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

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Attention-Based Recurrent Neural Network for Multicriteria Recommendations 1

引用

Intelligent systems Conference (IntelliSys)

作者： Bougteb, Yahya Frikh, Bouchra Ouhbi, Brahim Zemmouri, El Moukhtar Moulay Ismail Univ ENSAM LM2I Lab Marjane IIBP 5290 Meknes Morocco Ecole Natl Super Sci Appliquees LIASSE Lab BP 1796 Fes Morocco

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Traditional recommender systems use a single rating for each item. However, this approach is limited because a single overall rating does not provide sufficient information about the reasons that led to a user's overall rating. Therefore, multicriteria recommender systems have been developed to benefit from user preferences expressed across variety of criteria. Recurrent neural networks (RNNs) have been also used in recommendation systems;indeed, they have proven their efficiency in other fields as speech recognition and machine translation. Nevertheless, the use of various RNN structures was restricted to reviews and session-based single-criteria recommender systems. These methods require useful metadata, such as the history of user activity during a session. In this paper, we propose a sequence-aware Long-Short Term Memory (LSTM) RNN with a custom attention mechanism to predict the overall ratings of users. The user's multicriteria ratings are the only needed data for the proposed approach. Thus, we consider every user's multicriteria rating given for an item as a sequence of data for that user. Extensive experiments conducted on real-world data show that the proposed method outperforms baseline approaches.

关键词： Recurrent Neural Network Deep Learning Multicriteria Recommender System

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Task Scheduling in Cloud Computing Using Harris-Hawk Optimization 11th

Task Scheduling in Cloud Computing Using Harris-Hawk Optimiz...

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Bahar, Iza A. A. Saudi, Azali Kadir, Abdul Nasirin, Syed Amboala, Tamrin Seman, Esmadi A. A. Tahir, Abdullah M. Lada, Suddin Univ Malaysia Sabah Labuan 87000 Malaysia Univ Sari Mulia Banjarmasin City Indonesia

ISBN: (纸本)9783031456473;9783031456480

This paper presents a simulation of the Harris-Hawk Optimization (HHO) algorithm, which aims to minimize the makespan of a specified task set in a cloud computing environment. The algorithm is inspired by the team association of hawks in hunting and escaping prey. It has gained significant attention from researchers due to its effectiveness in solving real-world problems across different applications. As a result, the HHO algorithm has been widely applied to various optimization problems. In this study, the proposed HHO algorithm is simulated and compared with other well-known swarm intelligence algorithms, including Bat Algorithm (BA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The simulation results demonstrate that the HHO algorithm surpasses the other three in producing better results. Furthermore, given the HHO algorithm's reliability in solving single-objective problems, this study further validates its effectiveness in addressing various optimization concerns.

关键词： Task Scheduling BA GWO PSO HHO Cloud Computing

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Scientific Communication in eScience 9th

Scientific Communication in eScience

引用

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

作者： Paletta, Francisco Carlos Univ Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9789819733040;9789819733057

This paper examines the role of scientific communication and information management in the context of eScience research. It discusses how the evolution of information and communication technologies (ICTs) has shaped the operationalization and fluidity of scientific communication. The paper emphasizes the importance of digital literacy and information literacy in the network society. It highlights the mediating role of libraries and information services in scientific communication through knowledge storage, organization, and access. The concept of eScience is explained as data-driven and computationally intensive research methodology enabled by technological infrastructure like high-performance computing, data storage/management, and networks. The paper describes how eScience involves collaboration between computing researchers and domain experts, as well as creation of computational tools and methods to analyze large, complex data. It also covers the fourth paradigm of data-driven science paradigm facilitated by eScience. The paper concludes by underscoring the importance of managing the life cycle of ICTs in digital libraries and repositories to support efficient scientific communication through open access, data sharing, and collaboration platforms. It advocates for an information technology governance model aligned with the mission of digital libraries to effectively organize and communicate knowledge.

