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A unified workflow strategy for analysing large-scale TripAdvisor reviews with BOW model

International Journal of Business Intelligence and Data Mining 2022年第1期21卷 102-117页

作者： Bektaş, Jale Elsadig, Arwa School of Applied Technology and Management Computer Technology and Information Systems Mersin University Mersin33730 Turkey

Nowadays, firms need to transform customer online reviews data properly into information to achieve goals such as having a competitive edge and improving the quality of service. This paper presents a unified workflow to solve the problems of analysing large-scale data with 710,450 reviews for 1,134 hotels by using text mining methods among the different touristic regions of Turkey. Firstly, a star schema dimensional data mart is built that includes one fact table and two dimensional tables. Then, a series of text mining processes which includes data cleaning, tokenisation, and analysis are applied. Text mining is implemented through standard BOW and the extended BON model. The results show significant findings through this workflow. We propose to build a dimensional model dataset before performing any text mining process, since building such a dataset will optimise the data retrieval process and help to represent the data along with different measures of interest. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Data mining

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Modelling and simulation of energy management of power-split hybrid electric vehicles using the discrete EVent system specification

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International Journal of Vehicle Systems Modelling and Testing 2024年第1期18卷 33-61页

作者： Maatoug, Abdelfettah Kharoubi, Sahraoui Maaskri, Moustafa Belalem, Ghalem Mahmoudi, Said Faculty of Applied Sciences Science and Technology Department University of Tiaret Tiaret14000 Algeria Faculty of Mathematics and Computer Science Computer Science Department University of Tiaret Tiaret14000 Algeria Faculty of Applied Sciences Electrical Engineering Department University of Tiaret Tiaret14000 Algeria Faculty of Exact and Applied Sciences Computer Science Department University of Oran 1 Ahmed Ben Bella Oran31000 Algeria Faculty of Engineering Computer Science Department University of Mons Mons7000 Belgium

This work provides a basis for studying energy management optimisation in power-split hybrid electric vehicles (PSHEVs) to reduce fuel consumption and increase powertrain efficiency by enforcing a strategy related to battery level and vehicle speed. Three modes were used to operate the vehicle to meet different situations related to available energy and desired speed. These are particularly abundance mode, ascetic mode and optimal mode. For this, a system was designed based on several sub-models written in the discrete EVent system (DEVS) specification. The vehicle's power train and control strategy models were created in the DEVSimPy environment, and a joint simulation was carried out from the composite sub models until the overall model is reached. Simulation results under several driving cycles showed that the proposed system improves the energy consumption of the vehicle in different operating modes of the engines and gives more opportunities to adopt electric motors in vehicle transmission. © 2024 Inderscience Enterprises Ltd.

关键词： Energy management

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Degree reduction of ball Said-Ball surfaces using an enhanced coot optimization algorithm

Degree reduction of ball Said-Ball surfaces using an enhance...

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作者： Hu, Gang Dou, Wanting Du, Bo Department of Applied Mathematics Xi'an University of Technology Xi'an China School of Computer Science and Engineering Xi'an University of Technology Xi'an China

Degree reduction of ball Said-Ball (BSB) surface is a complex and unsolved problem in computer-aided design (CAD) and computer graphics (CG), which has potential application prospects in many engineering fields of geometric modeling. In this paper, the degree reduction of BSB surface is transformed into the optimization problems of center surface and radius function, and the improved coot optimization algorithm is used to solve it. An effective method to solve the degree reduction of BSB surface by intelligent optimization algorithm is proposed. Firstly, the degree reduction of BSB surface is decomposed into center surface reduction and radius function reduction, and the objective function and constraint conditions are determined according to the optimization idea, then the degree reduction optimization model of center surface and radius function is established. Secondly, an enhanced coot (ECOOT for short) algorithm is proposed, which combines chain strategy, dispersed foraging strategy, and quadratic interpolation strategy. Compared with the original algorithm, ECOOT algorithm has better convergence performance and higher calculation accuracy. Finally, the proposed ECOOT algorithm is used to solve the degree reduction optimization model, and the optimal center surface and minimum distance radius function of the BSB surface after degree reduction are obtained. The experimental results show that the ECOOT algorithm can effectively solve the problem of degree reduction for BSB surface, the degree reduction error is reduced by about 20% compared to the original COOT algorithm, and is superior to other intelligent optimization algorithms in accuracy, robustness, and convergence. © 2023 John Wiley & Sons, Ltd.

