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The intention of using mobile ticketing in online travel agent

Procedia computer science 2022年 197卷 273-281页

作者： Andre Parvian Aristio Muhammad Al Farisi Iskandar Mudjahidin Arrindika Pradana Ramadhansyah S. Supardi Gatut Prijo Utomo Anggraini Naya Parahita Information System Department Institut Teknologi Sepuluh Nopember (ITS) Surabaya 60111 Indonesia Department of Mechanical Engineering University of 17 Agustus 1945 Semolowaru 45 Surabaya 60118 Indonesia Magister of Information Technology Faculty of Computer Science Universitas Indonesia (UI) Jakarta 16424 Indonesia

The mobile ticketing service application is a promising technology for an online travel agent in Indonesia. However, its adoption is low and not evenly distributed, this is due to the lack of understanding regarding the use of mobile ticketing. The purpose of this study aims to identify and analyses the structural relationship of the factors influencing user intentions in mobile ticketing, previous research was conducted in Malaysia with different user characteristics and adoption acceleration, in this study adjustments will be made to different characteristics and conditions in Indonesia. This study conducted using the Structural Equation Modelling (SEM) Method through the questionnaires’ distributions to 531 respondents using mobile ticketing services in an online travel agent, Indonesia. The study findings suggest that capability and use context that mediated mobility and perceived use significantly affect use intention. This study provides evaluation and recommendations for online travel agent services to increase user intentions and retain mobile ticketing services users.

关键词： Use intention mobile ticketing structural equation modeling online travel agent

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Research on Compression Optimization Algorithm for Super-resolution Reconstruction Network

Research on Compression Optimization Algorithm for Super-res...

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Electrical engineering and Automation (IFEEA), International Forum on

作者： Xiaodong Zhao Yanfang Fu Feng Tian Xunying Zhang Unmanned System Research Institute Northwestern Polytechnical University Xi’an China School of Computer Science and Engineering Xi’an Technological University Xi’an China Faculty of Science & Technology Bournemouth University UK School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (纸本)9781665464222

Under the condition of limited resources of embedded systems, the paper proposes a compression optimization algorithm based on pruning and quantization, so that the computational requirements of the super-resolution reconstruction algorithm based on a Convolutional Neural Network (CNN) can be met. First, a multiple regularization pruning optimization algorithm based on an attention module and a BatchNorm layer is proposed. Then, a coordination optimization algorithm of INT8 training and quantization for FPGA architecture is proposed. The performance of the pruning optimization algorithm was verified for the Super-Resolution CNN (SRCNN), the Fast Super-Resolution CNN (FSRCNN), and the Very Deep Super-resolution CNN (VDSRCNN). As for SRCNN, the performance of the quantization optimization algorithm was verified on the FPGA EC2 hardware simulation platform. The results show that the proposed compression optimization algorithm can achieve a good balance between network accuracy and inference speed.

关键词： Training Quantization (signal) Embedded systems Superresolution Reconstruction algorithms Inference algorithms Hardware

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Rice crop management expert system with forwarding chaining method and certainty factor 9

Rice crop management expert system with forwarding chaining ...

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9th International Seminar on New Paradigm and Innovation of Natural sciences and Its Application, ISNPINSA 2019

作者： Kharisma Adi, K. Isnanto, R.R. Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

Rice plants are one of the focuses on food sustainability so that an increase in rice production needs to be done. Farmers need information and knowledge to improve rice crop production by practicing good planting methods that begin with the selection of superior varieties of seeds and handling plant pests properly. The problem that arises is that assistance from agricultural specialists is not always available when farmers need it, making it difficult for farmers to consult experts according to their needs at any time. Therefore, in this study, an expert system was proposed which was able to help farmers to obtain information according to their needs. This research builds an expert system for the management of rice plants including the selection of rice seed varieties and handling pests of rice plants. The method used in this expert system is forward chaining and certainty factor. Knowledgebase data was obtained from experts in agricultural extension, experts on plant pest organisms and literature from the agricultural service. From this study, an expert system was produced that was able to provide information on the selection of seed varieties and pest of rice plants according to the conditions and needs of the users. © Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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The performance of extended scratch-build concept mapping tool in blended learning 4

The performance of extended scratch-build concept mapping to...

