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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Machine Learning Models-Based Forecasting Moroccan Stock Market 7th

Machine Learning Models-Based Forecasting Moroccan Stock Mar...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Oukhouya, Hassan El Himdi, Khalid Mohammed V Univ Rabat Fac Sci Dept Math Lab LMSA 4 Ave Ibn BattoutaBP 1014 RP Rabat Morocco

ISBN: (纸本)9783031686276;9783031686283

This paper explores the critical domain of finance research by focusing on modeling and forecasting daily prices for the Moroccan All Shares Index (MASI) across diverse sectors. Recognizing the pivotal role of predicting stock price movements in shaping investment strategies, we conduct a comprehensive comparative study employing various Machine Learning (ML) methods, including eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), SVR-XGBoost, MLP-XGBoost, and LSTM-XGBoost models. Leveraging an optimized Grid Search (GS) algorithm, our results highlight the superior performance of SVR-XGBoost and LSTM-XGBoost models, emphasizing their efficacy and accuracy in predicting daily prices for the MASI.

关键词： Modeling MASI price Sectors Forecasting GS SVR XGBoost MLP LSTM SVR-XGBoost MLP-XGBoost LSTM-XGBoost

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A Case Study of Student Performance Predictions in English Course: The Data Mining Approach 9th

A Case Study of Student Performance Predictions in English C...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Tran Duc Quynh Nguyen Doan Dong Nguyen Quang Thuan Vietnam Natl Univ Int Sch Hanoi Vietnam

ISBN: (纸本)9789819732883;9789819732890

Data mining techniques, by providing numerous insights, have been recently used and have improved many aspects of life, including education. Analyzing the students' records and predicting their performance of students can bring a lot of benefits to the education system. As a university with special training programs such as training in English, dual degree education, and technology programs, International School-Vietnam National University, Hanoi, Vietnam is on the rise and will benefit from the application of data mining. In this paper, we conduct an analysis of the pre-English course performance of students at the International School with the aim of obtaining positive predictive results through appropriate data engineering, performing exploratory data analysis, and building predictive models of the students' study time for the pre-English course. Therefore, the school can consider those as the reference for program adjustments.

关键词： Student performance Data mining Ensemble learning

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CollaVRation: An Immersive Virtual Environment for Collaborative Software Development 1

引用

Computing Conference

作者： Kozma, Miroslav Vincur, Juraj Kapec, Peter Slovak Univ Technol Bratislava Fac Informat & Informat Technol Ilkovicova 2 Bratislava 84216 4 Slovakia

ISBN: (数字)9783031622731

ISBN: (纸本)9783031622755;9783031622731;9783031622724

The increasing complexity of the software systems currently developed also increases the need for collaboration between multiple developers. In this paper, we propose and describe a collaborative environment in virtual reality with advanced features like live programming, UML class diagram visualization, and round-trip engineering. We conducted a user study that focuses on evaluating the use of VR in collaboration and aims to confirm whether such an approach is more effective compared to classical approaches when performing programming tasks. We evaluated two collaboration styles: a tutor-student approach that is common in academia and collaborative programming typical for pair programming in practice. The results showed that the proposed collaborative virtual reality environment was more attractive, stimulating, and novel than traditional approaches.

关键词： Virtual reality Collaborative programming Round-trip engineering Live programming

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Efficiency Calculation with Interval Type-2 Fuzzy Sets

Efficiency Calculation with Interval Type-2 Fuzzy Sets

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Ercan-Teksen, Hatice Eskisehir Osmangazi Univ Eskisehir Turkiye

ISBN: (纸本)9783031671913;9783031671920

Multi-criteria decision-making problems are a type of problem used in many areas. Daily life problems are too complex to be solved with a single criterion. Therefore, a solution that evaluates multiple criteria together is needed. Multi-criteria decision-making methods are used for the solution. Supplier selection problems have multiple criteria to select the supplier. For this reason, supplier selection problems are multi-criteria decision-making problems. Within this study, it is thought that the time spent while making decisions in supplier selection is much more than the time spent by creating a fuzzy system, and it is aimed to calculate the efficiency of the fuzzy system created in terms of the time spent by using the fuzzy number again. In this study, a fuzzy multi-criteria decision-making problem is defined for supply selection and a decision support system is designed for this problem. Considering that the problems are solved quicker with this system, an increase in efficiency is noticed. A new efficiency calculation is made by using interval type 2 numbers to calculate the efficiency increase. The study compared the classical supplier selection decisions with fuzzy decision-making system. As a result of this comparison, it is noticed that the time is saved even in a decision-making problem. It is thought that there is a time planning before it will be written by a problem department for use. Considering that the supplier selection problem is frequently made by the department, a serious time saving is noticed. No study is found on the use of fuzzy numbers in efficiency calculation in the accessible literature. This study will contribute to the literature by creating a new efficiency formula using interval type-2 fuzzy number in efficiency calculation.

