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Development of a web-Based Forecasting System Using the Holt-Winters Exponential Smoothing Method to Improve Accuracy in Predicting Cut Flower Harvest Needs

Development of a Web-Based Forecasting System Using the Holt...

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international conference on Cybernetics and Intelligent System (ICORIS)

作者： Wayan Cishe Fransiska Saputri Farid Fitriyadi Hardika Khusnuliawati Informatics Sahid University of Surakarta Surakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

information is the result of data processing so it becomes important for the recipient and is useful as a basis for decision making. In the flower business, production information is needed to determine the number of cut flowers to match customer demand in the future. this aims to minimize losses that occur due to a shortage or excess of cut flowers. there are still live flower shops that use manual methods and feelings to determine cut flower needs. therefore, a website-based forecasting system is needed to provide estimated flower cuts for the next period for businessmen. the application was built using the PHP programming language by implementing the Holt-Winters exponential smoothing forecasting method. the Holt-Winters method was used to estimate flower needs, especially cut flowers for bouquet products. Calculation of error score in the method was calculated by MAPE testing. Based on the test results, it is known that the error score in the Holt-Winters forecasting method for the number of cut flowers in a bouquet of Aster, Roses, and Pikok is 13.89%, 16.54%, and 13.23% respectively and they are in Good category. this method has lower error percentage for Aster, Rose, and Pikok flower respectively by 3.95%, 1.86%, and 14.01% than by using florist former method.

关键词： Smoothing methods Accuracy Systematics Training data Production Manuals Flowering plants Programming Forecasting Testing

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Plant Growth Monitoring Cloud Platform Based on Internet of things 6

Plant Growth Monitoring Cloud Platform Based on Internet of ...

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6th IEEE international conference on Intelligent Computing and Signal processing, ICSP 2021

作者： Ma, Haolun Qi, Jin Li, Ke He, Chen Northwest University College of Information Science and Technology Xi'an710127 China

ISBN: (纸本)9780738143705

the Internet of things (IoT) technology has been widely used in the agricultural field. this article introduces a plant monitoring system based on IoT cloud platform from three aspects: hardware system design in device side, building of cloud platform, and web app design in cloud and App side. Firstly, hardware design includes the embedded core system and various environmental data sensors. then this paper describes the process of writing codec plug-ins in the cloud, which enables the hardware system to transmit data such as CO2, soil temperature and humidity, and ambient temperature and humidity to the cloud platform via the NB-IoT protocol. through a web app, the monitoring system can alert the user if watering and ventilation are needed. After that, this research designed the mobile app by secondary development of the demo provided by the cloud through Android studio. this system will facilitate the management of crops. © 2021 IEEE.

关键词： Internet of things

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Promo-Zen: A Natural language processing Based Promotional Text Message Management System

Promo-Zen: A Natural Language Processing Based Promotional T...

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Advancements in Computing (ICAC), international conference on

作者： Chamath Udugampola Nethmi Weerasingha Department of Computer and Data Science NSBM Green University Homagama Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. these objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. the system utilizes a novel workflow/pipeline combining a Natural language processing (NLP) model with Named Entity Recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8–10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use.

关键词： Named entity recognition Developing countries Message services Feature extraction Logic Data mining Pattern matching Testing Context modeling

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A Comparative Assessment of State-Of-the-Art Methods for Multilingual Unsupervised Keyphrase Extraction 17th

A Comparative Assessment of State-Of-The-Art Methods for Mul...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Giarelis, Nikolaos Kanakaris, Nikos Karacapilidis, Nikos Univ Patras Ind Management & Informat Syst Lab MEAD Rion 26504 Greece

ISBN: (纸本)9783030791520;9783030791506;9783030791490

Keyphrase extraction is a fundamental task in information management, which is often used as a preliminary step in various information retrieval and natural language processing tasks. the main contribution of this paper lies in providing a comparative assessment of prominent multilingual unsupervised keyphrase extraction methods that build on statistical (RAKE, YAKE), graph-based (TextRank, SingleRank) and deep learning (KeyBERT) methods. For the experimentations reported in this paper, we employ well-known datasets designed for keyphrase extraction from five different natural languages (English, French, Spanish, Portuguese and Polish). We use the F1 score and a partial match evaluation framework, aiming to investigate whether the number of terms of the documents and the language of each dataset affect the accuracy of the selected methods. Our experimental results reveal a set of insights about the suitability of the selected methods in texts of different sizes, as well as the performance of these methods in datasets of different languages.

