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2019 International Conference on computer, Control, Electrical, and Electronics engineering, ICCCEEE 2019

作者： Mahmoud, Zeinab Solyman, Aiman Elhag, Arafat Abdulgader Mohammed Faculty of Science and Technology Nile Valley University Department of Computer Science Sudan South China University of Technology SCUT School of Software Engineering China University of Bisha Department of Information System Bisha Community College Saudi Arabia

ISBN: (纸本)9781728110066

Software institutions recently implement more than one software process model in order to achieve a high value in their products and services. The challenges of implementing several quality models are increasing the risk of Multi-Model Environment (MME) failure and more resources are needed to support the MME. This kind of resources is not available in the Small and Medium Enterprises (SME), especially in Sudanese institutions. This study aims to provide a solution by encouraging the Sudanese SMEs to apply software models in their software development processes. We are seeking to make these institutions understand how to integrate product quality models with software process improvement models. To achieve this goal, we used a systematic harmonization technique to harmonize and map ISO/IEC 25010 standard onto Capability Maturity Model Integrity (CMMI) Dev 1.3 level 2 and 3. Also, we reviewed the related works of the previous studies on MMEs to achieve better results. The results show some of the processes and activities of CMMI Dev 1.3 considering software quality attributes on the early stage of the Software Development Lifecycle (SDLC). On the other hand, we presented how to apply the mapped models and suggest a tool for implementation. This kind of approach gives small organizations the ability to apply multi SPI models. © 2019 IEEE.

关键词： Software design

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Pairwise Cluster Similarity Domain Adaptation for Multimodal Deep Learning Architecture

Pairwise Cluster Similarity Domain Adaptation for Multimodal...

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2019第二届信息科学与系统国际会议

作者： Rizal Setya Perdana Yoshiteru Ishida Department of Computer Science and Engineering Toyohashi University of Technology Department of Information System Faculty of Computer Science Brawijaya University

In this work, we investigate the drawback of direct application from a dataset to the new domain with a different probability distribution, known as domain shifting. The two different probability distribution of datasets is generally known as source domain and target domain. Regularly, when we use large datasets from the open repository(source domain) for the new problem(target domain), we need to bridge for adapting these two domains. Notably, this research focuses on learning setup for the multimodal task which is commonly known as domain adaptation. To minimize the difference between the source and target domain, we employ cluster analysis with a modification of the loss function of a deep neural network. The modification of the loss function is done by combining the cross-entropy with covariance between source and target domains. We propose a method named Pairwise Cluster Similarity Domain Adaptation(PCSDA) that orders the distance among clusters from the source and target domains. Comprehensive experimental results of image captioning task(object detection on image and text generation) demonstrate the robustness and effectiveness of the proposed method.

关键词： Deep Neural Network Transfer Learning Multimodal Machine Learning Domain Adaptation Image Captioning

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Power system Fault Detection Automation Based on Fuzzy Logic

Power System Fault Detection Automation Based on Fuzzy Logic

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International Conference on Smart systems and Technologies (SST)

作者： Ivica Petrović Srete Nikolovski Hrvoje Glavaš Filip Relić Croatian Transmission System Operator Ltd. Osijek Croatia Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia HEP Distribution System Operator Ltd. Sisak Croatia

ISBN: (数字)9781728197593

ISBN: (纸本)9781728197609

Fuzzy logic provides an appropriate way to incorporate an individual's knowledge into expert systems using descriptive language expressions. A large number of commercially successful applications in power energy systems show that fuzzy logic can be used very successfully, especially during disturbances. During a disturbance, automation of system fault analysis and information processing becomes desirable and necessary, due to the large increase in data collected from the system. The paper analyzes the possibilities for implementing automated fault analysis systems based on digital fault recorders, digital protection relays, sequence of events recorders, intelligent alarm processors and lightning location systems. These systems are interconnected with Intelligent Electronic Devices, supervisory control and data acquisition systems, remote terminal units or protection devices which are combined with different intelligent electronic devices in the system. For large number of devices and data, fuzzy systems have proven to be effective in detecting and classifying power system failures.

关键词： Circuit faults Circuit breakers Monitoring Substations Databases Relays Power systems

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Survey into predictive key performance indicator analysis from data mining perspective

Survey into predictive key performance indicator analysis fr...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Akshay Thakur Robert Beck Sanaz Mostaghim Daniel Großmann Productivity Controlling & Production system Volkswagen AG Wolfsburg Germany Productivity Controlling Volkswagen AG Wolfsburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Faculty of Engineering & Management Technische Hochschule Ingolstadt Ingolstadt Germany

