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A Machine Learning Model to Classify Social Media Content 3

A Machine Learning Model to Classify Social Media Content

3rd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2024

作者： Chetty, Naganna Alathur, Sreejith Saldana, Ashika Ruth Gupta, Amit Hasan, Mohammad Kamrul NMAM Institute of Technology Nitte University Department of Information Science & Engineering Mangaluru India Indian Institute of Management Department of Information Systems Kozhikode India K.R. Puram Hobli Bangalore East Taluk Kyndryl Solutions Private Limited Rachenahalli Village Bengaluru India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Centre for Cyber Security Universiti Kebangsaan Malaysia Faculty of Information Science & Technology Selangor Malaysia

ISBN: (纸本)9798350391244

The world has witnessed the exponential growth of internet users. The dissipation of toxic content through social media platforms has recently increased, leading to global communal activities. This work aims to develop a model to classify online toxic content. The data/tweets are collected from multiple sources and combined to train the classification model. The dataset is pre-processed to eliminate stop words, punctuations, digits, blank spaces and perform stemming. The data has been split into training and testing parts with a ratio of 75:25 respectively. The model is trained using different machine learning algorithms on extracted features from the dataset. The random forest algorithm has been shown better performance by categorizing tweets into racist, sexist, and normal than other algorithms used. The accuracy of various models is compared and tabulated. Further, the model can be enhanced by using deep learning algorithms and quality datasets. © 2024 IEEE.

关键词： Tweets

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Learning good state and action representations for Markov decision process via tensor decomposition

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The Journal of Machine Learning Research 2023年第1期24卷 5157-5209页

作者： Chengzhuo Ni Yaqi Duan Munther Dahleh Mengdi Wang Anru R. Zhang Department of Electrical & Computer Engineering Princeton University Laboratory for Information & Decision Systems Massachusetts Institute of Technology Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Department of Biostatistics & Bioinformatics Duke University

The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning method to identify meaningful low-dimensional state and action representations from empirical trajectories. The method exploits the MDP's tensor structure by kernelization, importance sampling and low-Tucker-rank approximation. This method can be further used to cluster states and actions respectively and find the best discrete MDP abstraction. We provide sharp statistical error bounds for tensor concentration and the preservation of diffusion distance after embedding. We further prove that the learned state/action abstractions provide accurate approximations to latent block structures if they exist, enabling function approximation in downstream tasks such as policy evaluation.

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Semantic Graph Representation and Evaluation for Generated Image Annotations 6th

Semantic Graph Representation and Evaluation for Generated I...

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6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Samih, Haitham Rady, Sherine Ismail, Manal A. Gharib, Tarek F. Faculty of Computer and Information Technology Egyptian E-Learning University Cairo Egypt Information Systems Department Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Engineering and Systems Department Faculty of Engineering Helwan University Cairo Egypt

ISBN: (纸本)9783030697167

The rapid advancement in generated image annotations, also known as captions, puts a crucial need for efficient and automated methods for those annotations’ evaluation. State-of-art in the automatic evaluation metrics has proven to be inefficient for evaluating the quality of the generated annotations because they rely on certain aspects of word matching, such as the n-gram overlap. This paper presents a semantic-based graph representation and evaluation for generated image annotations. The semantic graph explicitly encodes objects, attributes, and natural language annotations relationships, while consulting ConceptNet as an external knowledge source to provide a rich semantic generated graph. For annotations evaluation, the ConceptNet is extended for use in a proposed semantic evaluation metric, whose input is the semantic graphs. Experimental results show that, over the Flickr8k dataset, the proposed graph-based evaluation metric achieves a higher system-level correlation and rank correlation coefficient value compared to existing related works. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image annotation

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Covert Communication in Satellite-Terrestrial systems with a Full-Duplex Receiver

Covert Communication in Satellite-Terrestrial Systems with a...

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2024 International Conference on Satellite Internet, SAT-NET 2024

作者： Guo, Zewei Sun, Ranran He, Ji Shen, Yulong Jiang, Xiaohong Future University Hakodate School of Systems Information Science Hakodate041-8655 Japan Hangzhou Institute of Technology Xidian University Hangzhou311200 China Xidian University School of Computer Science and Technology Xi'an710071 China Institute of Systems General Academy of Systems Engineering Academy of Military Sciences Beijing100101 China Xidian University Shaanxi Key Laboratory of Network and System Security Xi'an710071 China

ISBN: (纸本)9798350375176

This paper investigates the covert communication in a satellite-terrestrial system, including a satellite transmitter Alice, a terrestrial full-duplex receiver Bob and a terrestrial warden Willie. To help the covert transmission from Alice, the full-duplex Bob transmits the jamming signal to confuse Willie and also receives the signal from Alice simultaneously. To explore the fundamental covertness in such a system, we first provide theoretical modelings for satellite-terrestrial downlink, and the detection error probability of Willie. Based on these models, we then study the transmit power optimization for covert rate maximization. Finally, we present extensive numerical results to validate our theoretical analysis and to illustrate the impact of various system parameters on the maximum covert rate. © 2024 IEEE.

