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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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Cross-cultural contextualisation for recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

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Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained Optimization and engineering Design Problems

Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Kathiroli, Panimalar Kanmani, S. Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu Chengalpattu District603203 India Department of Information Technology Puducherry Technological University Puducherry India

ISBN: (纸本)9789819746491

This paper proposes a new hybrid optimization algorithm that combines the grey wolf optimization (GWO) algorithm with rat swarm optimizer (RSO) to improve the exploration and exploitation capabilities of the search space. The proposed algorithm, named GRO, is evaluated using benchmark functions and engineering design problems. The GRO algorithm leverages the social hierarchy and hunting strategies of wolf packs and incorporates the swarm intelligence of rats and the diversity-enhancing effects of GWO to guide the search process. Experimental results demonstrate that the GRO algorithm outperforms existing state-of-the-art optimization algorithms in terms of convergence speed, accuracy, and robustness for most benchmark functions. The hybrid algorithm’s ability to balance exploration and exploitation is shown to be superior to other metaheuristic algorithms. The algorithm is further validated by solving two well-known engineering design problems, where it demonstrates its efficacy in solving practical constrained problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Constrained optimization

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Fundamental Limits of Distributed Covariance Matrix Estimation Under Communication Constraints 41

Fundamental Limits of Distributed Covariance Matrix Estimati...

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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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GENSIMO - A Generic Framework for Modelling Social Insurance systems 25th

GENSIMO - A Generic Framework for Modelling Social Insuranc...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： de Haan, Fjalar J. Thompson, Jason School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Parkville Australia Melbourne Centre for Data Science The University of Melbourne Parkville Australia Department of Psychiatry Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Parkville Australia

ISBN: (纸本)9783031880162

We report on the design, development and prototype testing of gensimo (GENeric Social Insurance MOdeling), a free and open-source software framework for modelling and simulation of social insurance systems. We discuss the conceptual and software design considerations of the framework in general before demonstrating how it works in detail by setting it up to model a particular social insurance scheme. We then present how we used this prototype set up to model the impact of a policy intervention on the workload of the insurer. While gensimo is under active development and we are a while away from serious verification and validation, this prototype testing shows realistic results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Software testing

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Kathakali Mudras: An AI-Enhanced Predictive Approach 11

Kathakali Mudras: An AI-Enhanced Predictive Approach

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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Transformer Embeddings for the Implementation of a Semantic MOOC Recommender System 6

Transformer Embeddings for the Implementation of a Semantic ...

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6th International Conference on Intelligent Computing in data Sciences, ICDS 2024

作者： Assami, Sara Daoudi, Najima Ajhoun, Rachida Ecole Nationale Supérieure d'Informatique et d'Analyse Des Systèmes of Mohammed v University Department of Computer Science Rabat Morocco Ecole des Sciences de l'Information Department of Data and Knowledge Engineering Rabat Morocco

ISBN: (纸本)9798350351200

Semantic web technologies aim to interconnect objects using descriptors that identify their characteristics and improve information retrieval. In Massive Open Online Courses (MOOCs), the learning contents semantic links and their pertinence for learner needs are essential to motivate learners and reduce dropout rates. This work aims to develop a semantic recommender system prototype that uses external data sources on learners to identify their knowledge, skills, and interests to recommend MOOCs. First, this paper traces the knowledge-based recommendation of MOOCs and its data representation in the literature. Afterward, it explores a dataset of a MOOC platform to extract MOOCs content-related data, and a dataset of a professional social media to extract knowledge-based characteristics of a learner from his/her social profile. For the implementation of the semantic recommender, three different techniques: TF-IDF, GloVe, and USE models are used to compute the semantic similarity of learner needs and MOOCs content. The semantic MOOC recommender system implementation showed the advantages of using different data sources and the increased performance of a MOOC recommender system based on transformer models like the USE model. © 2024 IEEE.

关键词： Contrastive Learning

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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Computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure information extraction Heuristics Cell function analysis Table structure analysis

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