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3rd International Conference on sciences and technology Applications, ICoSTA 2021

作者： Kustiyahningsih, Y. Rahmanita, E. Purbandini Kholifah, N. Departement of Informatics Engineering University of Trunojoyo Madura Madura Bangkalan Indonesia Department of Mathematics Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia

Indonesia is an archipelagic country with a coastline of about 81,000 km and has a variety of very large biological and non-biological resources. Position as an archipelagic country with a very wide sea causes each region to have the potential to produce salt. The Covid-19 pandemic has resulted in various negative impacts on several fields such as the economy, social, SMEs and community services. Covid-19 has also resulted in a decrease in salt production and farmers also have difficulty marketing it. The research problem is the large number of salt farmers and number of indicators in providing assistance so that a model for measuring performance of salt farmers is needed. This performance measurement model is guided by several indicators, namely land area (K1), production result (K2), business capital (K3), and marketing system (K4). The method used Interval type-2 Fuzzy Analytic Hierarchy Process (IVFAHP). IVFAHP is used to determine indicators that most influence measurement of salt farmers. This study aims to build a model for measuring performance of salt farmers to increase economic productivity and ability of human resources to deal with COVID-19 pandemic. The contribution is a group-based decision by developing the fuzzy interval type-2 method with triangular fuzzy number (TFN) one midpoints. The findings from study are that the most influential indicators in dealing with COVID-19 pandemic are business capital and salt marketing. This research also produces recommendations for improvement salt farmers in an effort to increase salt production. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence

关键词： Marketing

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CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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WSports-50: An Image Dataset for Women’s Sport Action Classification 4th

WSports-50: An Image Dataset for Women’s Sport Action Class...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Ray, Palash Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Nasipuri, Mita Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India Department of Computer Science and Information Systems BITS Pilani Rajasthan Pilani India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9789819726134

Sport action recognition is an interesting area in computer vision. Categorization of sport actions, representing difficult and complex body postures, is regarded as a fine-grained visual classification problem. The Convolutional Neural Networks (CNNs) have attained enhanced performance over conventional feature descriptors in recognizing various sport activities. In general, though decent improvement has been gained using deep learning for sport action recognition, however, recognition of women’s sport activities is not widely explored. Even, no benchmark dataset depicting women’s sport action with sufficient variations is available yet for study. Hence, fine-grained image classification of diverse sport categories involving female/women athletics requires immediate research attention. To overcome this limitation, this paper proposes an image dataset comprising worldwide popular 50 sport categories of women players only. A simple deep learning model is proposed that extracts the high-level deep features using a backbone CNN. Then, these features are pooled from a collection of regular regions representing local discriminative information. The spatial pyramid pooling is applied for mining semantic information and enhancing feature aggregation for classification. The proposed method has achieved satisfactory performance on the Women Sports dataset using four standard backbone CNNs. Moreover, our method has achieved better accuracy on the Yoga-82 pose recognition dataset with a significant margin, e.g., 11.6% gain using ResNet-50 base CNN. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Two-color emissive AIEgens with anti-Kasha property for dual-organelle imaging and phototherapy

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science China Chemistry 2024年第5期67卷 1740-1752页

作者： Pu Chen Guogang Shan Qingli Nie Yuting Yan Pengfei Zhang Zujin Zhao Hai-Tao Feng Ben Zhong Tang AIE Research Center Shaanxi Key Laboratory of PhytochemistryCollege of Chemistry and Chemical EngineeringBaoji University of Arts and SciencesBaoji 721013China Institute of Functional Material Chemistry and National&Local United Engineering Lab for Power Battery Faculty of ChemistryNortheast Normal UniversityChangchun 130024China College of Chemistry&Materials Science Northwest UniversityXi’an 710127China Guangdong Key Laboratory of Nanomedicine CAS-HK Joint Lab of BiomaterialsCAS Key Laboratory of Biomedical Imaging Science and SystemShenzhen Engineering Laboratory of Nanomedicine and NanoformulationsCAS Key Lab for Health InformaticsShenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Luminescent Materials and Devices Key Laboratory of Luminescence from Molecular Aggregates of Guangdong ProvinceSouth China University of TechnologyGuangzhou 510640China School of Science and Engineering Shenzhen Institute of Aggregate Science and TechnologyThe Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518172China

Despite the rapid development of probes for targeting single organelle, the construction of robust dual-organelle targeting probes with multicolor emission was rarely reported. Herein, two dual-emissive aggregation-induced emission luminogens(AIEgens)with donor-π-acceptor structures were designed and synthesized, namely QT-1 and QF-2. Both two AIEgens exhibited excitation wavelength-dependence defying the Kasha's rule, and could stain lipid droplets(LDs) and mitochondria in blue and red fluorescence, respectively. Moreover, thanks to the near-infrared emission and abundant reactive oxygen species(ROS) generation efficiency of QT-1, it was chosen as a photodynamic therapy agent to selectively kill cancer cells from normal cells. Upon light irradiation, an obvious decrease of mitochondrial membrane potential(MMP) and serious change of mitochondrial shape in cells were observed, which corresponded to the efficient inhibition of tumor growth in vivo. This work afforded a promising strategy for the construction of multicolor emission by tuning anti-Kasha behaviors and expanding their application in dualorganelle targeting-based phototheranostics.

