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International Conference of computer and informatics engineering (IC2IE)

作者： Bassamtiano Renaufalgi Irnawan Rizky Adi Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

Neural Machine Translation is already used for Indonesian informal to formal style transfer. It works by translating the input language to the target language. In Indonesian informal-to-formal style transfer task, informal sentence work as an input language, and formal sentence is the target the model needs to translate to. Currently, the STIF parallel dataset is the only manually labelled informal-to-formal dataset. We need sufficient data to achieve a good model for style transfer performance. In contrast, the current Indonesian informal to formal dataset is insufficient. We adopted the pre-train augmentation architecture introduced by work done in GEC tasks to elevate the Low-Resource data. We create the augmented dataset with a simpler word replacement approach. We benchmark several transformer-based pre-trained model architectures, including BART, GPT2, and BERT Encoder Decoder. We train the augmented dataset to all models as a pre-trained model and fine-tune it with the STIF dataset. We perform the sacreBLEU benchmarking techniques to find which approach with better style transfer quality. The result is the BART model that was pre-trained with an augmented dataset and fine-tuned with the STIF dataset with a score of sacreBLEU 53,19.

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Intelligent Live Video Streaming for Object Detection 23

Intelligent Live Video Streaming for Object Detection

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Mingkang Sun, Jingtao Aida, Kento Figueiredo, Renato J. Ku, Yun-Jung Subratie, Kensworth The Graduate University for Advanced Studies Sokendai Department of Informatics Tokyo Japan National Institute of Informatics Tokyo Japan University of Florida Electrical and Computer Engineering Florida United States

ISBN: (纸本)9781665494571

These days, sensors and cameras are being deployed on an increasingly large scale. Furthermore, the rapid development of machine learning models for computer vision now presents novel opportunities for the use of artificial intelligence (AI) and Internet of Things (IoT) combinations in various application scenarios. However, challenges remain in supporting low-latency video streaming from distributed mobile IoT devices under dynamic network environments, and overcoming video data quality degradation that results from weather 'noise', which reduces the accuracy of AI-based data analyses such as object detection. In this paper, we propose a live video stream processing system for supporting intelligent services that integrates the following features. First, to cope with dynamic networks and achieve low latency, our approach employs a peer-to-peer (P2P)-based virtual network at the edge and a multi-tiered architecture composed of IoT cameras, edge, and cloud servers. Second, we construct a flexible messaging system for video analysis built upon SINETStream, which is a messaging system that adopts a topic-based pub/sub model. Third, we implement a framework that can remove weather-related (rain, snow, and fog) noise by applying weather classification and adaptive noise removal models that improve the accuracy of video analysis from data collected outdoors. The latency, throughput, and image quality benchmark experiments conducted to validate the feasibility of our proposed system showed that the process resulted in image quality improvements of approximately 30% (on average). © 2021 IEEE.

关键词： Image quality Adaptation models Image edge detection Object detection Streaming media Throughput Internet of Things

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A hybrid deep learning-based approach for rolling bearing fault prognostics

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IFAC-PapersOnLine 2023年第2期56卷 6588-6593页

作者： Domício Neto Lorena Petrella Jorge Henriques Paulo Gil Alberto Cardoso University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems of the University of Coimbra (CISUC) Coimbra Portugal CTS-UNINOVA Department of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal

Predictive Maintenance (PdM) has the potential to revolutionize the industry by providing advanced techniques to assess the condition of an industrial system and yield key information that can help optimize maintenance planning and prevent unexpected faults and breakdowns. Nevertheless, PdM is far from being universally applied and it is still the subject of increasing research. Thus, developing new approaches has great relevance to help PdM become a practical reality for the industry. PdM can also bring benefits in terms of sustainability, by reducing human and material resources waste, which is one of the main objectives of Circular Manufacturing initiatives. In this context, rolling bearings are one of the most studied components, as most industrial systems with rotating mechanisms contain bearings, which are prone to a number of faults caused by natural and unnatural wear. In this work, an hybrid Deep Learning (DL) approach is proposed, combining a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) network to predict Remaining Useful Life (RUL) using rolling bearing vibration data preprocessed with the Short-Time Fourier Transform (STFT). This model was trained and validated using the PRONOSTIA public dataset, which is a popular benchmark for rolling bearing prognostics. The obtained results are satisfactory, providing RUL estimates close to the true values in most test cases, proving the competitiveness of the approach and its potential.