关键词： Natural language processing Keyphrase extraction Unsupervised learning Deep learning Graph-based models Empirical research

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Text Summarization using NLP Technique 6

Text Summarization using NLP Technique

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6th IEEE international conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Balaji, N. Megha, N. Kumari, Deepa Sunil Kumar, P. Bhavatarini, N. Shikah Rai, A. Nmam Institute of Technology Nitte Department of Ise Udupi India Sahyadri College of Engg. & Mgmt. Department of Ece Mangaluru India Reva University School of Cse Bengaluru India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

the text data online is increasing massively;hence, producing a summarized text document is essential. We can create the summarization of multiple text documents either manually or automatically. A manual approach may be tedious and a time-consuming process. the resulting composition may not be accurate when processing lengthy articles;hence the second approach, i.e., the automated summary generation process, is essential. Training machine learning models using these processes makes space and time-efficient summary generation possible. there are two widely used methods to generate summaries, namely, Extractive summarization and abstractive summarization. the extractive technique scans the original document to find the relevant sentences and extracts only that information from it. the abstractive summarization technique interprets the original text before generating the summary. this process is more complicated, and transformer architecture-based pre-trained models are used for comparing the text & developing the outline. this research analysis uses the BBC news dataset to evaluate and compare the results obtained from the machine learning models. © 2022 IEEE.

关键词： Natural language processing systems

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Word Sense Disambiguation for BODO language Using Simplified LESK 6th

Word Sense Disambiguation for BODO Language Using Simplified...

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6th international conference on Computational Intelligence in Communications and Business Analytics, CICBA 2024

作者： Basumatary, Subungshri Barman, Manas Narzary, Puja Barman, Anup Kumar Nag, Amitava Department of Computer Science and Engineering Central Institute of Technology Assam Kokrajhar783370 India

ISBN: (纸本)9783031813412

Word sense disambiguation (WSD) is the computational process of identifying a word's most appropriate sense in a given context, especially when words have many meanings. Word sense disambiguation (WSD) is a challenging task in natural language processing, partially because of the intrinsic complexity of both computer-based reasoning and human cognition. the main goals of Word Sense Disambiguation (WSD) techniques are the various methodologies that academics have devised to address this challenge and ascertain the precise meaning of a word in a given context. We came across a situation in which most of the words in our experimental sentences were not found in the Bodo Wordnet during our inquiry. As a result, we decided to use the condensed LESK method. the simplified LESK approach involves determining the intended meaning of a target word by identifying the sense gloss in Wordnet that has the greatest degree of overlap with the context word being considered. this is the essence of how the simplified LESK algorithm is employed for disambiguation. To the best of our current understanding, this study signifies the initial attempt in employing the LESK algorithm for the purpose of disambiguating word senses in the Bodo language. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ambiguity Bodo language LESK Natural language processing (NLP) Word sense disambiguation (WSD)

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A Study to Classify Virus Genome through Analyzing DNA Sequences Using Transformer Model

A Study to Classify Virus Genome Through Analyzing DNA Seque...

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international conference on Electrical Engineering and information & Communication technology (ICEEICT)

作者： Tasnim Binte Shiraj Mohammad Abu Yousuf Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Institute of Information Technology Jahangirnagar University Savar Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

In a generic computational environment for biological data processing, classifying DNA sequences is an important task. In recent times, a range of machine learning methods have been successfully applied to implement this task. Still, the hardest part of the process is choosing the features. the most popular representations exacerbate the massive dimensionality issue, because sequences lack unique features. Due to the pathogenic virus's high rate of transmission, early detection and accurate identification are essential for the treatment. It is challenging to predict though, because this virus's polymorphic nature enables it to adapt and survive in a variety of environment. Deep learning (DL) models have recently developed the ability to automatically extract characteristics from the input sequences. Previously, CNN-Bidirectional LSTM and CNN-LSTM structures with Label and K-mer encoding, Random Forests and Gradient Boosting Method, and Linear Discriminant Analysis have been utilized for DNA sequence classification. In this paper, we investigate the impact of a popular and well-established language model, namely Transformer (BERT) which uses self-attention mechanism, to alter the procedure to understand underlying hidden patterns of the virus DNA sequences and correctly classify them.

