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Improving bot detection of fake followers on Twitter via a hybrid B-HC optimisation algorithm 5

Improving bot detection of fake followers on Twitter via a h...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Alzeyadi, Haider Duran, Fecir Informatics Institute Department of Computer Science Gazi University Ankara Turkey Gazi University Engineering Faculty of Technology Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9781665493604

Sophisticated cyber threats are seen on Online Social Networks (OSNs) social media accounts automated to imitate human behaviours has an impactful effect on distorting public thoughts and opinions. OSNs are weaponized to diffuse deception, misinformation, and malicious activities, that forms a serious threat to society. The deceptive nature of imitating human behaviour has become a challenging and crucial task to detect automated accounts (socialbots). This research, however, proposes a hybrid metaheuristic optimisation algorithm for socialbot detection. Specifically, a hybrid B-Hill Climbing (B-HC) optimisation algorithm works in tandem with a k-NN nearest neighbour classifier to accurately select a relevant feature subset. It is applied to be tested for fake followers account on Twitter data. Experimental results showed that the proposed method is better than the traditional and the latest feature selection techniques as well as the rule-set methods. The B-HC alongside with k-NN method achieved promising results using only relevant feature subset. © 2023 IEEE.

关键词： Feature Selection

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User-Guided Anime Line Art Colorization with Spatially-adaptive Normalization 9

User-Guided Anime Line Art Colorization with Spatially-adapt...

引用

9th IEEE Smart World Congress, SWC 2023

作者： Fu, Yulu Zhong, Haowei Cui, Jinrong Liu, Hailong Huang, Cheng Wang, Jinghua South China Agricultural University College of Mathematics and Informatics Guangzhou China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

ISBN: (纸本)9798350319804

Line art plays an essential role in the process of anime creation. Colorization of the line art images is a tough task for the reason that neither grayscale values nor coloring semantic information exists in grayscale line art images. In this paper, we propose a novel line art colorization architecture with spatially-adaptive normalization for user-guide colorization. Our method can obtain high-quality colorized anime images by reserving more semantic information of inputs. Specifically, we integrate spatially-adaptive normalization block as well as spectral normalization, and the hybrid normalization enables us to train the network robustly while preserving the semantic information as much as possible. Our model is based on U-net architecture, which is regarded as a successful feature extraction architecture and is widely applied in the field of image segmentation and image generation. Also, We propose an anime data processing paradigm, combining the colored picture and the synthetic line art to generate simulated strokes in line with human intuition, which is conducive to making the generated images more realistic and perform on in-the-wild data better. With the proposed model, we evaluate a collected anime dataset and compare it with the existing method. The experimental results demonstrate the excellent and stable coloring effect of our proposed model. © 2023 IEEE.

关键词： GANs Interactive colorization Normalization User-guided colorization

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A Hybrid Localization Algorithm of RSS and TOA Based on Adaptive Weight Errors 5

A Hybrid Localization Algorithm of RSS and TOA Based on Adap...

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5th International Conference on Information technology, Applied Mathematics and Statistics, ICITAMS 2023

作者： Tariq, Suphian Mohammed Al-Mejibli, Intisar Shadeed Al-Iraqia University Computer Engineering Informatics Institute For Postgraduate Iraqi Commission For Computers & Informatics College Of Engineering Baghdad Iraq Biomedical Informatics College University Of Information Technology And Communications Baghdad Iraq

ISBN: (纸本)9798350335118

Specifying the location of wireless sensor devices is vital in many applications such as target tracking, remote environmental monitoring, and more. Time of arrival (TOA), the received signal strength indicator (RSSI) and range information are applied to locate the wireless sensor network (WSN) node's location. Still, they suffer inaccuracy because of environmental factors in the 2D region. A ranging method has been suggested, merging the TOA information domain with RSSI to accurately determine the distance between the unknown node and the anchor node. This approach relies on utilizing RSSI and TOA ranging techniques in the near and far endings. We combine received signal strength indication and arrival time in parallel style as major elements. The findings of the two methods are then combined with the error percentage to determine the final location using adaptive weight errors. The proposed range method is promoted in this work to enhance the accuracy of the positioning. The proposal has been implemented by MATLAB simulation and the results show improving positioning accuracy compared to the conventional TOA and RSSI ranging method. The mean square error in the proposed method is 0.5044, whereas it was 0.7032 and 0.8102 in RSSI, respectively. This indicates that the suggested approach is more efficient and performs better for localization than the other two methods. © 2023 IEEE.