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

Predictive analytics is seen as one of the emerging technology in this digital age of big data. Computational processing power and speed has grown exponentially in the last few years that has made predictive analytic practical for application in different organization. Manufacturing industries has huge amount of data in different shapes and forms, and keep regular track of their performance by monitoring key performance indicators defined under business strategy. Prioritizing and predicting these key performance indicators provide organization cutting edge as compared to competitors by being proactive rather than reactive. As compared to traditional business intelligence tools where focus is on static report or dashboards about past data, predictive analysis focuses on estimating outcomes with the objective of driving better business performance. Moreover, it is also being adopted for decision-making tools. Different data mining techniques are applied in the field of performance management system as per individual or project need. Many researches has developed different ideas to understand and evaluate complex intervened key performance indicator relationships in performance measurement system. The aim of the paper is to present comprehensive version of predictive key performance indicator analysis from its background to state of the art, describing various data mining standards, methodologies as well as industrial and research application. The paper also studies various surveys regarding predictive analytic for business application to identify different best practices in this field.

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Correction: Highly Accurate Gear Fault Diagnosis Based on Support Vector Machine

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Journal of Vibration engineering & Technologies 2022年第3期11卷 1389-1389页

作者： Abdul, Zrar Kh. Al-Talabani, Abdulbasit K. Department of Information System Engineering Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq Department of Applied Computer Science College of Medicals and Applied Sciences Charmo University Chamchamal Sulaymaniyah Iraq Department of Software Engineering Faculty of Engineering Koya University Kurdistan Region Iraq

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Age and Gender prediction in Open Domain Text

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Procedia computer science 2020年 170卷 563-570页

作者： Emad E. Abdallah Jamil R. Alzghoul Muath Alzghool Department of Computer Information System Faculty of Information Technology Hashemite University Zarqa Jordan University College Of Surveying And Geospatial Sciences Regional Center For Space Science And Technology Education for Westrern Asia Amman Jordan Department of Electrical Engineering and Computer Science York University 4700 Keele Street Toronto Ontario M3J 1P3 Canada

The massive use of the social media and the huge number of messages that are shared on the internet, create a countless need to automatically detect the age and gender of the people who write these messages. Several sites and platforms attempt to mislead and cheat the people who are visiting them by providing deceptive information about the age and the gender of their customer. The traditional way to detect deceivers was by human judgment, but this way is no longer suitable since lots of interviews are not conducted face to face. This paper presents an automate tool with a unique set of features that used to analyze a given text. The features include the unigram, part of speech, and production rules. The accuracy results of the proposed method outperform the existing techniques. The best results achieved by using the production rules features.

关键词： Open domain text Deceptive information Age prediction Gender prediction

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Implementation of Dynamic Mutual Information and Support Vector Machine for Customer Loyalty Classification 2

Implementation of Dynamic Mutual Information and Support Vec...

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2nd International Conference on Applied sciences Mathematics and Informatics, ICASMI 2018

作者： Sulistiani, H. Muludi, K. Syarif, A. Departement of Computer Science Faculty of Mathematics and Sciences Lampung University Jl. Sumantri Brojonegoro No.1 Bandar Lampung35145 Indonesia Departement of Accounting Information System Faculty of Computer Science and Engineering Teknokrat Indonesia University Jl. ZA. Pagar Alam No. 9-11 Bandar Lampung35145 Indonesia

Fast Moving Consumer Goods (FMCG) is known one of the important industrial sectors worldwide. It includes household and personal care products as well as processed foods and beverages. Because of the tight competition company must develop good marketing strategies. So, it is important for the company to know customer loyalty and also to predict the income as reference in company development planning. Data mining now is becoming popular technique for predicting customer loyalty. One of the well known data mining strategies is retaining customer's strategy. In this paper, we would present a new model for predicting customer loyalty. The model is based on Dynamic Mutual Information and Support Vector Machine (DMI-SVM) to identify the relevant factors that affect the performance of the classification of customer loyalty. The comparison of two classification methods and several selected features is given to show the effectiveness of the methods. We validated the model by 10-fold cross validation method. Classification accuracy, precision, recall and f-measure are used to evaluate classifier performance on a test/hold-out sample. A result in this paper is shown that SVM method gives better performance accuracy than Naïve Bayes. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

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Efficient Algorithms for E-Healthcare to Solve Multiobject Fuse Detection Problem

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Journal of Healthcare engineering 2021年 2021卷

