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Performance Evaluation of FC-RDVM and LDIWM Router Placement Methods for WMNs by WMN-PSODGA Hybrid Simulation system Considering Load Balancing and Chi-Square Distribution of Mesh Clients 1

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Barolli, Admir Sakamoto, Shinji Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Fukuoka Higashi-Ku811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University 3-7-2 Kajino-machi Koganei-shi Tokyo184-8584 Japan

ISBN: (数字)9783031284519

ISBN: (纸本)9783031284502

In this paper, we present WMN-PSODGA hybrid simulation system for optimization of mesh routers in Wireless Mesh Networks (WMNs). We consider Chi-square distribution of mesh clients and compare the results of a Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) with Linearly Decreacing Inertia Weight Method (LDIWM). The simulation results show that FC-RDVM has better load balancing than LDIWM. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MESH networking

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Online Media as a Price Monitor: Text Analysis using Text Extraction Technique and Jaro-Winkler Similarity Algorithm

Online Media as a Price Monitor: Text Analysis using Text Ex...

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2020 International Conference on Emerging technology in Computing, Communication and Electronics, ETCCE 2020

作者： Nurcahyawati, Vivine Mustaffa, Zuriani Faculty Technology and Informatics Universitas Dinamika Department of Information System Surabaya Indonesia Science Faculty Computing Universiti Malaysia Pahang Department of Computer Pahang Malaysia

ISBN: (纸本)9781665419628

Online media has become an essential part of everyday life in modern society. Everyone or organization is free to share their opinions and feelings about any topic on it, including information or news about commodity price fluctuations. Commodity price data from the National Strategic Price Information Center (NSPIC) website is not real-time, so it is not sufficient as a basis for monitoring commodity price fluctuations. Meanwhile, the government needs to collect data and infor-mation quickly about these price fluctuations, hence immediately strategic decisions and policies can be made to stabilize the prices. This study explores the potential function of online media by extracting the text in it and analyzing text so that it can display the commodity price data sought. The commodities used as search keywords were com-modities that had the highest consumption level in 2016 in Indonesia. The texts analyzed were taken from three online media, namely Twit-ter, ***, and ***. It was analyzed using text extraction techniques and the application of the Jaro-Winkler algorithm to find commodity prices in the text collection. Then compare the results of text analysis with commodity prices from the NSPIC website. The experimental data were 99,007 with a data collection time of three months. From only 122 data that match the keywords, it consists of 100 training data and 22 testing data. The results of the text analysis show that the text from the *** website shows the commodity prices closest to the price data from the NSPIC, while Twitter shows the farthest results. The accuracy test with the confusion matrix is 75%. Based on this research, online media texts are a viable source for moni-toring commodity price fluctuations. © 2020 IEEE.

关键词： Text processing

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

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Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA), International Conference on

作者： Bandi Swathi Madapuri Rudra Kumar Mohammad Riyaz Belgaum Zainab Alansari Computer Science and Engineering G. Pulla Reddy Engineering College Kurnool India Computer Science and Engineering G Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Information Technology University of Technology and Applied Science (UTAS) Muscat Oman

ISBN: (纸本)9798350398021

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies’ safety and security. Speech is a uniquely human trait used to express and communicate one’s point of view to others. Emotion recognition from speech audio signals is obtaining a presenter’s emotions from the presenter’s audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach’s performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers’ performance in making accurate detections with few false positives.

关键词： Training Emotion recognition Sensitivity Hidden Markov models Speech recognition Benchmark testing Feature extraction

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Subspace-Contrastive Multi-View Clustering

arXiv

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arXiv 2022年

作者： Fu, Lele Zhang, Lei Yang, Jinghua Chen, Chuan Zhang, Chuanfu Zheng, Zibin The School of System Sciences and Engineering Sun Yat-Sen University Guangzhou China The School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Faculty of Information Technology Macau University of Science and Technology China