关键词： Optimization

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A Secure Framework for WSN-IoT Using Deep Learning for Enhanced Intrusion Detection

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computers, Materials & Continua 2024年第10期81卷 471-501页

作者： Chandraumakantham Om Kumar Sudhakaran Gajendran Suguna Marappan Mohammed Zakariah Abdulaziz S.Almazyad School of Computer Science&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India School of Electronics&Engineering Vellore Institute of TechnologyChennai CampusChennai600127India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

The security of the wireless sensor network-Internet of Things(WSN-IoT)network is more challenging due to its randomness and self-organized *** detection is one of the key methodologies utilized to ensure the security of the *** intrusion detection mechanisms have issues such as higher misclassification rates,increased model complexity,insignificant feature extraction,increased training time,increased run time complexity,computation overhead,failure to identify new attacks,increased energy consumption,and a variety of other factors that limit the performance of the intrusion system *** this research a security framework for WSN-IoT,through a deep learning technique is introduced using Modified Fuzzy-Adaptive DenseNet(MF_AdaDenseNet)and is benchmarked with datasets like NSL-KDD,UNSWNB15,CIDDS-001,Edge IIoT,Bot *** this,the optimal feature selection using Capturing Dingo Optimization(CDO)is devised to acquire relevant features by removing redundant *** proposed MF_AdaDenseNet intrusion detection model offers significant benefits by utilizing optimal feature selection with the CDO *** results in enhanced Detection Capacity with minimal computation complexity,as well as a reduction in False Alarm Rate(FAR)due to the consideration of classification error in the fitness *** a result,the combined CDO-based feature selection and MF_AdaDenseNet intrusion detection mechanism outperform other state-of-the-art techniques,achieving maximal Detection Capacity,precision,recall,and F-Measure of 99.46%,99.54%,99.91%,and 99.68%,respectively,along with minimal FAR and Mean Absolute Error(MAE)of 0.9%and 0.11.

关键词： Deep learning intrusion detection fuzzy rules feature selection false alarm rate accuracy wireless sensor networks

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Reconfigurable Interactive Environments for the Future of Work 23

Reconfigurable Interactive Environments for the Future of Wo...

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2nd International Conference on Mobile and Ubiquitous Multimedia, Proceedings of MUM 2023

作者： Dominiak, Julia Lodz University of Technology Institute of Applied Computer Science Lodz Poland

ISBN: (纸本)9798400709210

Inevitable technological progress changes the very nature of work. On the one hand, more and more processes are subject to automation, and cooperating with a robotic workforce becomes an increasing challenge. On the other hand, society is increasingly embracing diversity, and there is an emerging social need to build diverse and inclusive spaces for work. My research attempts to answer these challenges by designing, implementing, and evaluating a novel type of work environment: an Interactive Reconfigurable Environment (IRE) IREs will leverage artificial intelligence techniques to dynamically adapt the physical surfaces around a user to provide an efficient, usable and inclusive interface. An adaptive work environment will support concentration and productivity while ensuring work safety in mixed environments - industrial, semi-automatic, automatic, and, above all, in situations of cooperation between people and autonomous agents. © 2023 ACM.

关键词： Autonomous agents

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The Improved Fully Convolutional Network applied in Segmentation and Detection for Pavement Crack 23

The Improved Fully Convolutional Network applied in Segmenta...

