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2024 Arab ICT Conference, AICTC 2024

作者： Ahmed, Saad Salman Alansari, Zainab Belgaum, Mohammad Riyaz College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman G. Pullaiah College of Engineering and Technology Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350342086

Routing attacks pose a substantial security risk in Wireless Sensor Networks (WSNs), compromising network performance and reliability. Utilizing the Cartesian product concept to identify these routing attacks in WSNs is an emerging and promising approach. This strategy involves generating a Cartesian product graph from the original network graph and subsequently extracting relevant features to detect potential threats. The success of this approach is dependent on several factors, such as feature selection, the detection algorithm employed, and the prevailing network conditions. This research presents an innovative framework that leverages the Cartesian product approach to identify routing attacks in WSNs. The performance of this framework is evaluated using the Cooja simulator, yielding results that indicate high detection accuracy, low energy usage, and minimal overhead. A comparative analysis with contemporary studies illustrates the superior ability of this framework to handle a wide range of attack scenarios and network conditions. The introduced framework significantly advances the field of routing attack detection in WSNs and provides an actionable solution for easy integration into existing WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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An associative classification algorithm for categorizing text

An associative classification algorithm for categorizing tex...

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2013 International Conference on Artificial Intelligence, ICAI 2013

作者： Aburumman, Bashar Abdel-jaber, Hussein Department of Computing Ablaqa'a Applied University Jordan Faculty of Computer Studies Department of Information Technology and Computing Arab Open University Saudi Arabia

ISBN: (纸本)1601322488

Constructing predictive, accurate and interpretable classifiers for categorizing texts is a significant task in text mining and knowledge discovery. Associative classification is a data mining research branch that applies association rule to form classification systems (classifiers). This classification approach has shown high accuracy performance in classifying unseen data if compared with other classification techniques. In this paper, we introduce a TC algorithm based on associative classification for the hard problem of TC where documents are classified based on keywords into the right categories. This algorithm employs an efficient intersection method based on DiffSet and a rule filtering procedure by considering more rules during building the classifier. Experimental results on real world textual data set called the Reuter indicated that the proposed model outperforms current rule based TC techniques.

关键词： Data mining

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Configuring the stochastic Helmholtz machine for subcortical emotional learning

Configuring the stochastic Helmholtz machine for subcortical...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Yau, Chi-Yung Burn, Kevin Wermter, Stefan Faculty of Applied Sciences Department of Computing Engineering and Technology University of Sunderland United Kingdom Institute for Knowledge Technology Department of Informatics University of Hamburg Germany

ISBN: (纸本)9781424469178

Emotional learning involves two stages. The first is to acquire reinforcers from stimuli and the second is to associate such reinforcers with emotional responses. Both stages can be found occurring in the amygdala. LeDoux's fear circuit model [1] suggests two routes, a subcortical route and a cortical route, for emotional information entering the amygdala for associative learning. It can be used to explain how the actual recognition of emotions from facial expressions can be processed in the brain. Based on the model, a neural architecture is proposed using the stochastic Helmholtz machine (SHM) with the wake-sleep algorithm. In this paper, the results of three experiments about the subcortical emotional learning are reported, where different configurations of SHMs are involved. The first two experiments are to identify a suitable way to allow behavioural responses entering the central nucleus of the amygdala for association. However, both experiments show symptoms of overfitting, where some weights and biases of neurons are observed that will unusually increase during training. Therefore, the final experiment is designed to maintain the range of weights between -1 and +1 in order to solve the overfitting problem. The last experiment shows that the neural architecture with the new weight policy holds a lot of potential for modelling subcortical learning. © 2010 IEEE.

关键词： Stochastic systems

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Numerical analysis of vehicle-to-vehicle impact using vehicle dynamics control systems for collision mitigation

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International Journal of Dynamics and Control 2013年第2期1卷 172-191页

作者： Elmarakbi, Ahmed Elkady, Mustafa MacIntyre, John Department of Computing Engineering and Technology Faculty of Applied Sciences University of Sunderland SunderlandSR6 0DD United Kingdom Mouseitbeh Beirut Lebanon Department of Automotive Engineering Faculty of Engineering Ain Shams University Cairo Egypt

This paper focuses on the use of vehicle dynamics control systems (VDCS) to mitigate vehicle collisions in case of offset frontal vehicle-to-vehicle crash scenario. A unique 6-degree-of-freedom vehicle dynamics/crash mathematical model is developed and analyzed in this research. The model is used to define the vehicle body crash parameters by integrating a vehicle dynamics model with a vehicle front-end structure model. In this model, the anti-lock braking system and the active suspension control system are co-simulated with the full car vehicle dynamics model and integrated with the front-end structure. The associated equations of motion of the model are developed and solved numerically. Four different cases of VDCS are investigated in this paper and their associated results are compared with the free rolling case scenario. It is shown from the numerical simulations that the vehicle dynamics/crash response can be captured and analyzed quickly and accurately. Furthermore, it is shown that the VDCS affect the crash characteristics positively. © 2013, Springer-Verlag Berlin Heidelberg.

