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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Eckertz, Nina Deborah, Hilda Hardeberg, Jon Y. Sandu, Irina Crina Anca Norwegian University of Science & Technology Department of Computer Science Gjovik Norway Conservation Section MUNCH Museum Oslo Norway

ISBN: (纸本)9798331513139

This article presents an algorithm to analyse and map the distribution of pigments on areas of interest on a painting surface using hyperspectral imaging (HSI) and image segmentation. Whereas previous investigations of pigments were typically on a pixel-by-pixel basis, this work presents a spatially constrained analysis. By combining independent component analysis and watershed segmentation, we provide an approach to first identify regions within the paint layer prior to pigment classification. The pigment classification itself is compared to an existing spectral library of pure pigments, providing a first analysis of the pigments' properties. The results of this investigation can be used to map the distribution of pigments, to be used in further examinations of their properties by experts in the cultural heritage field. © 2024 IEEE.

关键词： Pixels

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Data Replication Methods in Cloud, Fog, and Edge Computing: A Systematic Literature Review

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Wireless Personal Communications 2024年第1期135卷 531-561页

作者： Karamimirazizi, Fatemeh Jameii, Seyed Mahdi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Nowadays, a large amount of data is being generated and these data are usually stored in distributed environments such as cloud, fog, and edge environments. Data replication, which is commonly used to manage large amounts of data, improves overall accessibility and availability. Due to the importance of data replication, a lot of research has been done on replication methods in these environments. In this paper, a systematic review has been conducted to analyze the existing studies on data replication methods in cloud, fog, edge, and edge-cloud environments. The advantages, disadvantages, and some of the main challenges of the existing methods are explained and a taxonomy of data replication methods is provided. The selected papers are analyzed based on various criteria related to data replication, such as recovery, storage cost, load balancing, replica consistency, security, mobility, and data migration. Finally, the open issues and future research directions are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fog

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Analysis of mini program user usage market based on decision tree classification and decision tree regression algorithm 4

Analysis of mini program user usage market based on decision...

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4th International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2024

作者： Liu, Lidong Huang, Rui Li, Xiaoqin Luo, Yongbin Zhang, Rong Department of Computer Science Guangdong University of Science and Technology No. 2 Songshan Lake Section Dongguan523083 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

The decision tree classification algorithm identifies category patterns in data by constructing a tree like structure, while the decision tree regression algorithm predicts the trend of continuous numerical changes based on the tree model. This article uses decision tree classification and decision tree regression algorithms to analyze the market for mini program users. Through classification algorithms, we have gained a profound insight into the activity classification characteristics of mini program users at different time periods. These analyses not only reveal the evolution trajectory of user behavior patterns, but also provide us with an advanced perspective to understand market dynamics. At the same time, regression algorithms, with their precise predictive ability, accurately depict the growth and decline of the market size used by mini programs, injecting strong momentum into predicting market trends. This comprehensive analysis method that integrates classification and regression algorithms endows mini program developers with the super ability to insight into user activity in the time dimension, thereby helping them formulate strategies more accurately and efficiently, and promoting the stable and sustainable growth of mini program user scale. © 2024 SPIE.

关键词： Random forests

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An Adaptable Algorithm for Optimizing Bus Line Distribution Using the Clustering Method

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Wireless Personal Communications 2024年第4期134卷 2347-2370页

作者： Sheikhi, Fatemeh Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The current fleet of cars requires reevaluation, upgrades, and replacement in response to the expanding human population, the increase in urban traffic volume, and the elevated levels of air pollution attributed to vehicular emissions. In densely populated cities, bus routes effectively compete with private transportation, optimize user accessibility, and provide access to all urban residents. This study proposes a novel method for optimizing bus routes, encompassing four primary processes: preprocessing, site clustering, ridership prediction, and optimization. The clustering process employs the K-means technique to classify available bus stops based on their geographical information. The suggested method utilizes an artificial neural network model to forecast the number of passengers at different locations. Subsequently, the bee colony optimization algorithm is implemented to determine bus frequencies and achieve an optimal distribution of buses across various traffic lines. Results obtained using a real traffic line dataset indicate a 32% increase in traffic fleet capacity and a 22.33% reduction in passenger waiting time upon application of the proposed method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Personality-based and trust-aware products recommendation in social networks

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Applied Intelligence 2023年第1期53卷 879-903页

作者： Vatani, Nasim Rahmani, Amir Masoud Javadi, Hamid Haj Seyyed Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science Shahed University Tehran Iran

