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PSCS4CPP: A Generative PSCS Implementation for C++ 1

Model-Driven engineering and Software Development - 9th International Conference, MODELSWARD 2021, and 10th International Conference, MODELSWARD 2022, Revised Selected Papers

作者： Hammer, Maximilian Maschotta, Ralph Wichmann, Alexander Jungebloud, Tino Bedini, Francesco Zimmermann, Armin Systems and Software Engineering Group Computer Science and Automation Department Technische Universität Ilmenau Ilmenau Germany

ISBN: (数字)9783031388217

ISBN: (纸本)9783031388200

Since the early 2000s, Model-driven engineering has aimed to accomplish executable UML models. With fUML and PSCS, the Object Management Group (OMG) published standardized specifications of precise semantics for certain parts of UML in the form of metamodels, which form an execution environment. A certain characteristic of these environments is that static information about a model is analyzed and evaluated during runtime. With composite structures being a concept for describing structural properties of a model, the majority of execution semantics specified by PSCS concern analysis and processing of such static information about the model’s fine-grained structure. Thus, the PSCS specification appears suitable for a generative realization approach. Using Model-To-Text-Transformation to generate source code, which serves as an input for the actual execution environment, the runtime level of model executions can be relieved by outsourcing analysis and processing of static information to the level of code generation. By inserting this preprocessing step, the performance of the actual model execution at runtime can be improved. This paper introduces an implementation of the PSCS specification for C++ based on code generation using Model-to-Text-Transformation. This also includes a generative approach to realize an extension mechanism of the fUML and PSCS execution environments by introducing user-defined semantic execution strategies. The proposed PSCS implementation was developed as a part of the MDE4CPP project. Moreover, this paper presents a set of test models validating the correct functionality of the implementation as well as a performance benchmark. Finally, analysis results of an application example for the presented realization are evaluated and discussed. © 2023, Springer Nature Switzerland AG.

关键词： Specifications

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Deep neural network-based secure healthcare framework

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Neural Computing and Applications 2024年第28期36卷 17467-17482页

作者： Aldaej, Abdulaziz Ahanger, Tariq Ahamed Ullah, Imdad College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia Management Information Systems Department CoBA Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia School of Computer Science Faculty of Engineering The University of Sydney SydneyNSW2006 Australia

Healthcare stands out as a critical domain profoundly impacted by Internet of Things (IoT) technology, generating vast data from sensing devices as IoT applications expand. Addressing security challenges is paramount for a successful IoT healthcare framework, with blockchain technology offering a decentralized structure for robust data protection and secure data exchange within multi-node IoT networks. The research introduces a secure IoT healthcare diagnostic model empowered by deep neural networks, emphasizing encryption, safe transactions, and healthcare diagnostics as key components. Notably, the model incorporates innovative techniques like the orthogonal particle swarm optimization algorithm for sharing medical images and a neighborhood indexing sequence method for hash value encryption. The development of an optimized deep neural network-based classification model for illnesses, validated through extensive trials, demonstrates superior performance metrics compared to existing decision-making techniques, with significant improvements in f-Measure (96.25%), sensitivity (93.26%), specificity (94.26%), and accuracy (93.26%). This study’s scientific contribution lies in its innovative approach to securing IoT-healthcare diagnosis models, validated performance enhancements using real-world datasets, and insightful recommendations for future research directions, fostering advancements in healthcare technology for enhanced patient care and system efficiency. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Internet of things

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An Architecture-based Self-Typing Service for Cloud Native Applications 4

An Architecture-based Self-Typing Service for Cloud Native A...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Mandalapu, Srinivasa Rao Sivamuni, Kalaimagal Chitra Devi, D. Aswal, Upendra Singh Sherly, S. Irin Balaji, N. Alangudi R.V.R. and J.C. College of Engineering Department of Computer Science and Business Systems Chowdavaram522019 India Mk College of Engineering and Technology Department of Computer Science and Engineering Puduvoyal601206 India S.A.Engineering College Department of Computer Science and Engineering Thiruverkadu600077 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun248002 India Panimalar Engineering College Department of Information Technology Poonamallee600123 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram522302 India

ISBN: (纸本)9798350300888

This research study analyzes Kubow, a service that automatically adjusts to new circumstances, built on a modular framework for cloud-native applications. To accommodate Kubernetes and Docker containers, the Rainbow self-adaptation framework was upgraded to become Kubow. This research provides an overview of Kubow's architecture, highlights its main design decisions, and illustrates it with a simple example of how to set it up and utilize it. It takes more than just installing software on virtual machines to get apps running in the cloud. Cloud applications need continuous management so that they can 1) adjust their resources according to the demand, and 2) respond to temporary challenges by duplicating and restarting the components to offer resilience on unstable infrastructure. To give programmed and responsive responses to disappointments (wellbeing the board) and changing natural conditions (auto-scaling), and framework boundaries. © 2023 IEEE.

