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Improved accuracy of long short term memory (LSTM) algorithm in chili price prediction

AIP Conference Proceedings 2024年第1期3222卷

作者： Rizki Ramadhansyah Benny Benyamin Nasution Ade Candra Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Computer Engineering Program Computer Engineering and Informatics Politeknik Negeri Medan Medan Indonesia

The unstable price of chili is still a serious problem in society. Many previous studies have created systems that can predict chili prices. However, there are no chili price prediction results that are considered quite accurate and consistent. So the problem is overcome by processing chili price data using interpolation and dropna on missing values in order to maintain data quality and using the Long Short Term Memory (LSTM) algorithm development in predicting chili prices. This LSTM development was carried out to improve the inconsistent chili price prediction results due to the high error value. LSTM development is carried out in the cell state (ct) and hidden state (ht) with the aim of obtaining better prediction results. Then the accuracy of the error value obtained from the LSTM development in Labuhanbatu district is MAE = 2.589, RMSE = 3.419, and MSE = 11.695.900. This value is lower than using the original LSTM and only using dropna in processing missing value data, namely MAE = 5.517, RMSE = 7.930, and MSE = 62.900.289. Then the difference in percentage error value from the comparison is MAE = 53.07%, RMSE = 56.88%, and MSE = 81.41%. Therefore, it is expected that the low error value results from the development of LSTM can be an indicator of increasing the accuracy of chili price prediction results and making the results more consistent.

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Enhancing Protein Sequence Classification with a Fuzzy Neural Network: A Study in Anticancer Peptide Identification

Enhancing Protein Sequence Classification with a Fuzzy Neura...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： Nguyen Quoc Khanh Le Van-Nui Nguyen Thi-Tuyen Nguyen Thi-Xuan Tran Trang-Thi Ho Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan University of Information and Communication Technology Thai Nguyen University Thai Nguyen Vietnam University of Economics and Business Administration Thai Nguyen University Thai Nguyen Vietnam Department of Computer Science and Information Engineering TamKang University New Taipei Taiwan

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

In bioinformatics, classifying protein sequences into anticancer peptides (ACPs) and non-ACPs is crucial yet challenging due to the inherent uncertainties of biological data. This study introduces a novel fuzzy neural network (FNN) model that integrates fuzzy logic within neural network architectures, enhancing the handling of ambiguity and improving classification accuracy. Our model, tested against several conventional machine learning models and recent studies, demonstrated superior specificity (83.28%) and overall accuracy (79.14%), marking a significant advancement in the identification of therapeutically relevant peptides. The integration of fuzzy logic not only optimized the performance but also increased the interpretability of the results, making it a valuable tool for complex bioinformatic analyses. These findings underscore the potential of fuzzy systems to refine predictive capabilities in computational biology, aligning perfectly with the themes of enhancing fuzzy theory applications in practical and impactful ways.

关键词： Fuzzy logic Uncertainty Accuracy Biological system modeling Peptides Machine learning Fuzzy neural networks

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Low-Cost Air Box Application for Indoor Environment Classification and LSTM Microclimate Prediction

Low-Cost Air Box Application for Indoor Environment Classifi...

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International Conference on Control and Robots (ICCR)

作者： Hu Hsun-Jui Chiu Yu-Hsien Yang Pei-Ching Master Degree Program in Information and Design Chang Jung Christian University Tainan Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung Taiwan Artificial Intelligence Research Center Chang Jung Christian University Tainan Taiwan Department of Computer Science and Information Engineering Chang Jung Christian University Tainan Taiwan

ISBN: (数字)9798331518158

ISBN: (纸本)9798331518165

Air pollution is a significant issue because it burdens human health and the environment. Particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5) can penetrate the lungs and enter the bloodstream, seriously affecting the cardiovascular system, causing strokes, and impacting the respiratory system. Modern individuals spend 70% to 90% of their time indoors, making indoor air quality even more important than outdoor air quality. To estimate and predict indoor air quality, this study utilizes low-cost, self-assembled air boxes based on the Internet of Things to collect various air quality data. The collected data is then used in an indoor air quality forecasting system that combines Long Short-Term Memory (LSTM) and K-means clustering methods to compare the overall performance of the algorithms. The experimental results are analyzed and statistically described based on each model method prediction accuracy and clustering results. These methods are integrated to develop an indoor air quality forecasting system to help indoor populations prevent issues in advance or optimize energy usage.

关键词： Temperature measurement Temperature sensors Atmospheric modeling Predictive models Air quality Data models Forecasting Long short term memory Monitoring Indoor air quality

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The Influence of Business Strategy, Leadership Styles and Business Process on Quality of Accounting Information 21

The Influence of Business Strategy, Leadership Styles and Bu...