关键词： Pathogens Computer viruses Biological system modeling DNA Transformers Encoding Convolutional neural networks

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How to pretrain an efficient cross-disciplinary language model: the ScilitBERT use case 6

How to pretrain an efficient cross-disciplinary language mod...

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6th international conference on information technology Research, ICITR 2021

作者： De La Broise, Jean-Baptiste Bernard, Nolwenn Dubuc, Jean-Philippe Perlato, Andrea Latard, Bastien MDPI Basel Switzerland

ISBN: (纸本)9781665420006

Transformer based models are widely used in various text processing tasks, such as classification, named entity recognition. the representation of scientific texts is a complicated task, and the utilization of general English BERT models for this task is suboptimal. We observe the lack of models for multidisciplinary academic texts representation, and on a broader scale, a lack of specialized models pretrained on specific domains, for which general English BERT models are suboptimal. this paper introduces ScilitBERT, a BERT model pretrained on an inclusive cross-disciplinary academic corpus. ScilitBERT is half as deep as RoBERTa, and has a much lower pretraining computation cost. ScilitBERT obtains at least 96% of RoBERTa's accuracy on two academic domain downstream tasks. the presented cross-disciplinary academic model has been publicly released11https://***/JeanBaptiste-dlb/ScilitBERT. the results obtained show that for domains that use a technolect and have a sizeable amount of raw text data;the pretraining of dedicated models should be considered and favored. © 2021 IEEE.

关键词： Text processing

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Sentiment Analysis using a Long Short-Term Memory Recurrent Neural Network on Filipino Tweets: A Case Study for Bank A 23

Sentiment Analysis using a Long Short-Term Memory Recurrent ...

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Proceedings of the 2023 6th international conference on Computational Intelligence and Intelligent Systems

作者： Julio Jeremy Escudero Macrohon Joel C De Goma Julio Jerison Escudero Macrohon School of Information Technology Mapua University Philippines Department of Information Engineering I-Shou University Taiwan

ISBN: (纸本)9798400709067

Social media has emerged as a significant source of big data, offering a vast amount of social data and evidence. However, much of this data remains untapped and underutilized. Nowadays, various organizations, including corporations, government agencies, and data enthusiasts, are actively seeking to extract and leverage this data to create data models that drive actionable insights, helping them achieve their goals. this study focuses on examining customer sentiments towards Bank A, one of the largest banking and financial institutions in the Philippines with a history spanning over 50 years. By employing natural language processing techniques, we aimed to gain a comprehensive understanding of the general sentiment towards the bank's products, services, and overall brand satisfaction. To accomplish this, we annotated the sentiments and utilized them as training and testing data for a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). this model was designed to classify tweets in both English and Filipino languages into two polarities and demonstrated an accuracy rate of 88.50%.

关键词： Filipino Tweets LSTM RNN natural language processing sentiment analysis twitter

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Revelation of Geospatial information using Augmented Reality 6

Revelation of Geospatial Information using Augmented Reality

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6th international conference on Wireless Communications, Signal processing and Networking (IEEE WiSPNET)

作者： Bharath, G. Ch, Rupa Karthik, M. Bhavani, Leela A. Chowdary, Manish, V VR Siddhartha Engn Coll A Dept CSE Vijayawada India

ISBN: (纸本)9781665440868

Augmented Reality (AR) is the technology that enables us to combine digital knowledge into the user's real-world space. Augmented Reality uses the existing environment to overlay digital content and information onto the physical world. the Proposed method demonstrates AR's use to provide a lively and visual representation of information about the geography of different masses on the globe and how AR can be utilized to enhance research studies and education by visualizing data collected by various statistical sources. this work helps to visualize the globe in Augmented Reality where one can overlay any OGC (Open Geospatial Consortium) web Map Service (WMS) layer onto the globe's surface and interact with it to understand the visualization of statistics around the world. Users can utilize any pre-defined marker, target or placeholders of the object, which is trained beforehand to be recognized later in the camera stream. When seeing the placeholder from the camera of phone, an augmented reality globe must appear over the placeholder. Moving the phone camera, it have to be possible to overlay any web Outline on the globe. this application must provide an interface to choose the data such as Climate, Sea Floor Temperature, Social Progress, World Literacy information to be overlaid on the globe. Real-time information changes according to the observer's movement as if that information truly exists in the real world.

关键词： Augmented reality Image target Rendering Vuforia Super imposition 3DModel

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