In recent years, with the development of technology, the shopping approach of people has moved towards pervasive online social shopping. As a result, how to create a recommendation algorithm that offers products based on the personal and different needs and tastes of people on social networks is a significant research issue. This article proposes a personality-based and trust-aware probabilistic product recommendation algorithm in social networks. We present a dynamic method for determining how similar people in social networks are. For this purpose, we consider the personality-based features of recommendation attributes of products in social networks. Then, the level of trust of products and types of correlations among the products is considered to create a probabilistic matrix of product recommendation. Moreover, for solving the cold start problem of products, we consider qualitative aspects of products while exploiting personality-based user behavior regarding their purchases. At last, the empirical experiments are conducted to analyze the impact of the algorithm’s different influence factors using the Amazon dataset. Moreover, the results of comprehensive experiments adopted to verify the proposed personalized recommendation algorithm’s effectiveness show that the proposed algorithm has the appropriate effectiveness and the higher accuracy. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Behavioral research

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A Comparative Study on Deep Learning Models for Stock Price Prediction 5

A Comparative Study on Deep Learning Models for Stock Price ...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： He, Zhexin Zhang, Huan Por, Lip Yee Chongqing College of Architecture and Technology Chongqing401331 China Chongqing Academy of Science and Technology Chongqing401123 China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9781643685243

With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnesses these deep learning models, including LSTM, MLP, CNN, and GRU, to investigate the nonlinear forecasting of stock market price indices. The experiment selects globally representative stock indices and, through analyzing short-term, mid-term, and long-term data, discovers that the GRU model demonstrates exceptional performance in both prediction accuracy and generalization capabilities. To further enhance precision, the research constructs a filtering ensemble decision-making system based on multiple GRU models. This strategy reduces the error by at least 0.3% in medium to long-term predictions, significantly outperforming a single standalone GRU model. Consequently, the GRU-filtered ensemble decision model has been empirically proven as an effective tool for predicting stock indices, not only broadening the methods available for stock market prediction but also deepening the understanding and application of the generalization abilities of deep learning models within financial markets. © 2024 The Authors.

关键词： Long short-term memory

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RP-SWSGD: Design of sliding window stochastic gradient descent method with user’s ratings pattern for recommender systems

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Multimedia Tools and Applications 2024年第14期83卷 41083-41120页

作者： Khan, Zeshan Aslam Raja, Hafiz Anis Chaudhary, Naveed Ishtiaq Iqbal, Sumbal Mehmood, Khizer Raja, Muhammad Asif Zahoor Department of Electrical and Computer Engineering International Islamic University Islamabad Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

To offer relevant and useful recommendations, the crucial role of recommender systems in e-commerce industry is to predict the users’ concern for various items by estimating items’ attributes and users’ preferences. The reliability of a recommender system is usually assessed through accuracy and speed of relevant recommendations for a variety of items. The matrix factorization-based stochastic gradient descent (SGD) methods proposed by researchers lack memory needed to capture the ratings history hidden in the previous iterations. Recently, sliding window-based SGD strategies designed for Recommender systems and Hammerstein nonlinear systems gained attention due to the improved performance in terms of convergence speed and estimated accuracy. The memory impact with regard to the historical information enhances the performance of sliding window-based SGD techniques. However, sliding window-based methods are deficient in capturing the ratings history based on the users’ rating patterns. Hence utilizing the same window length for the set of observed ratings rated by users. Therefore, we propose an improved sliding window-based SGD strategy to acquire historical information of the ratings with respect to a user’s rating patterns for efficient matrix factorization of recommender systems. The proposed strategy performs significantly by accomplishing fast convergence speed and accuracy for window sizes greater than 1. The accuracy of the suggested technique is verified for two benchmark datasets such as ML-100 K and Film-Trust. However, the authenticity of the proposed method as compared to the standard counterpart (window size = 1) is confirmed through Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The average improvements achieved by the proposed strategy in terms of RMSE and MAE over the baseline for ML-100 K dataset are 0.726% and 2.245% respectively. Whereas the proposed method accomplishes considerable average improvement of 7.89% and 9.41% for RMSE an

关键词： Recommender systems

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End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

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computers, Materials & Continua 2025年第2期82卷 3223-3249页

作者： Shafi Ullah Haidawati Muhammad Nasir Kushsairy Kadir Akbar Khan Ahsanullah Memon Shanila Azhar Ilyas Khan Muhammad Ashraf Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Computer Engineering Section Malaysian Institute of Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Electrical Engineering Section Universiti Kuala LumpurBritish Malaysian InstituteSalengor53100Malaysia Electrical Engineering Department Mehran UniversityKhairpur-Mirs76062Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.

关键词： Mutual authentication lightweight end-to-end encryption elliptic curve cryptography industrial internet of things curve25519 machine-to-machine communication

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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