关键词： eScience Scientific communication Research Digital literacy Data science

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A Fuzzy Mid-Point Based Decision-Support Method for the Assessment of Investments and Companies 1

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Mulet-Forteza, Carles Serra-Moll, Miquel A. Valero, Oscar Balearic Isl Univ Dept Business Econ Palma De Mallorca 07122 Baleares Spain Balearic Isl Univ Dept Math & Comp Sci Palma De Mallorca 07122 Baleares Spain Hosp Univ Son Espases Hlth Res Inst Balearic Isl IdISBa Palma De Mallorca 07120 Baleares Spain

ISBN: (数字)9783031700187

ISBN: (纸本)9783031700170;9783031700187

In decision-making, we face a situation where we have to decide whether it is worth performing an investment or not, based on a multitude of variables. The Net Present Value (NPV) indicates the profit that our investment will produce over a series of time periods within a fixed time horizon. Its computation begins with certain data from which we can obtain a cash-flow for each time period under consideration. This study will focus on the variables involved in the aforementioned computation that can take values within an interval of possibilities whose end-points are their pessimistic forecast (PF) and their optimistic one (OF). Now, we are in front of infinite different possible forecasts and the expert in charge needs to determine which data is more appropriate among them in order to make the final decision. The objective of this study is to introduce an analytical technique based on the use of L-fuzzy sets, aggregation functions and mid-points to select one forecast among the aforesaid infinite range. This methodology incorporates a penalization, fixed following an analytical procedure and thus reducing subjectivity, for the elapsed time and for the discrepancy between PF and OF in order to generate the NPV. Finally, the methodology is tested by applying it a five-year hotel assessment where real data is considered. Here, it is illustrated that the proposed methodology could be appropriate for the assessment of investments and companies.

关键词： Aggregation Function Decision-Making Company Assessment Investment Mid-Point Time-Dispersion Penalization

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High-Propulsive Trunk Flexion-Extension Mechanism Using Cheetah-Inspired S-Shaped Spine 27th

High-Propulsive Trunk Flexion-Extension Mechanism Using Chee...

引用

27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Hattori, Shoei Fukuhara, Akira Kano, Takeshi Ishiguro, Akio Tohoku Univ Dept Elect Engn Sendai Miyagi 9808579 Japan Tohoku Univ Res Inst Elect Commun Sendai Miyagi 9800812 Japan Future Univ Hakodate Sch Syst Informat Sci Hakodate Hokkaido 0418655 Japan

ISBN: (纸本)9783031707216;9783031707223

In this paper, to improve the agility of quadruped robots, we propose a high-propulsive flexion-extension mechanism using an S-shaped flexible spine inspired by the curved shape of the back of a cheetah. To confirm the effectiveness of the proposed mechanism, we built a trunk-driven running robot and compared its performance with that of a conventional flexion-extension mechanism. The experimental results showed that the running speed of the robot with the proposed flexion-extension mechanism was approximately 1.5 times higher than that of the conventional model.

关键词： quadruped robot flexible spine high-speed running

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Comparative Analysis of Multiclass Classification Machine Learning Models for Cybersecurity Intrusion Detection 4th

Comparative Analysis of Multiclass Classification Machine Le...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Loughmari, Mohamed El Affar, Anass Sidi Mohamed Ben Abdellah Univ Polydisciplinary Fac Taza Engn Sci Lab LSI BP 1223 Taza Morocco

ISBN: (纸本)9783031686528;9783031686535

Recently, the use of the Internet and computer networks in general, has increased exponentially, leading to a high demand for cybersecurity to protect against all kinds of network attacks that are constantly evolving. Nowadays, Machine Learning (ML) is implemented in various cybersecurity tools, Intrusion Detection System-based ML brings more capabilities to improve the detection of cyber attacks. This study aims to perform a comparative analysis of a multiclass classification problem for cybersecurity attack detection. The comparative analysis is performed using six different machine learning algorithms: Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, eXtreme Gradient Boosting, and Multi-Layer Perceptron, which are applied to the full NSL-KDD dataset, and three other subsets of datasets to confirm and verify the findings in terms of precision, accuracy, training time, and testing time. In all dataset subsets we worked on in addition to the NSL-KDD dataset, eXtreme Gradient Boosting significantly beat the other algorithms. From all the experimental results, it is concluded that XGBoost is a plausible choice for an intrusion detection system in terms of all the metrics compared to the other ML algorithms discussed.

关键词： Cybersecurity Machine Learning Network Intrusion detection system NSL_KDD KDDcup UNSW-NB15

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Edge Algorithm of Reconstruction 3D Model by Two of Its Images 13th

Edge Algorithm of Reconstruction 3D Model by Two of Its Imag...