关键词： aggregation-induced emission anti-Kasha luminescence fluorescent probe bioimaging photodynamic therapy

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Development of A Chatbot for the Online Application Telegram Chat with An Approach to the Emotion Classification Text Using the Indobert-Lite Method 4

Development of A Chatbot for the Online Application Telegram...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Hulliyah, Khodijah Rayyan, Faishal Bakar, Normi Sham Awang Abu Syarif Hidayatullah State Islamic University Jakarta Faculty of Science and Technology Department of Informatics Engineering Jakarta Indonesia KICT International Computer Science Department Gombak Malaysia

ISBN: (纸本)9781665453950

The increasing preference for text-based communication on online chat applications has caused the number of social interactions to increase rapidly. However, text-based communication usually results in misunderstandings resulting from the absence of feeling intonation and emotions in the text. This study aims to create a chatbot that can detect emotions text to be entered into online chat applications. This study used a pre-trained model specifically trained from a collection of Indonesian-language datasets, namely IndoBERT-lite. The dataset used to train the model is a collection of Indonesian tweets totaling 4,403 which have been labeled with 5 classes of emotions, namely love, happy, anger, sadness, and fear. The hyperparameters used in this study to train the model were 5 epochs, batch size 16, learning rate 0.000003, and adam optimizer. Based on the test results with the parameters already mentioned, the accuracy, F1 score, recall, and precision values were obtained in the training set of 89%, 89%, 89%, and 90%, while the validation set obtained 70%, 71%, 70%, and 72%. © 2022 IEEE.

关键词： Social networking (online)

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Investigation of the separation zone for split-domain light field visualization 28

Investigation of the separation zone for split-domain light ...

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2024 28th International Conference on Information technology, IT 2024

作者： Javidi, Kamran Martini, Maria G. Kara, Peter A. School of Computer Science and Mathematics Faculty of Engineering Computing and the Environment Kingston University Wireless Multimedia and Networking Research Group Department of Computer Science Penrhyn Road Campus Kingston upon Thames LondonKT1 2EE United Kingdom Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary

ISBN: (纸本)9798350369618

Light field visualization commonly provides a single content over the entire field of view. However, the angularly-selective nature of the technology enables the simultaneous visualization of different contents at different viewing angles. Yet segmenting the valid viewing area comes with content interference as well in forms of separation zones, resulting invalid viewing domains. In this paper, we introduce a study on the separation zone of split-domain light field visualization. We created a static-content-based scenario in which we split the valid viewing area into two distinct domains, with a separation zone in between them. This was achieved by merging two light field contents in the middle with an instantaneous switch. The resulting visualization on a light field display was captured by a DSLR camera from two viewing distances, and it was compared to the crosstalk effect caused by insufficient angular resolution. © 2024 IEEE.

关键词： Visualization

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DevSecOps: A Security Model for Infrastructure as Code over the Cloud 2

DevSecOps: A Security Model for Infrastructure as Code over ...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Ibrahim, Amr Yousef, Ahmed H. Medhat, Walaa School of Information Technology and Computer Science Nile University Center of Informatics Science Nile University Cairo Egypt Ain Shams University Faculty of Engineering Cairo Egypt

ISBN: (纸本)9781665466776

DevSecOps includes security practice while applying DevOps. DevSecOps help secure the whole DevOps process. This paper aims to define a DevSecOps module to be used by the infrastructure team while applying infrastructure as code. The proposed module solves the problem of security by including security practice with the DevOps cycle to reach DevSecOps. The module was tested to measure time effi-ciency. A small survey was created to test other DevSecOps metrics and enhance future work. © 2022 IEEE.

关键词： Codes Computational modeling Ubiquitous computing Time measurement Security

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Heart Arrhythmia Classification Using Deep Learning: A Comparative Study 6

Heart Arrhythmia Classification Using Deep Learning: A Compa...

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6th Iraqi International Conference on engineering technology and its Applications, IICETA 2023

作者： Radi, Omar Alslatie, Mohammad Mustafa, Wan Azani Alquran, Hiam Badarneh, Alaa Mohammed, F.F. Alkhayyat, Ahmed Jordan University of Science and Technology Department of Biomedical Engineering Irbid Jordan The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman11855 Jordan Faculty of Electrical Engineering Technology Perlis Arau Malaysia Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Al-Zahraa University for Women Karbala Iraq The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9798350303339

Heart arrhythmia is an irregular heartbeat that causes heart problems. It can be classified by their seriousness into serious and non-serious arrhythmia. Mainly to diagnose heart arrhythmias, we use Electrocardiogram (ECG). In this paper, the authors compared three different models of classifiers: Convolutional Neural Network, Dense Neural Network and Long Short-Term Memory to classify cardiac arrhythmia into two types normal and abnormal, using the MIT-BIH database. The results show that CNN and DNN have the best result of the models with 99% accuracy while LSTM shows 60 accuracy percent. © 2023 IEEE.

关键词： Diseases

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How we can use text classification in the Back-Office environment of a bank as 'business as usual' solution 17

How we can use text classification in the Back-Office enviro...

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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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