Most multi-view clustering methods are limited by shallow models without sound nonlinear information perception capability, or fail to effectively exploit complementary information hidden in different views. To tackle these issues, we propose a novel Subspace-Contrastive Multi-View Clustering (SCMC) approach. Specifically, SCMC utilizes view-specific auto-encoders to map the original multi-view data into compact features perceiving its nonlinear structures. Considering the large semantic gap of data from different modalities, we employ subspace learning to unify the multi-view data into a joint semantic space, namely the embedded compact features are passed through multiple self-expression layers to learn the subspace representations, respectively. In order to enhance the discriminability and efficiently excavate the complementarity of various subspace representations, we use the contrastive strategy to maximize the similarity between positive pairs while differentiate negative pairs. Thus, a weighted fusion scheme is developed to initially learn a consistent affinity matrix. Furthermore, we employ the graph regularization to encode the local geometric structure within varying subspaces for further fine-tuning the appropriate affinities between instances. To demonstrate the effectiveness of the proposed model, we conduct a large number of comparative experiments on eight challenge datasets, the experimental results show that SCMC outperforms existing shallow and deep multiview clustering methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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A new approach for solving global optimization and engineering problems based on modified Sea Horse Optimizer

arXiv

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arXiv 2024年

作者： Hashim, Fatma A. Mostafa, Reham R. Khurma, Ruba Abu Qaddoura, Raneem Castillo, P.A. Faculty of Engineering Helwan University Egypt University of Sharjah Sharjah27272 United Arab Emirates Department of Information System Faculty of Computers and Information Sciences Mansoura University Mansoura35516 Egypt MEU Research Unit Faculty of Information Technology Middle East University Amman11831 Jordan School of Computing and Informatics Al Hussein Technical University Amman Jordan Department of Computer Engineering Automatics and Robotics University of Granada Granada Spain

Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of sea horses, SHO integrates the logarithmic helical equation and Levy flight, effectively incorporating both random movements with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more comprehensive exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named mSHO. The enhancement primarily focuses on bolstering SHO’s exploitation capabilities by replacing its original method with an innovative local search strategy encompassing three distinct steps: a neighborhood-based local search, a global non-neighbor-based search, and a method involving circumnavigation of the existing search region. These techniques improve mSHO algorithm’s search capabilities, allowing it to navigate the search space and converge toward optimal solutions efficiently. To evaluate the efficacy of the mSHO algorithm, comprehensive assessments are conducted across both the CEC2020 benchmark functions and nine distinct engineering problems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum and Friedman’s tests, are aptly applied to discern noteworthy differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navigating complex search spaces. The comprehensive results distinctly establish the supremacy and effic

关键词： Global optimization

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Neural Topic Modeling with Deep Mutual Information Estimation

arXiv

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arXiv 2022年

作者： Xu, Kang Lu, Xiaoqiu Li, Yuan-Fang Wu, Tongtong Qi, Guilin Ye, Ning Wang, Dong Zhou, Zheng School of Computer Science Nanjing University of Posts and Telecommunications China School of Computer Science and Engineering Southeast University China Faculty of Information Technology Monash University Australia Department of Earth System Science Tsinghua University Beijing100084 China

The emerging neural topic models make topic modeling more easily adaptable and extendable in unsupervised text mining. However, the existing neural topic models is difficult to retain representative information of the documents within the learnt topic representation. In this paper, we propose a neural topic model which incorporates deep mutual information estimation, i.e., Neural Topic Modeling with Deep Mutual Information Estimation(NTM-DMIE). NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation. To learn robust topic representation, we incorporate the discriminator to discriminate negative examples and positive examples via adversarial learning. Moreover, we use both global and local mutual information to preserve the rich information of the input documents in the topic representation. We evaluate NTM-DMIE on several metrics, including accuracy of text clustering, with topic representation, topic uniqueness and topic coherence. Compared to the existing methods, the experimental results show that NTM-DMIE can outperform in all the metrics on the four datasets. © 2022, CC BY.