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Proceedings of the 2021 5th International Conference on Software and e-Business

作者： Meiryani Meiryani Zaidi Mat Daud Jajat Sudrajat Tommy Andrian Suryadiputra Liawatimena Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia BINUS Entrepreneuship Center Management DepartementBINUS Business School Undergraduate Program Bina Nusantara University Indonesia Accounting Department Faculty of Economics and Communication Bina Nusantara University Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Indonesia

ISBN: (纸本)9781450385831

This study aims to determine the effect of business strategy on the quality of accounting information, to determine the effect of leadership style on the quality of accounting information and to determine the effect of business processes on the quality of accounting information. This study uses primary data from a population of State-Owned Enterprises throughout Indonesia with a total sample of 61 state-owned enterprises, and is analyzed using a simultaneous equation estimation model or SEM (Structural Equation Modeling), the sample determination is done by purposive sampling. The results showed that (1) leadership style and business processes affect the quality of accounting information systems (2) business strategy has no effect on the quality of accounting information systems on accounting information systems at State-Owned Enterprises in Indonesia.

关键词： Business Strategy Leadership Style Business Process Accounting Information Systems Accounting Information Quality

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Journalist system using named entity recognition based on Twitter location extraction

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AIP Conference Proceedings 2023年第1期2691卷

作者： Abba Suganda Girsang Sani Muhamad Isa Raditya Fajar BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Interactions that occur on Twitter social media are easier to do and can easily reach all levels of society, as well as conversations tweeted by users. They can easily spread information or issues that are developing. Based on this, the use of conversational data on the Twitter platform can get an overview of issues that are developing and even those that are just being formed from community conversations on this Twitter platform. Still related to previous research, which researched to implement the Twitter social media monitoring system to provide access to additional information for journalists. This study aligns with its predecessors but comes with a richer data collection mechanism, so it can gather more conversations. The difficulty to be answered in this study is the difficulty of journalist determining a bunch of location of an incident on a Twitter conversation, which describes the name of a place or contains the address of a location in it. Also in this study, there are additional modifications to extract location names using the NER model, which has been customized according to the structure of naming location names in Indonesia. So from the text of the Twitter conversation, whether it's an address name or a description of a location name, it can get a location name that refers to the name of a place where it could be the location where an event occurred. The results of an Indonesian language location NER model, with an accuracy score of 97.67%, with a precision value of 90.57%, recall 59.30% and f1-score 71.67%.

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Weight-dropped long short term memory network for stock prediction with integrated historical and textual data

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2020年第3期47卷 367-377页

作者： Thayogo Wibowo, Antoni Computer Science Department Binus Graduate Program-Master of Computer Science Bina Nusantara University Indonesia

Investors and traders need an accurate stock prediction model to help them make decisions. They can use deep learning models such as Long Short-Term Memory Network (LSTM). However, a weakness of LSTM is that it tends to overfit to the training data and have unstable results. To overcome this weakness, this paper proposes using Averaged Stochastic Gradient Descent and Weight-Dropping on an LSTM network (AWD-LSTM). The proposed model regularizes the network by weight-dropping with DropConnect and optimizes the training process using a Non-Monotically Triggered Averaged Stochastic Gradient Descent (NT-ASGD). Additionally, this paper tested with integrating historical data with textual data which was shown to be valuable by other studies. This paper evaluated six variants of the model with different regulizers, optimizers, and data. The results show that (1) DropConnect regulizer performed better than DropOut or No Drop;(2) Adam optimizer is better for stock prediction than NT-ASGD;(3) Adding textual data slightly increases performance;(4) The models were able to gain consistent profits in a market simulation;(5) A variant of the model outperformed a previous study in 4 out of 5 indexes. © International Association of Engineers.

关键词： Optimization

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Efficient and secure semi-quantum private comparison protocol using three-particle GHZ-like states against participant attack

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Modern Physics Letters A 2024年第10期39卷

作者： Yang, Chun-Wei Huang, Yu-Yun Lin, Jason Tsai, Chia-Wei Master Program for Digital Health Innovation China Medical University No. 100 Section 1 Jingmao Road Beitun District Taichung 406040 Taiwan Department of Computer Science and Engineering National Chung Hsing University No. 145 Xingda Road South District Taichung 40227 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Section 3 Sanmin Road North District Taichung 40401 Taiwan

This study identifies a participant attack vulnerability in Li et al.'s SQPC protocol. The participant attack allows a malicious participant, Bob, to obtain the participant Alice's secret information by intercepting and measuring photons sent to Alice, and later decrypting Alice's encrypted comparison result. An improved SQPC protocol is proposed to address this problem. The key enhancement is introducing a mutual eavesdropping check requiring the third party and participants to verify they obtain the same photon measurement results. This enables detecting participant attacks like the one identified. The SQPC protocol proposed in this study not only withstands attacks from internal participants but also achieves quantum efficiency almost equivalent to Li et al.'s SQPC protocol. Thus, this study presents a more secure SQPC protocol without compromising quantum efficiency. © 2024 World Scientific Publishing Company.