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4th International Conference on Vocational Education and Training, ICOVET 2020

作者： Prasetya, Didik Dwi Pinandito, Aryo Hayashi, Yusuke Hirashima, Tsukasa Universitas Negeri Malang Faculty of Engineering Dept. of Electrical Engineering Malang Indonesia Information System Department Computer Science Faculty Universitas Brawijaya Malang Indonesia Hiroshima University School of Engineering Dept. of Information Engineering Higashihiroshima Japan

ISBN: (纸本)9781728181318

computer-based concept mapping tools have been widely used in several learning strategies, one of which is blended learning. Blended learning synthesizes face-To-face and online interaction in offering a synchronous and asynchronous activity. However, little effort has been made to utilize extended concept maps in blended learning. Extended concept mapping is a technique to connect existing concept maps with a new knowledge structure. Extending activities allow learners to review their prior knowledge, elicit missing ideas and relationships, and improve new experiences. This study sought to investigate the performance of the Extended Scratch-Build (ESB) concept mapping tool in a blended learning environment. A total of 54 university students participated and were divided into two groups, experimental and control. The students in the experimental group used the ESB technique, and those in the control group used the whole-class discussion and summarization method. The results reported that the experimental group outperformed the control group in terms of immediate-Test scores attainment. © 2020 IEEE.

关键词： computer aided instruction

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Enhancing Chewing Detection with a CNN-BiLSTM Network Utilizing IMU and Photoplethysmography Data from an Earable Device

Enhancing Chewing Detection with a CNN-BiLSTM Network Utiliz...

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IEEE International Conference on Software engineering and Service sciences (ICSESS)

作者： Sakorn Mekruksavanich Wikanda Phaphan Phaisarn Jeefoo Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Geographic Information System School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Detecting chewing is vital to tracking eating habits, which is necessary to preserve good well-being and health. Advancements in wearable sensors have recently enabled cap-turing a wide range of sensor data, including signals from inertial measurement units (IMUs) and photoplethysmography (PPG). This research presents a new deep-learning design that efficiently detects chewing occurrences employing IMU and PPG sensor data acquired from an early instrument. The design includes convolutional neural networks (CNN) and bidirectional long short-term memory (LSTM). The CNN-BiLSTM network presented in this study combines the advantages of both CNN and BiLSTM to capture spatial and temporal characteristics from the sensor data effectively. The CNN layers extract localized patterns and characteristics from the IMU and PPG data, whereas the BiLSTM layers represent the extended relationships and time-based changes in chewing behavior. By integrating these two formidable designs, our network attains a high level of resilience and precision in detecting chewing. We assess the efficacy of the CNN-BiLSTM network using a standardized dataset obtained from several individuals wearing wearable devices while engaging in eating events. The dataset comprises IMU and PPG sensor data that have been annotated with associated chewing sessions. The experimental findings clearly show that the suggested network outperforms the current leading approaches, obtaining an accu-racy of 98.18 % and an F1-score of 98.29 %, the highest recorded. The CNN-BiLSTM network performs better than baseline models such as CNN, LSTM, BiLSTM, GRU, and BiGRU, emphasizing the efficacy of integrating spatial and temporal modeling for chewing identification.

关键词： Performance evaluation Recurrent neural networks Measurement units Photoplethysmography Convolutional neural networks Wearable devices Long short term memory Wearable sensors Software engineering Resilience

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Are Halal Food Supply Chains Sustainable: A Review And Bibliometric Analysis

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Journal of Foodservice Business Research 2021年第5期24卷 554-595页

作者： Rejeb, Abderahman Rejeb, Karim Zailani, Suhaiza Department of Logistics and Forwarding Doctoral School of Regional Sciences and Business Administration Széchenyi István University Györ Hungary Department of Software Engineering Higher Institute of Computer Science El Manar Ariana Tunisia Department of Operations Management and Information System Faculty of Business and Accountancy University Malaya Kuala Lumpur Malaysia

Halal food supply chains (HFSCs) are increasingly characterized by fragmentation, complexity, and uncertainty. Halal food consumers are becoming more demanding for food quality and compliance with Islamic dietary law. As sustainability is a multidimensional concept that entails several aspects and interpretations, the objectives of HFSCs significantly overlap with the sustainable dimensions of the food ecosystem. The purpose of this study is to examine the inherent sustainable aspects and characteristics of HFSCs. Therefore, a systematic literature review and a bibliometric analysis were conducted to redress the lack of academic attention being given to the relationships of HFSCs with sustainability. Selected 74 papers were coded and analyzed according to the dimensions of sustainability, namely, the economic, social, and environmental dimensions. The review primarily attempts to provide answers to the following research question: What are the sustainable aspects of HFSCs? We provide a comprehensive analysis of the three critical components of sustainability, offering a broad and comprehensive picture of many critical issues in HFSCs. Finally, the contribution of the study, the scope of future research, and the limitations are highlighted in detail. © 2021 Taylor & Francis.