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Electrification of Transportation: Policy Framework, Technical Aspects and Challenges in Pakistan - A Case Study

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Masood, Arsalan Hassan, Syed Zulqadar Kamal, Tariq Khan, Salman Rizvi, Syed Asad Abbas Salman, Salman Department of Electrical Engineering Faculty of Engineering and Architecture University of Sialkot Pakistan Department of Computer Science Faculty of Information Technology and Computer Science University of Central Pubjab Lahore Pakistan School of Technology and Innovations Electrical Engineering University of Vaasa Vaasa Finland Department of Electrical Power Engineering and Mechatronics Tallinn University of Technology Estonia State Key Lab. for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University) Beijing China Department of Electrical Engineering Balochistan University of Engineering and Technology Balochistan Khuzdar Pakistan

Pakistan faces severe air pollution and high dependency on imported fossil fuels for transportation, urgently needing solutions. Electrification of transportation could address these issues, but Pakistan lacks a clear roadmap for adopting electric vehicles. This paper examines the electrification of transportation in Pakistan, focusing on policies, technical aspects, and standards. It looks at best practices from developed countries and discusses incentives offered by the Pakistani government to boost electric vehicle adoption. The study also explores challenges and opportunities in adopting electric vehicles within existing power systems. A SWOT analysis is included, highlighting strengths, weaknesses, opportunities, and threats related to electric vehicle policies and market integration in Pakistan. By reviewing these factors, the paper provides insights into the obstacles and potential benefits of electric vehicle adoption. It also suggests ways for developing nations like Pakistan to encourage widespread use of electric vehicles, aiming to reduce fossil fuel dependency and meet environmental goals. © 2024

关键词： Electric transportation Electric vehicles Key challenges Policy SWOT analysis

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An Enhanced Method for Detecting Attack in Collaborative Recommender system

An Enhanced Method for Detecting Attack in Collaborative Rec...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Reda A. Zayed Hesham A. Hefny Lamiaa F. Ibrahim Hesham A. Salman Computer Scinces Dep. Faculty of Graduate Studies for Statistical Research Cairo University Cairo Egypt Dean of College of Informatics Everyone’s Smart University Management Information system Dep. Higher Institute for Computers & Information Technology ElShorouk Cairo Egypt

In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. The system suggests and recommends articles to the user that may interest the user in online advertising (recommending and suggesting appropriate content to the user that matches the user’s tastes and activities). Recommendation systems have become an integral part of our daily online journeys. The quality of predictions is degraded by the attackers by injection of fake profiles. therefore, the shilling attacks detection are necessary. thus, various shilling attacks detection techniques proposed. In this paper, we introduce an enhanced technique for detecting shilling attacks in collaborative recommender system using supervised learning techniques. The proposed method results show that getting better accuracy when we employee ensemble learning algorithm.

关键词： Technological innovation Video on demand Web services Smart cities Supervised learning Collaboration Information services

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Gradient Boosting and Deep Learning Models Approach to Forecasting Promotions Efficiency in FMCG Retail 19th

Gradient Boosting and Deep Learning Models Approach to Forec...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Henzel, Joanna Sikora, Marek Department of Computer Networks and System Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030615338

In the paper, different approaches to the problem of forecasting promotion efficiency are presented. For four defined indicators of promotion effect, prediction models using Gradient Boosting method and Deep Learning methods were trained. The comparison of the results is provided. The experiments were performed for three groups of products from a large grocery company. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks

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computers, Materials & Continua 2020年第9期64卷 2043-2055页

作者： Congcong Wang Pengyu Liu Kebin Jia Xiaowei Jia Yaoyao Li Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of TechnologyBeijing100124China Department of Computer Science and Engineering University of MinnesotaTwin CitiesUSA

Weather phenomenon recognition plays an important role in the field of ***,weather radars and weathers sensor have been widely used for weather ***,given the high cost in deploying and maintaining the devices,it is difficult to apply them to intensive weather phenomenon ***,advanced machine learning models such as Convolutional Neural Networks(CNNs)have shown a lot of promise in meteorology,but these models also require intensive computation and large memory,which make it difficult to use them in *** practice,lightweight models are often used to solve such ***,lightweight models often result in significant performance *** this end,after taking a deep dive into a large number of lightweight models and summarizing their shortcomings,we propose a novel lightweight CNNs model which is constructed based on new building *** experimental results show that the model proposed in this paper has comparable performance with the mainstream non-lightweight model while also saving 25 times of memory *** memory reduction is even better than that of existing lightweight models.

关键词： Deep learning convolution neural networks lightweight models weather identification

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