关键词： Sensor nodes

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Transformer-Based Embeddings for Greek Language Categorization 24

Transformer-Based Embeddings for Greek Language Categorizati...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Liapis, Charalampos M. Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Patras Computer Technology Institute & Press 'Diophantus' Mathematics Dept. Patras26504 Greece University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350376647

The Greek School Network (GSN) provides support to students, teachers, and school units in secondary education across Greece. Handling numerous user queries manually can be challenging, necessitating the development of an automated system for accurate categorization of these queries. This paper presents a comparative study of various transformer-based models for multi-class text categorization of Greek language queries submitted to the GSN helpdesk. We introduce a new experimental balanced dataset and extract vector representations from eleven transformer-based models. These representations are evaluated using ten classic machine learning classifiers. Our findings highlight the superior performance of the Multilingual E5 Text Embeddings model, particularly when paired with the extreme gradient-boosting classifier. This combination demonstrates a clear advantage in accurately categorizing user queries, paving the way for more efficient automated helpdesk systems. © 2024 IEEE.

关键词： Contrastive Learning

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Zero-Shot Sentiment Analysis Exploring BART Models 8

Zero-Shot Sentiment Analysis Exploring BART Models

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Kyritsis, Konstantinos Perikos, Isidoros Paraskevas, Michael University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece University of Patras Computer Engineering and Informatics Department Patras Greece

ISBN: (纸本)9798350373615

The BART model is an advanced adaptation of transformers introduced by Facebook. It has incorporated elements from both BERT and GPT transformers, enabling significant advancements in language understanding and general speech processing. Utilizing both encoder and decoder components, BART proves versatile for various tasks, including translation, text completion, automatic sentence generation, entity recognition, sentiment analysis, and more. In this study, we focus on the study of pretrained models, BART and a modified version called distilbart, in the context of Zero-Shot Text Classification. In the experimental study we dive into the Zero-Shot technique applied to various pretrained Transformers. Our analysis demonstrates that, depending on the Model we utilize, we can achieve F1 scores of up to 88%, showcasing the model's effectiveness in discerning classes for this Sentiment Analysis problem using the Zero-Shot Text Classification technique. © 2023 IEEE.

关键词： Sentiment analysis

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Electricity Power Consumption Prediction With The Monte Carlo Simulation: Case Study Universitas Widya Dharma Pontianak 14

Electricity Power Consumption Prediction With The Monte Carl...

引用

14th International Conference on Information and Communication technology and System, ICTS 2023

作者： Hoendarto, Genrawan Saikhu, Ahmad Ginardi, Raden Venantius Hari Institute Technology of Sepuluh Nopember Department of Information Technology Surabaya Indonesia Universitas Widya Dharma Pontianak Indonesia Institute Technology of Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Information Technology Surabaya Indonesia Institute Technology of Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Science Surabaya Indonesia

ISBN: (纸本)9798350312164

Predicting electricity consumption for buildings has been a concern lately, as it accounts for 39% of total electricity consumption. One of the buildings that uses a lot of electricity is the campus building. Various methods are being developed and carried out in response to the fossil energy crisis to save electricity consumption. Before taking steps to reduce the use of electricity, it is necessary to predict its use. Monte Carlo simulation is a good option for predicting electricity consumption. The dataset used in this study was collected over the past year and recorded several times a day. According to the lecture days, the data used is only from Monday to Friday. Since the recording is done randomly over a day, we preprocess the data using tree regression. After the data pre-processing stage produces data in minutes, the last stage is the quartile search process to find the hourly average. © 2023 IEEE.

