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A Three-Stage Cutting Simulation System Based on Mass-Spring Model

computer Modeling in engineering & Sciences 2021年第4期127卷 117-133页

作者： Xiaorui Zhang Jiali Duan Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China Faculty of Information Technology University of Information Technology and ManagementRzeszow35-225Poland

The cutting simulation of soft tissue is important in virtual *** includes three major challenges in computation:Soft tissue simulation,collision detection,and handling,as well as soft tissue *** order to address the earlier challenges,we propose a virtual cutting system based on the mass-spring *** this system,MSM is utilized to simulate the soft tissue *** stress is introduced to the model for simulating the shrinking effect of soft tissue in ***,a cylinder-based collision detection method is used to supervise the collision between surgical tools and soft ***,we simulate the cutting operation with a three-stage cutting method with swept volume,B´ezier curve,and an algorithm named shortest distance nodes matching *** order to verify the system performance,we carry out three validation experiments on the proposed system:Cutting accuracy experiment,collision detection validation,and practical cutting *** indicate that our system can well perform the shrinking effect of soft tissue in *** system has fast and accurate collision ***,the system can reconstruct smooth incisions vividly.

关键词： Mass-spring model collision algorithm virtual surgery soft tissue simulation

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Emotion Recognition System via Facial Expressions and Speech Using Machine Learning and Deep Learning Techniques

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SN computer Science 2023年第4期4卷 363页

作者： Chaudhari, Aayushi Bhatt, Chintan Nguyen, Thanh Thi Patel, Nisarg Chavda, Kirtan Sarda, Kalind U & P U. Patel Department of Computer Engineering Chandubhai S. Patel Institute of Technology Charotar University of Science And Technology (CHARUSAT) Gujarat Changa 388421 India Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Knowledge Corridor Gujarat Gandhinagar 382007 India School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Victoria 3216 Australia Bishop University 2600 College St Sherbrooke J1M 1Z7 QC Canada Crest Data Systems Private Limited Gandhinagar Highway Makarba Gujarat Ahmedabad 380051 India Programmer Analyst Meditab Software Pvt Ltd Kalasagar Mall Ghatlodiya Gujarat Ahmedabad 380061 India

Patients in hospitals frequently exhibit psychological issues such as sadness, pessimism, eccentricity, and anxiety. However, hospitals normally lack tools and facilities to continuously monitor the psychological health of patients. It is desirable to identify depression in patients so that it can be managed by instantly providing better therapy. This can be possible by advances in machine learning for image processing with notable applications in the domain of emotion recognition using facial expressions. In this paper, we have proposed two different methods, i.e. facial expression detection and voice analysis, to predict emotions. For facial expression recognition, we have used two approaches, one is the use of Gabor filters for feature extraction with support vector machine for classification and another is using convolutional neural network (CNN). For voice analysis, we extracted mel-frequency cepstral coefficients from speech data and, based on those features, predicted the emotions of the speech using a CNN model. Experimental results show that our proposed emotion recognition methods obtained high accuracy and thus could be potentially deployed to real-world applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep learning Expressions Facial emotion Machine learning Speech SVM

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Haram Kamal Bakhtawar Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Muhammad Atif Yaqub Hooria Umar Hoor Fatima Usama Nasir Computer Engineering Department Information Technology University Lahore Pakistan Department of Computer Science Bahria University Faculty of Information Technology University of Central Punjab Lahore Department of Artificial Intelligence University of Management & Technology Lahore Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Software Developer Pakistan Bahria University

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The heart plays a pivotal role in the functioning of living organisms, making its diagnosis and prediction of related diseases a matter of utmost importance. Approximately 17.9 million individuals succumb to cardiovascular disease, accounting for 32% of worldwide fatalities. This global concern highlights the importance of early detection, as timely treatment can significantly reduce heart disease-related mortality. Errors in diagnosis can lead to severe consequences, including fatigue or even death. The increasing prevalence of heart-related diseases necessitates the development of precise prediction systems to enhance awareness. Machine learning, a subset of Artificial Intelligence (AI), offers robust tools for predicting various events based on patterns learned from natural occurrences. This research paper evaluates the accuracy of machine learning algorithms, specifically k- nearest neighbor, decision tree, linear regression, and support vector machine (SVM), in predicting heart disease. We utilize the UCI repository dataset for training and testing and employ the Python programming language through the Anaconda Jupyter notebook for implementation and the experimental findings indicate an accuracy rate of 95.7% using the heart disease prediction model, leveraging its extensive libraries and header files to ensure accuracy and precision in our analysis.