关键词： Interval Type-2 Fuzzy Set Fuzzy Multi-Criteria Decision Making Fuzzy Efficiency Supplier Selection Problem

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Remote Monitoring Algorithm for Vegetation Recovery on Mining Industry Sites 13th

Remote Monitoring Algorithm for Vegetation Recovery on Minin...

引用

13th Computer Science Online Conference

作者： Orlov, Sergei Tsygulev, Kirill Sekrieru, Roman Smagin, Sergei Smagin, Aleksei Russian Acad Sci Far Eastern Branch Comp Ctr Khabarovsk 680000 Russia Russian Acad Sci Min Inst Far Eastern Branch Khabarovsk 680000 Russia

ISBN: (纸本)9783031702846;9783031702853

The aim of this research is to develop an algorithm for the automatic analysis of natural vegetation recovery on disturbed lands for monitoring open-pit mining sites based on remote sensing data (RSD). Images from Sentinel-2 satellites of mineral extraction areas in open-pit mines were chosen as the initial data. The feasibility of using a convolutional neural network for determining the boundaries of the object under study by forming its mask is considered. The modified Normalized Difference Vegetation Index (mNDVI) is used as an additional channel for the neural network, which helps to increase the accuracy of defining the mentioned boundaries. To divide the quarry territory into three classes (water, vegetation, soil) and to construct the corresponding class map, the Modified Normalized Difference Water Index (MNDWI) and the Soil and Atmospherically Resistant Vegetation Index (SARVI) are applied to the obtained mask. The class map allows for more detailed monitoring and analysis of the studied objects.

关键词： algorithm monitoring remote sensing Sentinel-2 convolutional neural network

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Prediction of Sexually Transmitted Diseases Using Deep Convolutional Neural networks for Image Data 2nd

Prediction of Sexually Transmitted Diseases Using Deep Convo...

引用

2nd International Conference on Forthcoming networks and Sustainability in the AIoT Era (FoNeS-AIoT)

作者： Mahameed, Ans Ibrahim Mahmood, Rafah Kareem Univ Tikrit Coll Educ Pure Sci Dept Math Tikrit Iraq Univ Technol Baghdad Coll Engn Electromech Engn Dept Baghdad Iraq

ISBN: (纸本)9783031628702;9783031628719

This study uses deep convolutional neural networks to build an STD prediction decision assistance system. Since conventional STD prevention strategies have failed in poor nations, experts have examined social media-based disease control. Deep convolutional neural networks cannot successfully represent data due to their short duration, excessive noise, and informality. Disease tweets were categorized using character-level word vectors from deep learning to develop an epidemic prediction model. Our prediction algorithm missed formal events but notified us 14 days ahead. Our Deep Convolutional Neural Network (DCNN) technology beats cutting-edge methods for this challenge. Improved procedure efficiency and accuracy have been achieved through innovation. Research using convolutional neural networks improved HIV/AIDS detection and categorization. Additionally, an STD prognostic model was developed. The collection comprises benign and malignant STI patient data. Training comprises 80% of the dataset, and testing and validation comprise 20%. 10-fold cross-validation verifies the data. To test the approaches, we gathered 10 IDs with a low-resolution depth camera. They trained and tested on the dataset. Anaconda was the creator of Python algorithms. The basic algorithm of DCNN fails to manage noise and uneven light, creating a worthless output. Graphic representations of foreign script characters can help teach a new language and overcome initial obstacles. Preprocessing lowers noise and low-light issues. Our hypothesis predicted HIV and STDs with 95.47% accuracy, surpassing all previous hypotheses. Nobody equaled our success.

