluated, XGBoost performed best in terms of classification accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Machine learning

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Prognostic Stacking Machine Learning Model for Intensive Care Unit Admission Prediction of COVID Patients 4th

Prognostic Stacking Machine Learning Model for Intensive Car...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Sharma, Richa Pandey, Himanshu Agarwal, Ambuj Kumar Srivastava, Dolley Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819937158

Statistical methods and models are the backbone of machine learning, which allows computers to autonomously discover and exploit data patterns. The ultimate objective is for computers to accurately forecast outcomes based on historical data. In this work, we present two related research projects: one on stacking and the other on boosting, two ensemble learning approaches. This research investigates its behavior from several angles and sheds light on its relationship to related ensemble learning schemes by showing that it is typically the best option and that stacking can be used to replicate the vast majority of ensemble learning schemes. The COVID-19 clinical dataset has been used to test these strategies for use in anticipating and preparing healthcare systems to avert collapse, where collapse is defined as an over-capacity demand of ICU beds. F1-measures stacking is superior to other methods in terms of accuracy, precision, and recall, as shown by the findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： COVID-19

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Lattice-Based Authentication Scheme to Prevent Quantum Attack in Public Cloud Environment

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computers, Materials & Continua 2023年第4期75卷 35-49页

作者： Naveed Khan Zhang Jianbiao Intikhab Ullah Muhammad Salman Pathan Huhnkuk Lim Faculty of Information Technology Beijing University of TechnologyBeijing100124China Lecture in Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science Maynooth UniversityMaynoothW23 A3HYIreland Department of Computer Engineering Hoseo UniversityAsan-si31499Korea

Public cloud computing provides a variety of services to consumersvia high-speed internet. The consumer can access these services anytimeand anywhere on a balanced service cost. Many traditional authenticationprotocols are proposed to secure public cloud computing. However, therapid development of high-speed internet and organizations’ race to developquantum computers is a nightmare for existing authentication schemes. Thesetraditional authentication protocols are based on factorization or discretelogarithm problems. As a result, traditional authentication protocols arevulnerable in the quantum computing era. Therefore, in this article, we haveproposed an authentication protocol based on the lattice technique for publiccloud computing to resist quantum attacks and prevent all known traditionalsecurity attacks. The proposed lattice-based authentication protocolis provably secure under the Real-Or-Random (ROR) model. At the sametime, the result obtained during the experiments proved that our protocol islightweight compared to the existing lattice-based authentication protocols,as listed in the performance analysis section. The comparative analysis showsthat the protocol is suitable for practical implementation in a quantum-basedenvironment.

关键词： Lattice authentication quantum attack ProVerif

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Deep Learning Techniques for Alzheimer's Disease Prediction 5

Deep Learning Techniques for Alzheimer's Disease Prediction

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Mahesh, T.R. Agarwal, Ankita Nair, Amrutha V. Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350383522

Deep learning, a cutting-edge approach of machine learning, surpasses classical machine learning when it comes to detecting particular structures in complicated, high-dimensional data in the area of computer vision. The fast improvement of neuroimaging technologies and the associated creation of huge volumes of multimodal neuroimaging data have led in a noteworthy surge in interest in the application of deep learning for automated categorization and early diagnosis of Alzheimer's disease (AD). The best classification results were achieved utilizing multimodal neuroimaging in conjunction with fluid biomarkers. © 2024 IEEE.

关键词： and Alzheimer's disease (AD) Deep learning feature selection gradient descent multimodal neuroimaging data performance evaluation repeatability

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Novel Algorithm for Mobile Robot Path Planning in Constrained Environment

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computers, Materials & Continua 2022年第5期71卷 2697-2719页

作者： Aisha Muhammad Mohammed A.H.Ali Sherzod Turaev Ibrahim Haruna Shanono Fadhl Hujainah Mohd Nashrul Mohd Zubir Muhammad Khairi Faiz Erma Rahayu Mohd Faizal Rawad Abdulghafor Faculty of Manufacturing Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechanical Engineering Faculty of EngineeringUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science and Software Engineering College of Information TechnologyUnited Arab Emirates UniversityAl AinUnited Arab Emirates Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang(UMP)Pekan26600Malaysia Department of Mechatronics Engineering Faculty of TechnologyBayero UniversityKano(BUK)700241Nigeria Computer Science and Engineering Department Chalmers and University of Gothenburg41296GothenburgSweden Department of Artificial Intelligence Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Computer Science Faculty of Information and Communication TechnologyInternational Islamic University Malaysia53100Kuala LumpurMalaysia

This paper presents a development of a novel path planning algorithm, called Generalized Laser simulator (GLS), for solving the mobilerobot path planning problem in a two-dimensional map with the presence ofconstraints. This approach gives the possibility to find the path for a wheelmobile robot considering some constraints during the robot movement inboth known and unknown environments. The feasible path is determinedbetween the start and goal positions by generating wave of points in alldirection towards the goal point with adhering to constraints. In simulation,the proposed method has been tested in several working environments withdifferent degrees of complexity. The results demonstrated that the proposedmethod is able to generate efficiently an optimal collision-free path. Moreover,the performance of the proposed method was compared with the A-star andlaser simulator (LS) algorithms in terms of path length, computational timeand path smoothness. The results revealed that the proposed method hasshortest path length, less computational time and the best smooth path. Asan average, GLS is faster than A∗ and LS by 7.8 and 5.5 times, respectivelyand presents a path shorter than A∗ and LS by 1.2 and 1.5 times. In orderto verify the performance of the developed method in dealing with constraints, an experimental study was carried out using a Wheeled Mobile Robot(WMR) platform in labs and roads. The experimental work investigates acomplete autonomous WMR path planning in the lab and road environmentsusing a live video streaming. Local maps were built using data from a live video streaming with real-time image processing to detect segments of theanalogous-road in lab or real-road environments. The study shows that theproposed method is able to generate shortest path and best smooth trajectoryfrom start to goal points in comparison with laser simulator.

关键词： Path planning generalized laser simulator wheeled mobile robot global path panning local path planning

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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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