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International Conference on information technology, information Systems and Electrical Engineering (ICITISEE)

作者： Abulwafa Muhammad Sarjon Defit Gunadi Widi Nur Cahyo Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia

Tourist destination reviews on Google Maps have become a valuable point of reference for visitors seeking enjoyable spots to visit. Additionally, users can gain insight into the reasons for writing reviews. Text classification is used to determine the motivation behind these reviews. The aim of this study is to compare machine learning (ML) text classification techniques for identifying the intent behind sentiments expressed as complaints (0), suggestions (1), opinions (2), statements(3), and awards (4). The ML techniques assessed are Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). To achieve this, tourist destination review data sets, comprising 738 reviews, were collected using web scraping from the Google Maps website. Preprocessing stages, including case-folding, tokenisation, filtering and stemming, are executed prior to the data being prepped for feature extraction with TF-IDF. The employed classification models perform well with KNN achieving 1.00 accuracy on a 90:10 data split, and SVM obtaining 0.90 on the same data split. Although, the Multinomial Naive Bayes algorithm with 0.70 accuracy on a 90:10 data split is classified as fair. Comparison of methods was conducted by means of a Receiver Operating Characteristic (ROC) diagram. The testing accuracy for the SVM method was found to be 95%, MNB at 91%, and KNN at 84%.

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Navigating Consumer Skepticism: Conceptual Model of Distrust Detection and Recognition technology Adoption Among Online Shop Owners in Indonesia

Navigating Consumer Skepticism: Conceptual Model of Distrust...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Muhamad Rachmat Gunawan Febriliyan Samopa Ahmad Mukhlason Doctoral Program in Technology Management School of Management and Technology Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia Department of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Surabaya Indonesia

ISBN: (数字)9798350365191

ISBN: (纸本)9798350365207

Background: Consumer skepticism affects trust between online shoppers and shop owners in digital commerce, necessitating effective distrust detection mechanisms. This study presents a conceptual model aimed at promoting the adoption of distrust detection and recognition technology among online shop owners, focusing on technologies powered by Large Language Models (LLMs). The model builds on Taylor's Decomposed Theory of Planned Behavior (DTPB) and incorporates factors relevant to online shop owners' perceptions of consumer distrust. Method: A quantitative research approach was used to survey 100 online shop owners to validate the model empirically. Results: The results indicate a significant effect of Perceived Usefulness (PU) and Social Influence (SI) on intention to adopt the technology. For small businesses such as online shops, PU (an internal factor) and SI (an external factor) can predict owners' intention to adopt distrust detection technology. Implications: This study highlights a simplified method for measuring small business owners' intention to adopt advanced information technologies and suggests business opportunities for e-commerce platforms to strategically position online shop owners.

关键词： Surveys Navigation Large language models Focusing Position measurement Behavioral sciences Electronic commerce information technology Business

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Vision Transformer Algorithm for Plant Disease Detection: A Systematic Literature Review

Vision Transformer Algorithm for Plant Disease Detection: A ...

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International Conference on Informatics and Computing (ICIC)

作者： Reza Al Husna Lintang Yuniar Banowosari Tubagus Maulana Kusuma Tri Handhika Doctoral program Information Technology Universitas Gunadarma Jakarta Indonesia Department of Informatics Program Universitas Gunadarma Jakarta Indonesia Department of Technology and Engineering Universitas Gunadarma Jakarta Indonesia Department of Computational Mathematics Studies Universitas Gunadarma Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Advances in deep learning have significantly impacted various sectors, including agriculture, by improving efficiency and sustainability. Computer vision-based plant disease detection that utilizes deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) has replaced manual methods that require specialized skills. Vision Transformers (ViTs) with self-attention mechanisms excel at recognizing complex patterns in images of diseased plants, enabling early detection and more effective management. This study conducted a systematic literature review to evaluate the use of feature fusion-based transformer algorithms in plant disease detection and to identify current research trends, challenges, and future opportunities. The results show that ViTs can improve accuracy and efficiency in plant disease diagnosis, particularly under diverse environmental conditions. However, the adoption of this technology faces obstacles such as the need for large annotated datasets, significant computational costs, and variability in field conditions that can affect model performance. This research highlights the need for further innovation to address these challenges and expand the accessibility and reliability of deep learning-based plant disease detection for the global agricultural sector.

关键词： Deep learning Plant diseases Computer vision Technological innovation Accuracy Computational modeling Transformers Feature extraction Computational efficiency Systematic literature review

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Implementation of Apriori Algorithm for Analysis of Consumer Purchase Patterns 1

Implementation of Apriori Algorithm for Analysis of Consumer...