关键词： Forecasting

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

引用

4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Exploring Network Dynamics: Community Detection and Influencer Analysis in Multidimensional Social Networks

Exploring Network Dynamics: Community Detection and Influenc...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Andreas Vonitsanos, Gerasimos Karamitsos, Ioannis Al-Hussaeni, Khalil Ionian University Department of Informatics Corfu Greece University of Patras Computer Engineering and Informatics Department Patras Greece Rochester Institution of Technology Dubai United Arab Emirates Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates

ISBN: (纸本)9798350362480

In the digital era, multidimensional social networks have become integral to daily communication, catering to diverse relational needs, from interpersonal to professional and commercial. This study utilizes two comprehensive datasets from Twitter to explore and visualize user interactions within these networks. Focusing on advanced community detection algorithms, we apply the Louvain and Label Propagation methods to delineate the structure of these communities and identify influential users effectively. Through systematic analysis, our research reveals significant insights into the dynamics of network clusters and the pivotal role of influencers. We demonstrate that community structures significantly influence in formation dissemination and user engagement, providing key data to optimize digital communication strategies in complex environments. The findings underscore the importance of strategic influencer engagement and tailored community management in enhancing interaction within multidimensional social networks. Additionally, our results suggest that understanding the network's structural nuances can aid in developing targeted interventions that leverage influencer capabilities to maximize communication impact, illustrating potential applications across various sectors, including marketing, politics, and public health. © 2024 IEEE.

关键词： Tweets

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The Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks 3rd

The Effects of Weather Conditions on Data Transmission in Fr...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Kadhim, Aqeel A. Naif, Jolan R. Salman, Muna M. Hoomod, Haider K. Computer Techniques Engineering Department Imam AlKadhim University College Baghdad Iraq Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Information Technology Directorate Ministry of Science and Technology Baghdad Iraq College of Education Computer Science Department Al-Mustansiriya University Baghdad Iraq

ISBN: (纸本)9789819708918

It is necessary to collect data measured in open lands, whether for example for monitoring purposes or for border protection. The process of data transmission from active nodes and its next level to the cluster head (CH), followed by follow-up to arrive at the base station (BS), with safety and acceptability, are the main objectives of wireless sensor networks (WSNs). The key benefits of wireless systems are low-cost system installation and loss-free data transfer. Weather conditions in the spacious area affect wireless data transmission that is done between sensor nodes in free space. This study attempted to ascertain the influence of atmospheric and meteorological conditions on the loss of data when utilizing the wireless data transmission systems in a wireless sensor network. For this reason, the volume of data transfer losses that happen in free space because of three types of weather conditions is the main discussion point. The meteorological criteria that can be considered in this research are temperature, clear weather, dust, and rain as the weather elements that affect the Iraqi climate. The correlation analysis's outcomes show that the amount of precipitation and the size of raindrops have a main effect on the losses of data transmission. From a practical point of view, there is no confirmed statistical evidence of the amount of loss in the volume of transmitted data as a result of the amount of rainfall with the use of different transmission frequencies. In this research, we designed an objective routing protocol called Fuzzy Clustering with Ant Colony System FCACS, which has been utilized as a simulation program using the MATLAB 2018b application. The practical results of the proposed protocol for data transmission in free space and under different weather conditions were examined, and the simulation results were compared with those of two well-known routing protocols, which are DEEC and Z-SEP. In terms of the quantity of data received from the CH or BS a

关键词： Data transfer

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study 10

Data Driven Methods to Predict Vertical Profiles of the Rail...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Nugraha, Ade Chandra Supangkat, Suhono Harso Nugraha, Igb Baskara Handoko, Yunendar Aryo Purwadinata, Awan Hermawan Saputro, Dimas Wisnu Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Department of Computer Engineering and Informatics Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Smart City and Community Innovation Center Bandung Indonesia Bandung Institute of Technology Faculty of Mechanical and Aerospace Engineering Bandung Indonesia PT Kereta Api Indonesia Managing Director of Infrastructure Bandung Indonesia Bandung State of Polytechnic Department of Computer Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350339543

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains). © 2023 IEEE.

关键词： Forecasting

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