关键词： Equations of motion

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Effectiveness of Machine Learning Algorithms on Battling Counterfeit Items in E-commerce Marketplaces

Effectiveness of Machine Learning Algorithms on Battling Cou...

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2023 International Research Conference on Smart computing and Systems Engineering, SCSE 2023

作者： Gunawardhana, H.M.K.T. Kumara, B.T.G.S. Rathnayake, Kapila Jayaweera, Prasad M. Faculty of Computing Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Sri Lanka Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Sri Lanka Faculty of Applied Sciences University of Sri Department of Computer Science Jayawardanapura Sri Lanka

ISBN: (纸本)9798350341454

For e-commerce marketplaces, counterfeit goods are a major issue since they endanger public safety in addition to causing customer unhappiness and revenue loss. Traditional techniques to identify fake goods in online marketplaces take too long and have a narrow reach, hence they are ineffective. Machine learning algorithms have become a potential tool for swiftly and precisely identifying counterfeit goods in recent years. The usefulness of two machine learning algorithms in identifying fake goods in online marketplaces is examined in this research. The study assesses the performance using a sizable dataset of descriptions, title, prices, and seller names from many well-known e-commerce platforms. The study's findings show that machine learning algorithms significantly affect the detection of fake goods in online marketplaces. © 2023 IEEE.

关键词： Electronic commerce

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A deep reinforcement learning approach for wind speed forecasting

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Engineering Applications of Computational Fluid Mechanics 2025年第1期19卷

作者： Band, Shahab S. Lin, Ting Jia Qasem, Sultan Noman Ameri, Rasoul Shahmirzadi, Danyal Aslam, Muhammad Shamrooz Pai, Hao-Ting Salwana, Ely Mousavi, Amir International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Department of Information Management National Yunlin University of Science and Technology Douliu Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu Taiwan Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Institute of the Information Society Ludovika University of Public Service Budapest Hungary John Von Neumann Faculty of Informatics Obuda University Budapest Hungary Department of Information and Computing Systems Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transf... 详细信息

The conventional wind forecasting methods often struggle to handle the non-stationary and inconsistent wind patterns. This paper presents a hybrid method of Empirical Wavelet Transform (EWT) and Deep Reinforcement Learning (DRL) for wind speed modeling to overcome the forecasting challenges. The EWT method transforms the original wind speed series into several independent modes and a residual series. In addition, the DRL method is utilised to optimise the weights associated with three distinct supervised deep learning models, i.e., Long Short-Term Memory (LSTM), Convolutional Neural Networks with LSTM (CNN-LSTM), and CNN with Gated Recurrent Units (CNN-GRU). The performance of the proposed EWT-DRL is evaluated against deep learning models, including LSTM, CNN-LSTM, CNN-GRU, and their coupling with EWT. The combination of EWT and the DRL (EWT-DRL) method achieves a Mean Absolute Error (MAE) of 0.151, a Mean Squared Error (MSE) of 0.060, a Root Mean Squared Error (RMSE) of 0.192, and a correlation coefficient (R) of 0.9913. These results indicate the effectiveness of EWT-DRL in improving accuracy for wind speed modeling. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： artificial intelligence deep reinforcement learning empirical wavelet transform long short-term memory machine learning Renewable energy wind speed forecasting

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Antioxidant and antimicrobial properties of Litsea elliptica Blume and Litsea resinosa Blume(Lauraceae)

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Asian Pacific Journal of Tropical Biomedicine 2014年第5期4卷 386-392页

作者： Mui-Hung Wong Li-Fang Lim Fasihuddin bin Ahmad Zaini bin Assim Faculty of Applied Sciences Universiti.Teknologi MARA.Samarahan CampusSarawak Branch Faculty of Engineering Computing and Science.Swinburne University of TechnologySarawak Campus Department of Chemistry Faculty of Resource Science and TechnologyUniversiti Malaysia Sarawak

Objective:To investigate antioxidant and antimicrobial activities of two plant species,Litsea elliptica(L elliptica) and Litsea resinosa(L resinosa).Methods:In vitro method-2,2-diphenyl-1-picrylhydrazyl radical scavenging assay was conducted for antioxidant activity detennination while antimicrobial assay consisted of agar well diffusion assay and mycelial radial growth ***:Methanol extracts of root and stem of L elliptica and L resinosa exhibited the highest antioxidant activity with EC_(50)of 23.99,41.69,11.22 and 33.48 mg/*** methanol extracts of L resinosa as well as root extracts from L elliptica showed significant scavenging *** extract from stem of L resinosa presented the largest inhibition zone in Gramnegative bacteria Pseudomonas aeruginosa and Escherichia coli while chloroform extract from inner bark of L resinosa showed major inhibition towards Gram-positive bacteria Bacillus *** oils from the root of both species showed significant antifungal activities which are 80.11%and 66.85%***:Overall,methanol extracts from root and stem of both species showed antioxidant activity comparable to standard butylated *** from L resinosa demonstrated stronger antimicrobial properties compared to that from L elliptica.