关键词： computer architecture

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Model for Recognition of Sign Language and Hand Gesture by Hard Hearing People

Model for Recognition of Sign Language and Hand Gesture by H...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural Networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech Technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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Detection of Sleep Apnea using Cat Swarm Optimization with TabNet versus CNN Model

Detection of Sleep Apnea using Cat Swarm Optimization with T...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Kujani, T. Arivubrakan, P. Usha, V. Alex David, S. Umamaheswari, R. Sathya, T. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798331541170

Sleep apnea is a sleep disease that is often an undetected cause of death. It is generally diagnosed with polysomnography, which can be costly and uncomfortable. To solve this issue, we suggest a website for detecting sleep apnea that uses user-provided data such as the Berlin questionnaire, Epworth Sleepiness Scale (ESS), body measurements, Body Mass Index (BMI), age, and gender. The purpose of this work is to categorize sleep apnea based on the Apnea-Hypopnea Index (AHI) as normal, mild, moderate, or severe. This classification is carried out using a deep learning model, TabNet which suits the best to process the text input. It could be useful for people who feel they have sleep apnea, allowing them to take the essential steps toward a formal diagnosis and therapy. However, this aids in early diagnosis by indicating the stage of the disease, and users are recommended to check with healthcare specialists for a definitive diagnosis. For feature selection, nature-inspired Cat Swarm optimization is applied. The TabNet is trained on a dataset that includes these characteristics as well as the AHI, a common metric for determining the severity of sleep apnea. The algorithm uses these inputs to forecast the likelihood of having sleep apnea: normal, mild, moderate, or severe. The developed TabNet Model is compared with the Convolution Neural Network (CNN) and the results are showcased. To summarize, this approach is a revolutionary technique for sleep apnea detection using Cat Swarm Optimization. It promises to give a more accessible and cost-effective solution for people who feel they have this illness by utilizing user-generated data and deep learning algorithms. © 2024 IEEE.

关键词： Sleep research

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Performance Analysis of Machine Learning Algorithms for Prediction of Cerebral Attack (Stroke) 7th

Performance Analysis of Machine Learning Algorithms for Pred...

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7th International Conference on Emerging Applications of Information Technology, EAIT 2022

作者： Sengupta, Diganta Mondal, Subhash Singh, Yash Raj Pandey, Amartya Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science and Business Systems Meghnad Saha Institute of Technology Kolkata700150 India

ISBN: (纸本)9789811951909

In this study, we have analysed the behaviour and performance of twelve machine learning (ML) algorithms in prediction of cerebral attack (stroke). The ML algorithms used were light gradient boosting machine (LGBM), histogram-based gradient boost (HGB), random forest (RF), gradient boost classifier (GBC), XGBoost (XGB), AdaBoost (AB), decision tree (DT), support vector machine (SVM), K-nearest neighbours (KNN), logistic regression (LR), Bernoulli Naïve Bayes and Gaussian Naïve Bayes. The dataset comprised of 5110 entries, which were subjected to pre-processing prior to deployment in the ML algorithms. We observed an accuracy of 97.36% for RF classifier which was the highest among the twelve algorithms under consideration. Post hyper-parameter tuning, GBC reflected an accuracy of 97.73%. Stacking resulted in an accuracy of 98.85% having GBC as the meta model and XGB, RF, SVM, KNN, DT, AB and LR as the base models. Hence, form our observation, we conclude that GBC performs best in predicting stroke among all the ML algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Low Resource Machine Learning Approach for Prediction of Dressler Syndrome 1

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International Conference on Intelligent systems and Human-Machine Collaboration, ICISHMC 2022

作者： Sengupta, Diganta Mondal, Subhash Chatterjee, Debosmita Pradhan, Susmita Sur, Pretha Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata700150 India Department of Computer Science and Business Systems Meghnad Saha Institute of Technology Kolkata700150 India

ISBN: (数字)9789811984778

ISBN: (纸本)9789811984761

Cosmopolitan lifestyle and livelihood modifications have marked a toll on human health to the extent of myocardial disease onset at a relatively tender stage. One of the major issues that have been observed on the rise is the arterial blockage leading to myocardial infarction. Immune response to the arterial damage or the pericardium is termed as Dressler syndrome. This study focuses on prediction of Dressler syndrome based on myocardial infarction historical data. Moreover, the study focuses on prediction using a resource constraint dataset through six popular machine learning (ML) algorithms. The dataset comprised of 124 features, and 1700 data, post-cleaning. Of all the 124 features, 12 features were target values. We selected one of the target values (Dressler syndrome) for this study. 10% of the data was reserved for test data at the initial stage itself, and the rest was further split into 0.7:0.3 for training and validation sets. RF presented a model accuracy of 98%, which is the best of all the six algorithms. In terms of AUC, RF exhibited the highest value of 0.995. Moreover, the models were further tuned, and the results confirmed the efficacy of RF for the classification of Dressler syndrome. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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