关键词： Predictive Maintenance Circular Manufacturing Remaining Useful Life Rolling Bearings Deep Learning

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Online Vehicle Routing with Pickups and Deliveries under Time-Dependent Travel-Time Constraints

arXiv

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arXiv 2024年

作者： Kontogiannis, Spyros Paraskevopoulos, Andreas Zaroliagis, Christos Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press "Diophantus" Greece Computer Technology Institute and Press "Diophantus" Greece

The Vehicle Routing Problem with pickups, deliveries and spatiotemporal service constraints (VRPPDSTC) is a quite challenging algorithmic problem that can be dealt with in either an offline or an online fashion. In this work, we focus on a generalization, called VRPPDSTCtd, in which the travel-time metric is time-dependent: the traversal-time per road segment (represented as a directed arc) is determined by some function of the departure-time from its tail towards its head. Time-dependence makes things much more complicated, even for the simpler problem of computing earliest-arrival-time paths which is a crucial subroutine to be solved (numerous times) by VRPPDSTCtd schedulers. We propose two online schedulers of requests to workers, one which is a time-dependent variant of the classical Plain-Insertion heuristic, and an extension of it trying to digest some sort of forecasts for future demands for service. We enrich these two online schedulers with two additional heuristics, one targeting for distance-balanced assignments of work loads to the workers and another that makes local-search-improvements to the produced solutions. We conduct a careful experimental evaluation of the proposed algorithms on a real-world instance, with or without these heuristics, and compare their quality with human-curated assignments provided by professional experts (human operators at actual pickup-and-delivery control centers), and also with feasible solutions constructed from a relaxed MILP formulation of VRPPDSTCtd, which is also introduced in this paper. Our findings are quite encouraging, demonstrating that the proposed algorithms produce solutions which (i) are significant improvements over the human-curated assignments, and (ii) have overall quality pretty close to that of the (extremely time-consuming) solutions provided by an exact solver for the MILP formulation. © 2024, CC BY.

关键词： Travel time

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Personalized Learner Assistance Through Dynamic Adaptation of Chatbot Using Fuzzy Logic Knowledge Modeling

Personalized Learner Assistance Through Dynamic Adaptation o...

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International Workshop on Semantic Media Adaptation and Personalization (SMAP)

作者： Christos Troussas Akrivi Krouska Phivos Mylonas Cleo Sgouropoulou Department of Informatics and Computer Engineering University of West Attica Egaleo Greece

Personalized approaches and tailored support have become increasingly significant in the field of online education, aiming to enhance the overall learning experiences of learners. This paper introduces a novel approach for addressing challenges in providing tailored support by utilizing chatbot technology and the flexibility of fuzzy logic. The chatbot is responsible for delivering precise and tailored responses to learners, considering their input, typically in text form. This is accomplished through the utilization of a rule-based system that is capable of generating accurate answers according to predefined criteria. To augment this support, fuzzy logic is employed for modeling the learners' knowledge, thereby enhancing the chatbot's proficiency in accurately evaluating and responding to inquiries. Consequently, the provision of assistance can be tailored to the specific knowledge level of learners, aiding them in achieving their educational goals. This methodology is incorporated in an intelligent tutoring system designed to provide tutoring for the programming language Java. The evaluation findings demonstrated the effectiveness of our approach in delivering personalized assistance through a chatbot. The results indicated that the chatbot's responses were highly rated in terms of clarity, relevance, and usefulness. Additionally, the system was found to effectively address learners' needs with quality and adequacy.

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Empirical mode decomposition of near-infrared spectroscopy signals for predicting oil content in palm fruits

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Information Processing in Agriculture 2023年第3期10卷 289-300页

作者： Inna Novianty Ringga Gilang Baskoro Muhammad Iqbal Nurulhaq Muhammad Achirul Nanda Computer Engineering Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Informatics Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Technology and Plantation Production Management Study Program College of Vocational StudiesIPB UniversityBogor 16680Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial TechnologyUniversitas PadjadjaranJatinangor 45363Indonesia

Oil content estimation in palm fruits is a precious property that significantly impacts oil palm production,starting from the upstream and *** content can be used to monitor the progress of the oil palm fresh fruit bunch(FFB)and be applied to identify product *** on the near-infrared(NIR)signals,this study proposes an empirical mode decomposition(EMD)technique to decompose signals and predict the oil content of palm ***,350 palm fruits with Tenera varieties(Elaeis guineensis ***),at various ages of maturity,were harvested from the Cikabayan Oil Palm Plantation(IPB University,Indonesia).Second,each sample was sent directly to the laboratory for NIR signal measurements and oil content ***,the EMD analysis and arti-ficial neural network(ANN)were employed to correlate the NIR signals and oil ***,a robust EMD-ANN model is generated by optimizing the lowest possible *** on performance evaluation,the proposed technique can predict oil content with a coefficient of determination(R2)of 0.933±0.015 and a root mean squared error(RMSE)of 1.446±*** results demonstrate that the model has a good predictive capacity and has the potential to predict the oil content of palm fruits directly,without neither solvents nor reagents,which makes it environmentally ***,the proposed technique has a promising potential to be applied in the oil palm *** like this will lead to the effective and efficient management of oil palm production.