关键词： economic Halal Food Supply Chains social sustainability

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Big Data Analytics for Processing Real-time Unstructured Data from CCTV in Traffic Management

Big Data Analytics for Processing Real-time Unstructured Dat...

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2020 International Conference on Data science and Its Applications, ICoDSA 2020

作者： Hamami, Faqih Dahlan, Iqbal Ahmad Prakosa, Setya Widyawan Somantri, Khamal Fauzan Telkom University School of Industrial and System Engineering Bandung Indonesia Indonesia Defense University Military Engineering Faculty Bogor Indonesia National Taiwan University of Science Technology Electronic and Computer Engineering Taipei Taiwan Universitas Pendidikan Indonesia Faculty of Mathematics and Science Bandung Indonesia

ISBN: (纸本)9781728182353

Todays many devices generate data everywhere and anytime. Data grow massively and becomes complex thing that needs to be handled. Unstructured data is one type of big data that is difficult to process and consists of unstable attributes. In traffic management, CCTVs are installed to monitor the specific location in the highway. CCTV generates unstructured data in image and video format. These data are difficult to process due to the complexity of the data. This research proposes to implement big data analytics to process real-time unstructured data from CCTV into knowledge displayed in web dashboard. We implement the YOLO framework with YoloV4 Architecture and COCO dataset for traffic flow counting and detecting illegal parking which is categorized as abnormal situation. Unstructured data from CCTV then transformed into semi-structured format in JSON. Data also can be visualized in real time to facilitate local authority to understand the highway situation. Historical data are stored in the NoSQL database to deep more knowledge such as vehicle traffic pattern. The proposed system requires the ROI drawing line as trigger to count the passing vehicles. These experiments are conducted from open CCTV for traffic online in Bali Tower Public Streaming. The prototype result is able to detect the object with 10 fps. © 2020 IEEE.

关键词： Big data

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators

Studying of Machine Learning Models for Forecasting Macroeco...

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International Conference on Advanced Communication Technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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Intrusion Detection using Deep Neural Network Algorithm on the Internet of Things

Intrusion Detection using Deep Neural Network Algorithm on t...

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IEEE International Conference on Communication, Networks and Satellite (ComNetSat)

作者： Syariful Ikhwan Adi Wibowo Budi Warsito Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia D3 Telecommunication Engineering Institut Teknologi Telkom Purwokerto Banyumas Indonesia Computer Science Informatics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia Statistics Department of Science and Mathematics Faculty Diponegoro University Semarang Indonesia

ISBN: (数字)9781665460309

ISBN: (纸本)9781665460316

The increasing use of IoT devices on future networks is very helpful for humans in their lives. However, the increase in devices connected to IoT networks also increases the potential for attacks against those networks. Vulnerabilities in Internet of Things (IoT) networks can be exposed at any time. Artificial intelligence can be used to protect the IoT network by being able to detect attacks on the network so that they can be prevented. In this study, network detection was carried out using the Deep Neural Network (DNN) algorithm. The test was carried out using the UNSW Bot-IoT dataset with a comparison of training data of 75% of the overall data. The results obtained show the ability of the algorithm to detect attacks on average with 99.999% accuracy. The validation loss and training loss look very small. In this study, there is a validation loss that still occurs in overfitting, but the difference is very small.

关键词： Deep learning Training Satellites Neural networks Intrusion detection Training data Feature extraction

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Classification of chicken egg quality using faster region convolutional neural network

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AIP Conference Proceedings 2024年第1期2987卷

作者： Amira Nurul Amanda Ivan Jaya Fanindia Purnamasari Research Laboratory in Data Science Intelligent System and Software Engineering Faculty of Computer and Information Technology Universitas Sumatera Utara Medan Indonesia

Chicken eggs are one of animal product which have high nutrition with cheaper price compared to another animal product. As one of the most consumed food, the quality of chicken eggs need to be more considered. According to SNI 3926:2008 and Egg Grading Manual Booklet, the quality of chicken egg can be seen from inside and also external characteristic of egg (egg shell). The internal characteristic are the yolk, egg whites, blood stains. The external characteristic are color, texture, cleanliness, thickness of the shell. This study builds a system which can detect the quality of chicken eggs by seeing the texture and the color of the shell. The quality of that chicken eggs grouped into three levels which is Grade A, Grade B, and Grade C. The amount of data that used in this study are 60 data that divided into 480 data for training and 120 data for testing. Data were doing pre-processing before classified with Faster Region Convolutional Neural Network method with ResNet-50 as pre-trained model. Based on the result of training system, this system can classify the quality of chicken eggs with an accuracy rate of 94%. The performance of the system in classifying egg quality is very sufficient.

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