关键词： GHZ-like state participant attack Quantum cryptography semi-quantum private comparison

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Development of expert system for skin diseases based on named-entity recognition (NER) and fuzzy inference

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AIP Conference Proceedings 2024年第1期3201卷

作者： Faturahman Yudanto Muhamad Fahmi Naurah Nazhifah Aina Musdholifah Retantyo Wardoyo Afiahayati Master Program in Artificial Intelligence Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

The skin is an important part of the human body to be cared for. In healthy conditions, the skin has an important role in protecting the body from various diseases and infections. On the other hand, the skin is also susceptible to diseases caused by viruses such as measles, German measles, and chickenpox. In that case, an expert system to detect measles, German measles, and chicken pox is required. Fuzzy Inference has the ability to give flexible recommendations in the expert system. Named-entity recognition (NER) can detect symptom entities in text input that is inputted by the user in the form of natural language. In this study, we apply fuzzy inference and NER to develop an accurate expert system. The NER model, which obtained the best F1 score of 0.83, was able to detect entity symptoms with a very good level of accuracy using the ALBERT model, according to the data presented above. A web-based system that is very user-friendly has been developed as a result of the success of this trial, making it simpler for users to enter symptoms of children’s skin conditions. Based on symptom data that is entered in the form of free text, this system’s implementation has demonstrated its capacity to offer a diagnosis of children’s skin disease.

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A Diversity Inventory Monitoring System of Riparian Vegetation

A Diversity Inventory Monitoring System of Riparian Vegetati...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Dian Pratama Putra Nanda Satya Nugraha Teddy Suparyanto Alam Ahmad Hidayat Digdo Sudigyo Bens Pardamean Faculty of Agricultural Technology STIPER Agricultural Institute Yogyakarta Indonesia Faculty of Agricultural Technology Faculty of Forestry STIPER Agricultural Institute Yogyakarta Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

To strengthen conservation efforts for preserving biodiversity in a conservation area, forest inventory is important to understand the natural succession process in the area and to establish a monitoring strategy. Further, tree inventory aims to monitor the output yielded in the area. More specifically, the tree inventory in the watershed area plays a key role to achieve Sustainable Development Goals (SDG), especially in riparian zones which are also vital parts of green zones in forests. However, the traditional inventory approach is time-consuming and laborious therefore the development of an expert system to assist in inventory monitoring is required. In this study, we develop a monitoring system via a mobile application to collect, analyze and visualize tree inventory data. The application includes algorithms required to compute tree biodiversity, distribution, and richness for the given input of the data of all tree species in a conservation area. For the model validation stage, we compare the traditional inventory approach with our proposed application-based approach to compute diversity inventory in two riparian locations: Klaten Conservation Park and Wonosobo Conservation Park. After the three-day data collection in the areas, we obtain that the accuracy of reading data of our proposed system can achieve more than 90% in comparison with the manual approach. This demonstrates that the system can assist forestry workers to perform more efficient tree inventories in different locations.

关键词： Vegetation Manuals Forestry Soil Mobile applications Biodiversity Expert systems

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Preprocessing Techniques for Enhancing Vision-Based Detection System for Tire Sidewall Extraction

Preprocessing Techniques for Enhancing Vision-Based Detectio...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Jia Hao Loo Veronica Lestari Jauw Chin Seong Lim Khairul-Rijal Sarmin Nunung Nurul Qomariyah Ahmad Nurul Fajar Department of Mechanical Materials and Manufacturing Engineering University of Nottingham Malaysia Malaysia Continental Tyre Technology Centre Malaysia Petaling Jaya Malaysia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program-Master Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

This study aims to develop a system for extracting crucial information from tire sidewalls using Optical Character Recognition (OCR). Initially, images of tire were captured manually by smartphone cameras, including Redmi 9T, iPhone 11, and Galaxy S23 Ultra. The captured images are then transferred to a computer for storage. Subsequently, these images were cropped according to the boundaries identified by Hough Circle Transform (HCT). The cropped images were then further pre-processed. During the pre-processing phase, geometrical transformation and image sharpening techniques are applied to enhance the clarity and readability of the text images. The text is then extracted using Google Vision, with the extracted text categorized by size, DOT, brand and pattern. The results indicated that the effectiveness of image pre-processing was constrained by the accuracy of circle detection, which reached a maximum rate of 87.1%. This causes parts of the text to be cut out inaccurately, leading to a suboptimal extraction accuracy of 55.65%. It is also observed that the Redmi 9T camera produced inconsistent results compared to other devices. Specifically, the iPhone 11 and Samsung Galaxy S23 Ultra demonstrated superior extraction accuracies of 69.71% and 66.37%, respectively, whereas the Redmi 9T achieved a lower extraction accuracy of 37.76%.

关键词： Accuracy Optical character recognition Text categorization Lighting Transforms US Department of Transportation Cameras Tires Internet Data mining

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