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2023 7th International Conference on Deep Learning Technologies, ICDLT 2023

作者： Yang, Qian Wu, Wenlong Yang, Yuezhi Wu, Wenchen Su, Zhihao Computer Science College Guangzhou College of Applied Science and Technology China

ISBN: (纸本)9798400707520

Cracks are a typical disease form of airport pavement and highway pavement, mainly caused by heavy traffic load, complex external environment, and performance decay of road infrastructure. Pavement crack recognition technology is transitioning from traditional manual detection to more efficient automatic detection. In this process, semantic segmentation technology is an essential technical means and breakthrough direction to improve recognition accuracy. This paper designs an improved fully convolutional network, which is based on the traditional fully convolutional network model, but the model adopts a feature fusion strategy in downsampling to reduce the loss of details;a multi-scale fusion strategy is used in upsampling to obtain rich information under different receptive fields;Part of the ordinary convolution is replaced by dilated convolution to increase the receptive field to prevent the loss of edge details. We conducted experiments on the CrackLS315 and CrackTree260 datasets. Under the indicators of Precision, it is 8.8% and 9.2% higher than the original fully convolutional network algorithm on the CrackLS315 and CrackTree260 datasets, and it is also better than other classic algorithms. © 2023 ACM.

关键词： Convolution

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Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

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computers, Materials & Continua 2024年第6期79卷 3585-3629页

作者： Imtiaz Ahmad Tariq Hussain Babar Shah Altaf Hussain Iqtidar Ali Farman Ali Institute of Computer Sciences and Information Technology The University of AgriculturePeshawarPakistan School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhouChina College of Technological Innovation Zayed UniversityDubaiUnited Arab Emirates School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Department of Applied AI School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063South Korea

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and *** of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering *** have several challenges,including throughput,energy usage,and network lifetime *** strategies have been applied to get over these *** may,therefore,be thought of as the best way to solve such ***,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy *** paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)*** the help of APSO in the implementation of the WSN,the main methodology of this article has taken *** simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 *** sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to *** number of dead nodes likewise dropped for the given combination,falling between 71 and *** network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based *** to underwater,WSN can make use of the proposed *** overall results have been evaluated and

关键词： Wireless sensor network cluster head selection low energy adaptive clustering hierarchy accelerated particle swarm optimization

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Transfer learning in application to semantic segmentation of 3D indoor scenes scanned with a depth camera 27

Transfer learning in application to semantic segmentation of...

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27th IEEE Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2024

作者： Rosiak, Ewelina Klepaczko, Artur Lodz University of Technology Institute of Applied Computer Science Lodz Poland Lodz University of Technology Institute of Electronics Lodz Poland

ISBN: (纸本)9788362065486

Recent achievements in the domain of computational intelligence and robotics trigger development and automation in various industry branches. One such domain is the architecture and construction, where the challenges related to efficient measurement, progress monitoring, maintenance, or verification of the agreement between a design and a built object, remain unsolved and may benefit from modern image and signal processing technology. Recognition of objects such as, e.g., walls, doors, or furniture in 3D scenes, allows the creation of so-called digital twins of the real-world environment. Within this study, we developed a series of deep learning models dedicated to semantic segmentation of point clouds representing interiors of buildings. Such representations are usually collected with expensive high-precision LiDAR scanners. The available neural network models, published in the literature, were, accordingly, trained using either such laser data or, alternatively, by the help of the synthetic ones. Our motivation was thus to enable semantic segmentation of RGBD point clouds acquired with a much cheaper sensor, i.e. a depth camera. In the absence of open real datasets, we propose to perform transfer learning and show that it is feasible to achieve reasonable segmentation accuracy on depth-camera data using a model pretrained on LiDAR scans. © 2024 Division of Signal Processing and Electronic Systems, Poznan University of technology (DSPES PUT).