关键词： Litsea elliptica Litsea resinosa Antioxidant Antifungal Antibacterial EC_(50)

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Comparison of document similarity algorithms in extracting document keywords from an academic paper 7

Comparison of document similarity algorithms in extracting d...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Miah, M. Saef Ullah Sulaiman, Junaida Azad, Saiful Zamli, Kamal Z. Jose, Rajan Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Malaysia Pahang Center for Data Science and Artificial Intelligence Data Science Center Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Green University of Bangladesh Department of Cse Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Industrial Sciences and Technology Gambang Malaysia

ISBN: (纸本)9781665414074

The idea of this study is to validate a list of keywords derived from a scientific article by a domain expert from years of knowledge with prominent document similarity algorithms. For this study, a list of handcrafted keywords generated by Electric Double Layer Capacitor (EDLC) experts are chosen, and relevant documents to EDLC are considered for the comparison. Then, different similarity calculation algorithms were employed in different settings on the documents such as using the whole texts of the documents, selecting the positive sentences of the documents, and generating similarity score with automatically extracted keywords from the documents. The experiment's outcome provides us with findings that the machine-generated keywords are mostly similar to the curated list by the domain experts. This study also suggests the preferable algorithms for similarity calculation and automated key-phrase extraction for the EDLC domain. © 2021 IEEE.

关键词： Supercapacitor

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Sign reversal of Berry curvature triple driven by magnetic phase transition in a ferromagnetic polar metal

arXiv

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arXiv 2025年

作者： Sha, Xuyang Zhang, Xuejin Liu, Hao Cao, Jin Chen, Ruohan Zhai, Jinfeng Shao, Dingfu Wu, Shiwei Xiao, Cong Yang, Shengyuan A. He, Pan Guo, Hangwen Shen, Jian State Key Laboratory of Surface Physics Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Department of Physics Fudan University Shanghai200433 China Institute of Applied Physics and Materials Engineering Faculty of Science and Technology University of Macau China Key Laboratory of Materials Physics Institute of Solid State Physics HFIPS Chinese Academy of Sciences Hefei230031 China Fudan University Shanghai200433 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Zhangjiang Fudan International Innovation Center Fudan University Shanghai201210 China Collaborative Innovation Center of Advanced Microstructures Nanjing210093 China

Nonlinear Hall effects have been observed in quantum materials where Berry curvature and its momentum-space derivatives, such as the Berry curvature dipole (BCD) and Berry curvature triple (BCT), play a central role. While inversion symmetry breaking is widely recognized as a key criterion, the impact of time-reversal symmetry breaking remains less explored. Here, we report an abrupt enhancement of nonlinear Hall conductivity in non-centrosymmetric SrRuO3 (111) thin films during the paramagnetic-to-ferromagnetic transition. Scaling analysis reveals a sign reversal of the skew scattering contribution upon time-reversal symmetry breaking, which we attribute to the sign reversal of BCT at the Fermi surface. Density functional theory (DFT) calculations support this interpretation, showing the spin-polarized band splitting shifts the Fermi level asymmetrically for different spin channels. Our findings establish SrRuO3 (111) thin films as a promising platform for exploring magnetically tunable nonlinear transport effects. Copyright © 2025, The Authors. All rights reserved.

关键词： Ferromagnetism

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Enhancing the adaptive capability of fuzzy Petri net by an improved whale optimization algorithm

Enhancing the adaptive capability of fuzzy Petri net by an i...

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2024 China Automation Congress, CAC 2024

作者： Ye, Shao-Qiang Zain, Azlan Mohd Yusoff, Yusliza Ruan, Wen-Jie Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia School of Information Engineering Hunan Applied Technology University Changde China

ISBN: (纸本)9798350368604

Fuzzy Petri Nets (FPNs) are an advanced topological model that extends traditional Petri nets (PNs) by integrating fuzzy theory. However, the generalization capability of FPN models is often limited due to their reliance on expert-driven parameter determination. This paper proposes an improved whale optimization algorithm (IWOA) for optimizing FPN models to address this challenge. The IWOA employs an adaptive weighting strategy, optimal search techniques, population grouping, and the Levy flight strategy to dynamically adjust the search process, enhancing the WOA's effectiveness in handling complex problems and improving the balance between exploration and exploitation. The IWOA is tested on six classical benchmark functions in the experimental section and applied to the FPN model. The results demonstrate that the IWOA-FPN model exhibits strong generalization capabilities and superior convergence performance. © 2024 IEEE.

关键词： Optimization algorithms

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