关键词： Artificial neural network Empirical mode decomposition Oil palm Oil content prediction

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MOTION-BASED SIGN LANGUAGE VIDEO SUMMARIZATION USING CURVATURE AND TORSION

arXiv

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arXiv 2023年

作者： Sartinas, Evangelos G. Psarakis, Emmanouil Z. Kosmopoulos, Dimitrios I. Department of Computer Engineering and Informatics University of Patras Patras26504 Greece

An interesting problem in many video-based applications is the generation of short synopses by selecting the most informative frames, a procedure which is known as video summarization. For sign language videos the benefits of using the t-parameterized counterpart of the curvature of the 2-D signer’s wrist trajectory to identify keyframes, have been recently reported in the literature. In this paper we extend these ideas by modeling the 3-D hand motion that is extracted from each frame of the video. To this end we propose a new informative function based on the t-parameterized curvature and torsion of the 3-D trajectory. The method to characterize video frames as keyframes depends on whether the motion occurs in 2-D or 3-D space. Specifically, in the case of 3-D motion we look for the maxima of the harmonic mean of the curvature and torsion of the target’s trajectory;in the planar motion case we seek for the maxima of the trajectory’s curvature. The proposed 3-D feature is experimentally evaluated in applications of sign language videos on (1) objective measures using ground-truth keyframe annotations, (2) human-based evaluation of understanding, and (3) gloss classification and the results obtained are *** Codes 68T45, 68U10 © 2023, CC BY.

关键词： Torsional stress

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An Initial Acquisition Scheme with Reduced Complexity for Modified Walsh-Hadamard Code Division Multiplexing 15

An Initial Acquisition Scheme with Reduced Complexity for Mo...

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15th International Conference on Advanced Technologies for Communications, ATC 2022

作者： Kojima, Toshiharu Yoshinaka, Yuto Graduate School of Informatics and Engineering The University of Electro-Communications Department of Computer and Network Engineering 1-5-1 Chofugaoka Tokyo182-8585 Japan

ISBN: (数字)9781665451888

ISBN: (纸本)9781665451888

Modified Walsh-Hadamard code division multiplexing (MWHCDM) has an anti-blockage capability. It is thus suitable for helicopter satellite communications where the transmission channel is described as the periodic blockage channel. This paper addresses the computational complexity reduction of the initial acquisition for MWHCDM. The conventional initial acquisition scheme employs the sliding Walsh-Hadamard transform (WHT). A narrowing function based on the redundancy of MWHCDM signals is proposed to reduce the sliding WHT. computer simulation shows that the proposed narrowing function reduces the sliding WHT by about 70% without acquisition performance degradation. © 2022 IEEE.

关键词： Code division multiple access

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence Technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Sliding window based deep ensemble system for breast cancer classification

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Journal of Medical engineering and Technology 2021年第4期45卷 313-323页

作者： Alqudah, Amin Alqudah, Ali Mohammad Department of Computer Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid Jordan

Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer reduces the mortality rate, in the other hand, recognition of breast cancer is a complicated issue. Various studies and methods have been carried out to overcome this problem and to obtain accurate screening of breast cancer. One of the most recent methods with high performance is deep learning;it has been used to classify breast cancer using mammograms or histopathological images. This paper proposes a new using the concept of sliding window, and using the ensemble of four pre-trained convolutional neural networks (CNN) in order to classify breast cancer into eight classes. In this study, each image produces 4 non-overlapped sliding windows which are fed to GoogleNet, AlexNet, ResNet50, and DenseNet-201 CNNs, and an ensemble is then done to find the major class of each window, the ensemble is then applied again to find the class of the whole histopathological image. Breast Cancer Histopathological Database (BreakHis) database has been employed in this paper with eight classes (Adenosis, Ductal Carcinoma, Fibroadenoma, Lobular Carcinoma, Mucinous Carcinoma Papillary Carcinoma, Phyllodes Tumour, Tubular Adenoma). The proposed method is applied to four magnification cases: 40x, 100x, 200x, and 400x images. The proposed ensemble technique achieved an accuracy of 99.3325%. The results of the proposed system are comparable to recent studies results. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Developing countries

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