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1st International Conference on Computer Science and Applied Mathematic, ICCSAM 2018

作者： Panjaitan, Suprianto Sulindawaty Amin, Muhammad Lindawati, Sri Watrianthos, Ronal Sihotang, Hengki Tamando Sinaga, Bosker Teknik Informatika STMIK Pelita Nusantara Medan Sumatera Utara Indonesia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Program Doctoral Vocational Technology Education Postgraduate Engineering Faculty Padang State University Padang West Sumatera Indonesia Manajemen Informatika AMIK Logika Sumatera Utara Indonesia Manajemen Informatika AMIK Labuhan Batu Sumatera Utara Indonesia

Consumer purchasing patterns are a form of purchases made by consumers, whether someone or a lot of people to get the desired item by making a purchase transaction. One characteristic of the purchase pattern is the existence of acquiring something through exchanging money. This study aims to create an application that is used in determining consumer purchasing patterns by applying a priori algorithms and using Visual Basic 2010 as a tool for determining consumer purchase patterns. This application uses a priori algorithm calculation method where the sample consumer purchase data will be sorted and calculated by providing the value of the minium support and configuration parameters and based on the results of confidence the largest number of conclusions such as: can be used as information for determining sales, the application of a priori algorithms can provide information pattern combination item set from consumer purchase data that is with support above 15% and confidence above 50% on item set. © Published under licence by IOP Publishing Ltd.

关键词： Condensed matter physics

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E-Translator Kawi to Bahasa 2

E-Translator Kawi to Bahasa

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2nd International Joint Conference on Advanced Engineering and technology, IJCAET 2017 and International Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2017

作者： Sudana, Oka Putra, Darma Sudarma, M. Hartati, Rukmi S. Pradnya Prastika, Ryan Wirdiani, Ayu Study Program of Doctoral Engineering Science Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Information Technology Departement Faculty of Engineering Udayana University Kabupaten Badung-Bali80361 Indonesia Electrical and Computer EngineeringDepartement Faculty of Engineering Udayana University Jalan Raya Bukit Jimbaran Kabupaten Badung-Bali80361 Indonesia

Kawi Language is a kind of language which has developed in Java during the Hindu-Buddhist era in the ancient Indonesian archipelago. As the time passed, Kawi began to come to extinction. To overcome this, preservation of Kawi language can be done by enhancing the understanding of culture through the use of technology. technology that can be used for cultural preservation is one of NLP based applications such as Kawi Language to Bahasa Translator Application which is developed in this research. The translator application is an application that translates from one language to another. The result of translation of the translator application is a language that is easy to understand by the people or commonly used language. This translator application is developed to processes Kawi Language text into bahasa based on search method which each word inserted. The stemming method used is Bobby Nazief & Mima Adriani Algorithm using Kawi Language rule. The translated Kawi Language text can be a word, sentence or paragraph with the result of the Text in Bahasa as well as words, sentences or paragraphs. © The Authors, published by EDP Sciences, 2018.

关键词： Translation (languages)

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Nonlinear internal wave run-up on impermeable steep slopes

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Journal of Offshore Mechanics and Arctic Engineering 2009年第4期131卷 1-5页

作者： Tseng, I-Fan Chen, Chen-Yuan Kuo, Hui-Ming Department of Marine Environment and Engineering National Sun Yat-Sen University Kaohsiung 80424 Taiwan Department of Computer Science National Pingtung University of Education No. 4-18 Ming Shen Rd. Pingtung 90003 Taiwan Department of Management Information System Yung-Ta Institute of Technology and Commerce Lin-Luoh Pingtung 90941 Taiwan Doctoral Program in Management National Kaohsiung First University of Science and Technology Kaohsiung Taiwan Department of Logistics Management Shu-Te University Yen Chau Kaohsiung 82445 Taiwan

Laboratory experiments were conducted to investigate the run-up of internal solitary waves (ISWs) on steep uniform slopes in a two-layered fluid system with a free surface. A 12 m long wave flume, which incorporated a movable vertical gate for generating ISWs, was used in the experiments. A steep uniform slope was modeled at one end of the flume. In the present study we looked at internal waves with small and large amplitudes using a steep uniform slope (from one of Θ=30, 50, 60, 90, 120, and 130 deg) much longer than those previously published in the literature. Results collected from a wide range of experimental runs show that the run-up height depends on the planar slope, while the breaking depth is only antecedent to the amplitude of an ISW. The overall quantitative agreement with the linear feature aspects of the incident wave amplitude is encouraging. Copyright © 2009 by ASME.

关键词： Arctic engineering

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Cross-camera complementary vehicle matching via feature expandsion for video forensics

Cross-camera complementary vehicle matching via feature expa...