关键词： Hyperspectral Imagery Biological Data De-striping DCNN Hyperion Data

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Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification 1

引用

4th Congress on Intelligent systems (CIS)

作者： Mukku, Lalasa Thomas, Jyothi CHRIST Kengeri 560074 India

ISBN: (数字)9789819990436

ISBN: (纸本)9789819990429;9789819990436

It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patient's primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches.

关键词： Cervical cancer Deep learning Early fusion

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Identification of Phishing URLs Using Machine Learning Models 1

引用

4th Congress on Intelligent systems (CIS)

作者： Vivek, Meghashyam Premjith, Nithin Johnson, Aaron Antonio Maurya, Ashutosh Kumar Jingle, I. Diana Jeba Christ Bangalore Karnataka India

ISBN: (数字)9789819990436

ISBN: (纸本)9789819990429;9789819990436

In this study, we provide a machine learning-based method for identifying phishing URLs. Sixteen features, including Have IP, Have At, URL Length, URL Depth, Non-standard double slash, HTTPS domain, Shortened URL, Hyphen Count, DNS Record, Domain age, Domain active, iFrame, Mouse Over, Right click, Web Forwards, and Label, were extracted from the 600,000 URLs we gathered as a dataset of legitimate and phishing URLs. We then used this dataset to train a variety of machine learning models. These included standalone models such Naive Bayes, Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN). We also used ensemble models like Hard Voting, XGBoost, Random Forests, and AdaBoost. Finally, we used deep learning models such as Artificial Neural networks (ANN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and Convolutional Neural networks (CNN). On evaluation of performance metrics like accuracy, precision, recall, train time and prediction time it was found that XGBoost provides the best performance across all categories.

关键词： XGBoost Phishing Prediction Machine learning Classifier

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Addressing the Business Disruption Through the Development and Pilot Testing of Caviturismo: A Web Portal for Cavite Tourism 9th

Addressing the Business Disruption Through the Development a...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Muyot, Allen Jhon C. Nabablit, Karlo Jose E. Nolledo, Mark Edriane F. Victa, Maria Socorro D. Abad, Jenerry Y. Cavite State Univ CCAT Campus Dept Comp Studies Rosario Cavite Philippines

ISBN: (纸本)9789819732982;9789819732999

Tourism is a vital economic driver in the Philippines, contributing significantly to the nation's GDP and employment rates. Cavite is one of the promising tourist destinations in the CALABARZON region, known for its natural beauty, rich cultural heritage, and historical significance. In recent years, business disruption accelerated caused by the COVID-19 pandemic when consumer behavior shifted toward online platforms. However, Filipino micro, small, and medium enterprises (MSMEs) faced a lot of challenges in establishing and keeping up with their online presence, mainly due to technology and financial constraints. The study focused on the development and pilot testing of a web portal for the promotion of tourism in Cavite. In addition, this aimed to support the MSMEs by providing them with costfree, well-designed, and user-friendly business web pages. The website modules underwent rigorous functional and accuracy testing, yielding promising results. This project demonstrates that Caviturismo is a functional and reliable solution capable of effectively promoting the province of Cavite as a tourist destination and the stakeholders.

关键词： Cavite tourism Business pages Web development MSMEs Promoting tourism Business disruption

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A Unified Approach to Real-Time Public Transport Data Processing 12th

A Unified Approach to Real-Time Public Transport Data Proces...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Lazur, Juraj Hynek, Jiri Hruska, Tomas Brno Univ Technol Fac Informat Technol Bozetechova 1-2 Brno 61266 Czech Republic

ISBN: (纸本)9783031602269;9783031602276

The use of real operations data is essential for the planning and management of modern public transport systems. With the expansion of universal formats for describing the structure of public transport systems, such as GTFS or Transmodel, the use of these data has expanded far beyond the public transport domain. On the other hand, the effort to use these data encounters the problem of its processing, storage and integration with the structure of the transport system due to the volume and speed of data generation from real operations. These problems are even more evident in the case of further use of these data as inputs for machine learning, or data mining, where integration of data from different systems into a single model is necessary. The purpose of this paper was to design a method by the which big data from real operations could be integrated with the changing structure of the transport system so that this data could be stored long term without loss of granularity, or entropy value. As a result, we proposed a data model with big data transformation algorithm, whose functionality has been verified in testing over the public transport system of the second largest city in the Czech Republic.

关键词： Public Transport Big Data Processing Big Data Visualisation GTFS

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