引用

13th Computer Science Online Conference

作者： Klyachin, Vladimir A. Grigorieva, Elena G. Volgograd State Univ Comp Sci & Expt Math Dept Univ Prospect 100 Volgograd 400062 Russia

ISBN: (纸本)9783031705175;9783031705182

The article discusses the problem of calculating the frame of a 3D model from its images obtained by two cameras. The method of such reconstruction is based on calculating the coordinates of the vertices of segments from their projections from two cameras. During reconstruction, an ambiguity arises, which is resolved when an additional condition is met, which is achieved by slightly moving the cameras. Using these assumptions and some mathematical background, we develop an algorithm for the necessary frame reconstruction. #CSOC1120

关键词： central projection 3D model segments spatial reconstruction

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Online Search Attention, Social Media Sentiment and Sustainable Investment Performance 4th

Online Search Attention, Social Media Sentiment and Sustaina...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： El Oubani, Ahmed Moulay Ismail Univ Natl Sch Business & Management Meknes Morocco

ISBN: (纸本)9783031686597;9783031686603

Digitalization is increasing the volume of online searches and people's interaction in social media. This might reflect people's attention and sentiment toward topical issues such as sustainability. This paper aims to investigate the connectedness between online search attention, social media sentiment, and the Environmental, Social and Governance (ESG) index returns in Morocco. Using weekly data and the quantile connectedness approach, the results show that the connectedness between online search attention, social media sentiment and ESG index returns is time-varying and event dependent, especially during the COVID-19 pandemic. This implies that the pandemic has heightened investor sensitivity to sustainability, which might affect ESG index returns. Moreover, online search attention and social media sentiment sometimes act as net receivers and sometimes as net senders. The results also illustrate that spillover effects are mainly driven by the short-term component, except for the COVID-19 outbreak. This research has practical implications for investors, companies, and policymakers.

关键词： Digitalization sustainability ESG index

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Algo-Powered Banking: Enhancing Investment Decisions Through Machine Learning 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Gaikwad, Sanika Gupta, Tanish Singh, Adityapratap Jaiswal, R. C. Pune Inst Comp Technol Pune 411043 India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

Algo-Powered Banking revolutionizes the finance and investment landscape by seamlessly blending machine learning (ML) and algorithmic trading (AT). In a rapidly changing financial world, traditional strategies often fall short. This project addresses this by harnessing ML's power to analyze vast financial data, predict market trends, and offer timely insights. It combines this with AT to automate trading, sidestepping emotional biases. The platform offers personalized investment recommendations, real-time market monitoring, risk assessment tools, and customizable trading strategies through user-friendly interfaces. Its innovation lies in fusing ML-driven predictive analytics and algorithmic trading, aiming to enhance investment outcomes, portfolio performance, and user confidence in financial markets.

关键词： Algo-powered banking Machine learning (ML) Algorithmic trading (AT) Investment Predictive analytics

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Lip Reading Filipino Words Using Convolutional Neural networks and Recurrent Neural networks

Lip Reading Filipino Words Using Convolutional Neural Networ...

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Intelligent systems Conference (IntelliSys)

作者： Crespo, Steven David A. Recario, Reginald Neil C. Univ Philippines Los Banos Laguna Philippines

ISBN: (纸本)9783031477232;9783031477249

Lip reading is a skill that allows a person to recognize words and sentences through the movement of the mouth. The boom of audio-video data, particularly in teleconferencing and streaming platforms, make lip reading an interesting area of research particularly in low resource languages. Using ten Filipino words, the researchers collected a video dataset of Filipinos speaking those words to train and experiment with three different configurations of deep learning models namely Inception v3 CNN and GRU model, VGG-19 CNN and GRU model, and GRU model to lip read Filipino words and to determine each configuration's accuracy. Different combinations of parameters were used for each model, along with both original and augmented datasets. The study determined that apart from the low volume of training data, the features extracted by the CNN were not sufficient to predict the word being spoken. Furthermore, performing data augmentation did not significantly increase the model's accuracy. Training using the height of the lip's opening as a feature on the GRU model produced the best results-achieving a test accuracy of 17% and a validation accuracy of 26%.

关键词： Lip reading Neural networks Filipino words Lip localization Silent speech

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