作者： Ahmad, Ijaz Ullah, Inam Khan, Wali Ullah Ur Rehman, Ateeq Adrees, Mohmmed S. Saleem, Muhammad Qaiser Cheikhrouhou, Omar Hamam, Habib Shafiq, Muhammad School of Pattern Recognition and Intelligent System Shenzhen Institute of Advance Technology Chinese Academy of Science Shenzhen China Changzhou Campus Changzhou213022 China School of Information Science and Engineering Shandong University Qingdao266071 China Department of Electrical Engineering Government College University Lahore54000 Pakistan College of Computer Science and Information Technology Al Baha University Al Baha Saudi Arabia College of CIT Taif University P.O. Box 11099 Taif21944 Saudi Arabia Faculty of Engineering Université de Moncton MonctonNBE1A3E9 Canada Sfax Tunisia Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Object detection plays a vital role in the fields of computer vision, machine learning, and artificial intelligence applications (such as FUSE-AI (E-healthcare MRI scan), face detection, people counting, and vehicle detection) to identify good and defective food products. In the field of artificial intelligence, target detection has been at its peak, but when it comes to detecting multiple targets in a single image or video file, there are indeed challenges. This article focuses on the improved K-nearest neighbor (MK-NN) algorithm for electronic medical care to realize intelligent medical services and applications. We introduced modifications to improve the efficiency of MK-NN, and a comparative analysis was performed to determine the best fuse target detection algorithm based on robustness, accuracy, and computational time. The comparative analysis is performed using four algorithms, namely, MK-NN, traditional K-NN, convolutional neural network, and backpropagation. Experimental results show that the improved K-NN algorithm is the best model in terms of robustness, accuracy, and computational time. © 2021 Ijaz Ahmad et al.

关键词： Object detection

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The Localized Subtraction Approach For EEG and MEG Forward Modeling

arXiv

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arXiv 2023年

作者： Höltershinken, Malte B. Lange, Pia Erdbrügger, Tim Buschermöhle, Yvonne Wallois, Fabrice Buyx, Alena Pursiainen, Sampsa Vorwerk, Johannes Engwer, Christian Wolters, Carsten H. Institute for Biomagnetism and Biosignalanalysis University of Münster Münster Germany Institute of Medical Informatics University of Münster Münster Germany Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience University of Münster Münster Germany INSERM U1105 Research Group on Multimodal Analysis of Brain Function Jules Verne University of Picardie Amiens France Pediatric functional exploration of the nervous system department CHU Picardie Amiens France Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Computing Sciences Unit Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Electrical and Biomedical Engineering Private University for Health Sciences Medical Informatics and Technology Hall in Tyrol Austria Faculty of Mathematics and Computer Science University of Münster Münster Germany

In FEM-based EEG and MEG source analysis, the subtraction approach has been proposed to simulate sensor measurements generated by neural activity. While this approach possesses a rigorous foundation and produces accurate results, its major downside is that it is computationally prohibitively expensive in practical applications. To overcome this, we developed a new approach, called the localized subtraction approach. This approach is designed to preserve the mathematical foundation of the subtraction approach, while also leading to sparse right-hand sides in the FEM formulation, making it efficiently computable. We achieve this by introducing a cut-off into the subtraction, restricting its influence to the immediate neighborhood of the source. In this work, this approach will be presented, analyzed, and compared to other state-of-the-art FEM right-hand side approaches. Furthermore, we discuss how to arrive at an efficient and stable implementation. We perform validation in multi-layer sphere models where analytical solutions exist. There, we demonstrate that the localized subtraction approach is vastly more efficient than the subtraction approach. Moreover, we find that for the EEG forward problem, the localized subtraction approach is less dependent on the global structure of the FEM mesh, while the subtraction approach is sensitive to the mesh structure even at a distance to the source. Additionally, we show the localized subtraction approach to rival, and in many cases even surpass, the other investigated approaches in terms of accuracy. Especially for the MEG forward problem, where Biot-Savart’s law is employed, we show the localized subtraction approach and the subtraction approach to produce highly accurate approximations of the volume currents close to the source. The localized subtraction approach thus reduces the computational cost of the subtraction approach to an extent that makes it usable in practical applications without sacrificing the rigorousness and

关键词： Mesh generation

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Battery Energy Storage systems and FACTS Devices Influence on Microgrid Voltage Stability

Battery Energy Storage Systems and FACTS Devices Influence o...

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International Conference on Smart systems and Technologies (SST)

作者： Ružica Kljajić Predrag Marić Filip Relić Hrvoje Glavaš Faculty of Electrical Engineering Computer Science and Information Technology Osijek J. J. Strossmayer University of Osijek Osijek Croatia HEP Distribution System Operator Ltd. Elektra Sisak Sisak Croatia

ISBN: (数字)9781728197593

ISBN: (纸本)9781728197609

Distributed generation (DG) has significant influence on modern power system performance. Most of DG is integrated with battery energy storage systems (BESS) as a part of microgrids (MG) which can operate either on-grid or in islanded mode and combined. This paper gives an example of application of BESS with Photovoltaic (PV) generation system and Flexible AC Transmission system (FACTS) devices in microgrid represented by modified IEEE 37-bus distribution test system. PV generation and BESS have been implemented on the busbar with the lowest voltage value in the system to examine their influence on the voltage stability and profile. Further enhancements have been made implementing FACTS devices.

关键词： Power system stability Stability criteria Microgrids Batteries Reactive power Supercapacitors Axles

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