关键词： Heart Support vector machines Training Accuracy Machine learning algorithms Predictive models Prediction algorithms Reliability Diseases Testing

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Streamlining software Release Process and Resource Management for Microservice-based Architecture on multi-cloud

Streamlining Software Release Process and Resource Managemen...

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IEEE International Conference on Multi Topic

作者： Isuru Pathum Herath Samuditha Jayawardena Ahamed Fadhil Nuwan Kodagoda Udara Srimath S.Samaratunge Arachchillage Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Science Sri Lanka Institute of Information Technology Sri Lanka

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry’s automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components.

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A Small-Signal Stability Criterion for a Power System Dynamic Model with Constant Power Loads

SSRN

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SSRN 2024年

作者： Levron, Yoash Belikov, Juri Weiss, George The Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Software Science Tallinn University of Technology Tallinn12618 Estonia School of Electrical Engineering Tel-Aviv University Tel-Aviv69978 Israel

This theoretical paper continues a long line of research works about the stability of power systems represented by reduced order models. We provide a small-signal stability criterion for a dynamic model that uses time-varying phasors, and is characterized by a lossless transmission network and constant power loads located anywhere in the network. A key concept in this study is the matrix H of the partial derivatives of the generator powers with respect to their relative angles. Our main result is that the local stability of any equilibrium point is completely determined by properties of this matrix H. More specifically, we show that an equilibrium point is asymptotically stable if H is positive definite, and is unstable if H is full-rank and not positive definite. In addition, we also prove several relations between the eigenvalues of H and the eigenvalues of the system's Jacobian matrix. These results are demonstrated on a number of case-studies, and compared with related earlier ones from the literature. © 2024, The Authors. All rights reserved.

关键词： Electric power transmission networks

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Information Theoretic-based Feature Selection (IFS) for Machine Learning 1

Information Theoretic-based Feature Selection (IFS) for Mach...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Suleiman, Muhammad A. Labadin, Jane Nile University of Nigeria Faculty of Natural and Applied Sciences Dept. of Software Engineering and Information Technology Abuja Nigeria University Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Malaysia

ISBN: (纸本)9781665434935

Mutual Information (MI) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating MI estimator for regression tasks exist, despite the existence of numerous methods of MI estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing MI estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate MI based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed IFS method. The first test is designed to assess the correctness of the formulated MI estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features. © 2021 IEEE.

关键词： Feature Selection

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Systematizing the Meta-Analytical Process in software engineering 21

Systematizing the Meta-Analytical Process in Software Engine...

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Proceedings of the 2021 European Symposium on software engineering

作者： Letizia Jaccheri Zamira Kholmatova Giancarlo Succi Department of Computer Science Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer Science Lab for Industrializing the Production of Software Innopolis University Russia

ISBN: (纸本)9781450385060

The generalization of knowledge is a necessary part of every scientific field. Meta-analysis is already advocated as a tool for generalization in different areas such as medicine, psychology, business, and this process is already standardized for them. software engineering started using meta-analysis as a tool for aggregating results from families of experiments, but not so long for generalization of results coming from different studies, and for this purpose, the meta-analytical approach is not yet clarified. In this paper, we attempt to systematize the application of meta-analysis as a secondary study to the software engineering field suggesting our preliminary protocol. To see the reliability of the proposed protocol we conducted several studies using it. Following even uniform protocol with these studies, we identified the issues preventing the wide usage of meta-analysis in software engineering and proposed our solutions for them.

关键词： Meta-analysis in software engineering Evidence-based software engineering Research synthesis in software engineering

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Classification of IncRNA and mRNA using k-mers and random forest 4

Classification of IncRNA and mRNA using k-mers and random fo...