关键词： Semantic Segmentation

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Enhanced Federated Learning Framework based on Deep Learning and Neutrosophic Set for Android Malware Classification

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Neutrosophic Sets and Systems 2025年 82卷 781-799页

作者： Abdellah, Mohamed Refaat Oudah, Hasan H. Badawy, Ahmed Mohamed Ahmed Mohamed, Abdelfattah Abdelfattah M. Bedier, Shady Ahmed Metwaly, Ahmed A. Eassa, Mohamed Abdelhafeez, Ahmed The Department of Computer Science College of Information Technology Misr University for Science and Technology Cairo Egypt College of Arts Department of Journalism Ahl Al Bayt University Karbala Iraq Cybersecurity Department Faculty of Computers and Artificial Intelligence Helwan University Egypt Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Applied Science Research Center Applied Science Private University Amman Jordan

Malware detection is one of the critical tasks of cybersecurity, especially considering the growing popularity of mobile devices. The integrity and security of mobile ecosystems rely on the capacity to identify malware quickly and precisely, as security hacks against these devices become increasingly frequent. Due to the requirement for massive volumes of data aggregation, traditional centralized machine learning (ML) techniques for malware detection face challenges with data sharing, computational complexity, and privacy. This research addresses these challenges by proposing a novel model, called "CNN-Fed", which is based on Convolutional Neural Networks (CNN) with Federated Learning (Fed). The main goal of this work is to create a global classifier for Android malware detection that is highly accurate and does not require centralised data aggregation. Using four benchmark datasets—Drebin, Malgenome, Kronodroid, and Tuandromd—the CNN-Fed model is trained across many clients in a federated setting under the suggested architecture. Then we use multi-criteria decision-making (MCDM) methodology to evaluate the number of clients and number of rounds. We use two MCDM methods such as Entropy methodology to compute the criteria weights and the CoCoSo methodology to rank the alternatives. We use the interval-valued neutrosophic sets (IVNSs) to deal with uncertainty and vague information. We use five criteria such as accuracy, F1-score, precision, recall, and FP and six of alternatives refer to the number of clients and number of rounds. The results show the best alternative has a larger number of clients and rounds. © 2025, University of New Mexico. All rights reserved.

关键词： Android malware

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Developing Smart Skin: A Force Sensor-Based Touch and Pressure Detector 12

Developing Smart Skin: A Force Sensor-Based Touch and Pressu...

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12th International Conference on Information and Communication technology, ICoICT 2024

作者： Zain, Muhammad Aulia Rifqi Mutiara, Giva Andriana Periyadi Alfarisi, Muhammad Rizqy Sitorus, Angelica Adhira, Raka Duta Applied Science School Computer Technology Telkom University Bandung Indonesia Applied Science School Multimedia Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350363432

This research discusses the advancements in developing smart skin by employing Force Sensitive Resistor (FSR). The objective of this research is to advance research on the fundamental concept of artificial skin that relies on the human sense of touch sensors especially in pressure to imitate the sensory capabilities of human skin, since humans are incapable of accurately quantifying the pain induced by external pressure. Therefore, by employing FSR sensors, the acquired data will be stabilized, thereby serving as a dependable reference. A prototyping method is implemented in this research to construct and observe the smart skin design. This research is utilized RP-S40-ST and FS7548 sensors for gathering data pressure and touch. The smart skin is coated with an elastomeric substance that closely resembles human skin in terms of its flexibility, softness, and mechanical properties. The FSR sensor is linked to a microcontroller board called TTGO LoRa OLED, which enables the integration of data transfer for further development through the Long Range (LoRa) protocol. The AMF-500 force gauge functions as a standard for converting data obtained from analog sensors into precise force (N) and pressure measurements (kPa). This research represents a significant step towards developing smart skin using FSR sensors that aim to mimic human touch by detecting external pressure up to a maximum of under 60 Newtons and a maximum bend of under 90 degrees. © 2024 IEEE.

关键词： Contact sensors

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