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IEEE International Symposium on Consumer Electronics (ISCE)

作者： Chao-Yung Hsu Chih-Yang Lin Li-Wei Kang Hong-Yuan Mark Liao Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan Graduate School of Engineering Science and Technology-Doctoral Program and Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan

ISBN: (纸本)9781467361989

In this paper, we will investigate a more challenging vehicle matching problem. The problem is formulated as invariant image feature matching among opposite viewpoints of cameras, i.e. complementary object matching. For example, a front vehicle object may be given as a query to retrieve a rear vehicle object of the same vehicle. To solve the complementary object matching, invariant image feature is first extracted based on ASIFT (affine and scale-invariant feature transform) for each detected vehicle in a camera network. Then, the ASIFT feature is expanded via a special vehicle database. As a result, cross-camera vehicle matching with the help of complementary part can be greatly improved. Experimental results demonstrate the effectiveness of the proposed algorithm and the feasibility to video forensics applications.

关键词： Vehicles Cameras Databases Feature extraction Visualization Digital forensics

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Parallelizing Cipher Processes with Finite State Machine in MATLAB for Substitution and Transposition

Parallelizing Cipher Processes with Finite State Machine in ...

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IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing technology (AGERS)

作者： Fauziah Sunny Arief Sudiro Bayu Kumoro Yakti Ragiel Hadi Prayitno Sarifuddin Madenda Doctoral Program of Information Technology Gunadarma University Jakarta Indonesia Magister Teknologi Informasi STMIK Jakarta STI&K Jakarta Indonesia Industrial Technology Electrical Engineering Gunadarma University Jakarta Indonesia

ISBN: (数字)9798331543105

ISBN: (纸本)9798331543112

This article introduces a novel methodology for enhancing the efficiency of cipher processes by employing Finite State Machines (FSMs) within the MATLAB environment. The primary focus of this research is to improve the speed of algorithms used for both substitution and transposition ciphers, which are essential components of cryptographic practices. Recognizing the critical importance of speed in cryptographic operations, this research emphasizes the optimization of these algorithms as a key objective. Traditional encryption methods often struggle with performance limitations; thus, this paper proposes a solution by integrating parallelism into both the encryption and decryption stages through FSMs. This strategy capitalizes on the computational capabilities of modern multi-core processors, allowing for more efficient execution of cryptographic tasks. The research delineates two main phases of the proposed methodology. The first phase centers on transposition ciphers, while the second phase focuses on substitution ciphers. Data encryption is carried out via a meticulously defined FSM-based procedure that alters the input data by substituting and transposing values in accordance with a specified cryptographic key. The concurrent execution of both phases results in a substantial decrease in processing time. Empirical findings indicate that the processing duration is under 1 second for data sizes ranging from 8 to 129 bits, while encrypting and decrypting 1 billion bits of data takes approximately 162 seconds. The improvements in processing speed and the adaptability of these methods highlight their potential as effective solutions for secure data processing applications, which encompass secure communication and the advancement of sophisticated cryptographic protocols.

关键词： Ciphers Multicore processing Automata Parallel processing Software Hardware Encryption MATLAB Remote sensing Optimization

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Exploring Regression and Classification in Big Data with Deep Learning

Exploring Regression and Classification in Big Data with Dee...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Ravindra Pandey Aruna Sri Rongali S. Gopinath Nilesh Anute Forda Nama Sachin Pund Department - Management Hult International Business School Doctoral Information Technology/Information Technology Emphasis/Information Technology University of The Cumberlands US Department of Electronics And Communication Engineering Karpagam Institute of Technology Coimbatore Management ASM's Institute of Business Management and Research Pune Department of Informatics Doctoral Program of Environtmental Science University of Lampung Bandar Lampung Department of Industrial Engineering Shri Ramdeobaba College of Engineering and Management Nagpur Maharashtra India

Deep gaining knowledge is a synthetic intelligence approach used to recognize complex facts. It uses many facts to recognize the complicated relationships between various factors. With deep learning, models can be built which might be capable of understanding the complicated patterns within big datasets. In this paper, we can explore using deep mastering in regression and type tasks on large datasets. In particular, we can use deep learning strategies to build a regression model that predicts a target value primarily based on the functions in a massive dataset and a classification version that can classify an object into one of the many classes of an extensive dataset. We can then discuss the overall performance of the fashions in comparison to standard models and talk about capability areas for development. Moreover, we will analyze the scalability of deep mastering strategies used on large datasets and discuss exceptional practices for building deep mastering fashions on massive datasets. Finally, we will summarize our findings and offer our conclusions approximately the use of deep studying in regression and type obligations on large datasets.

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Comparative study of leaf image recognition with a novel learning-based approach

Comparative study of leaf image recognition with a novel lea...

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Science and information Conference (SAI)

作者： Jou-Ken Hsiao Li-Wei Kang Ching-Long Chang Chih-Yang Lin Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Graduate School of Engineering Science and Technology-Doctoral Program National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

关键词： Image recognition Dictionaries Training Feature extraction Shape Vectors Image coding

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