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4th International Conference on Innovative Computing, ICIC 2021

作者： Nadir, Rana M. Mateen, Hafsa Din, Saif U University of Management and Technology Department of Computer Sciences Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan University of Management and Technology Software Engineering Department Lahore Pakistan

ISBN: (纸本)9781665400916

The Long non-coding RNA is involved important biological process in our body but dark part is it also involve in some major deceases like cancer. Very few research is done on long non-coding RNA due to its structural resemblance with mRNA, which is protein coding RNA. A proper distinction model between mRNA and Long non-coding RNA is required to do further research on Long non-coding RNA and its association with ***: First, k-mers with different k values are extracted and directly tested with our model for best selection of k-mers features. Moreover, the k-mers with high accuracy score are combined in one pool and screened with the help of extra tees classifier to identify most relevant features. Furthermore short listed features are given to our random forest (RF) classifier to extract classification results for lncRna and mRna sequences of humans. Finally, over classifier is compared with other machine learning classifiers like State Vector Machine (SVM), Neural Network (NN) and K Nearest Neighbor (KNN). Results proves that our proposed model score highest accuracy, precision, recall and ***: We have established the RF classifier on the bases of combination of 2 and 3 k-mers features, which provides the accuracy of 0.9984. Thus this score is better than Convolutional Neural Network (CNN) based and all previous proposed classification approaches. © 2021 IEEE.

关键词： RNA

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Mimd Programs Execution Support on Simd Machines

SSRN

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SSRN 2023年

作者： Mustafa, Dheya Alkhasawneh, Ruba Obeidat, Fadi Shatnawi, Ahmed S. Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa13133 Jordan Department of Communication and Computer Engineering Faculty of Engineering Al-Ahliyya Amman University Amman19111 Jordan Synopsys Inc. AustinTX United States Department of Software Engineering Jordan University of Science and Technology Irbid21110 Jordan

Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power, and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (API) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of collaborative execution of GPUs with CPUs and XeonPhi is presented in this paper. It is believed that this study will be beneficial for developers and researchers in the field of computer architecture and parallel computing-intensive scientific applications, specifically for early-stage high-performance computing researchers to obtain a brief of performance optimization opportunities as well as challenges of existing SIMD platforms. © 2023, The Authors. All rights reserved.

关键词： Program processors

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Nuclei Instance Segmentation in Colon Histology Images with YOLOv7 2nd

Nuclei Instance Segmentation in Colon Histology Images with...

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2nd International Conference on Advanced engineering, technology and Applications, ICAETA 2023

作者： Yıldız, Serdar Memiş, Abbas Varlı, Songül Department of Computer Engineering Faculty of Electrical and Electronics Engineering Yıldız Technical University İstanbul Turkey Department of Software Engineering Faculty of Engineering and Natural Sciences İstanbul Health and Technology University İstanbul Turkey Health Institutes of Türkiye İstanbul Turkey BİLGEM TÜBİTAK Kocaeli Turkey

ISBN: (纸本)9783031509193

In histology image analysis, instance-based nuclei segmentation is one of the challenging tasks within the segmentation-guided studies since it is quite troublesome to detect each distinct nuclei instance of each nuclei type in images in contrast to the semantic segmentation in which all the image pixels of a nuclei type are labelled with the same mask ID although the segmented region may comprise of multiple instances. In this paper, an instance-based medical image segmentation task is addressed, and in this context, instances of multiple types of nuclei in colon histology images are aimed to be delineated distinctly. For the instance-based segmentation of the nuclei in colon histology images, the YOLOv7 algorithm and its built-in instance segmentation module are utilized. In the experimental studies performed on Colon Nuclei Identification and Counting (CoNIC) Challenge 2022 colon histology image dataset by using a 5-fold cross-validation performance evaluation strategy, nuclei instances belonging to 6 classes as the neutrophil, epithelial, lymphocyte, plasma, eosinophil and connective were segmented. To calculate the overall system accuracy, the quantification metrics of mean average precision (mAP) and mean panoptic quality (mPQ) were measured. In performance evaluations, quite promising accuracy values were obtained. The mAP values of 0.2885 and 0.2903, and mPQ values of 0.1659 and 0.1704 were observed by using the YOLOv7 algorithm. To the best of our knowledge, this is the first nuclei instance segmentation study with YOLOv7. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Colon histology images CoNIC 2022 dataset Nuclei instance segmentation Nuclei segmentation YOLOv7

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