关键词： Satellite communication systems

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What's the Real: A Novel Design Philosophy for Robust AI-Synthesized Voice Detection 24

What's the Real: A Novel Design Philosophy for Robust AI-Syn...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Hai, Xuan Liu, Xin Tan, Yuan Liu, Gang Li, Song Niu, Weina Zhou, Rui Zhou, Xiaokang School of Information Science and Engineering Lanzhou University Lanzhou China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Business Data Science Kansai University Osaka Japan

ISBN: (纸本)9798400706868

Voice is one of the most widely used media for information transmission in human society. While high-quality synthetic voices are extensively utilized in various applications, they pose significant risks to content security and trust building. Numerous studies have concentrated on AI-synthesized voice detection to mitigate these risks, with many claiming to achieve promising performance. However, recent research has demonstrated that fake voice detectors suffer from serious overfitting to speaker-irrelative features (SiFs) and cannot be used in real-world scenarios. In this paper, we analyze the limitations of existing fake voice detectors and propose a new design philosophy, guiding the detection model to prioritize learning human voice features rather than the difference between the human voice and the synthetic voice. Based on this philosophy, we propose a novel AI-synthesized voice detection framework named SiFSafer, which uses pre-trained speech representation models to enhance the learning of feature distribution in human voices and the adapter fine-tuning to optimize the performance. The evaluation shows that the average EERs of existing fake voice detectors in the ASVspoof datasets can exceed 20% if the SiFs like silence segments are removed, while SiFSafer achieves an EER of less than 8%, indicating that SiFSafer is robust to SiFs and strongly resistant to existing attacks. © 2024 ACM.

关键词： Fake detection

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IoT with Evolutionary Algorithm Based Deep Learning for Smart Irrigation System

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computers, Materials & Continua 2022年第4期71卷 1713-1728页

作者： P.Suresh R.H.Aswathy Sridevi Arumugam Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Anwer Mustafa Hilal Mohammad Alamgeer Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical Sciences Adithya Institute of TechnologyCoimbatore641107India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Information Systems King Khalid UniversityMuhayel AseerSaudi Arabia

In India, water wastage in agricultural fields becomes a challengingissue and it is needed to minimize the loss of water in the irrigation *** the conventional irrigation system needs massive quantity of waterutilization, a smart irrigation system can be designed with the help of recenttechnologies such as machine learning (ML) and the Internet of Things (IoT).With this motivation, this paper designs a novel IoT enabled deep learningenabled smart irrigation system (IoTDL-SIS) technique. The goal of theIoTDL-SIS technique focuses on the design of smart irrigation techniquesfor effectual water utilization with less human interventions. The proposedIoTDL-SIS technique involves distinct sensors namely soil moisture, temperature, air temperature, and humidity for data acquisition purposes. The sensordata are transmitted to the Arduino module which then transmits the sensordata to the cloud server for further process. The cloud server performs the dataanalysis process using three distinct processes namely regression, clustering,and binary classification. Firstly, deep support vector machine (DSVM) basedregression is employed was utilized for predicting the soil and environmentalparameters in advances such as atmospheric pressure, precipitation, solarradiation, and wind speed. Secondly, these estimated outcomes are fed intothe clustering technique to minimize the predicted error. Thirdly, ArtificialImmune Optimization Algorithm (AIOA) with deep belief network (DBN)model receives the clustering data with the estimated weather data as inputand performs classification process. A detailed experimental results analysisdemonstrated the promising performance of the presented technique over theother recent state of art techniques with the higher accuracy of 0.971.

关键词： Smart irrigation precision agriculture internet of things deep learning machine learning

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A Bi-population Cooperative Optimization Algorithm Assisted by an Autoencoder for Medium-scale Expensive Problems

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1952-1966页

作者： Meiji Cui Li Li MengChu Zhou Jiankai Li Abdullah Abusorrah Khaled Sedraoui IEEE the School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China the Department of Electronics and Information Engineering Tongji UniversityShanghai 201804China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

This study presents an autoencoder-embedded optimization(AEO)algorithm which involves a bi-population cooperative strategy for medium-scale expensive problems(MEPs).A huge search space can be compressed to an informative lowdimensional space by using an autoencoder as a dimension reduction *** search operation conducted in this low space facilitates the population with fast convergence towards the *** strike the balance between exploration and exploitation during optimization,two phases of a tailored teaching-learning-based optimization(TTLBO)are adopted to coevolve solutions in a distributed fashion,wherein one is assisted by an autoencoder and the other undergoes a regular evolutionary ***,a dynamic size adjustment scheme according to problem dimension and evolutionary progress is proposed to promote information exchange between these two phases and accelerate evolutionary convergence *** proposed algorithm is validated by testing benchmark functions with dimensions varying from 50 to *** indicated in our experiments,TTLBO is suitable for dealing with medium-scale problems and thus incorporated into the AEO framework as a base *** with the state-of-the-art algorithms for MEPs,AEO shows extraordinarily high efficiency for these challenging problems,t hus opening new directions for various evolutionary algorithms under AEO to tackle MEPs and greatly advancing the field of medium-scale computationally expensive optimization.

关键词： Autoencoder dimension reduction evolutionary algorithm medium-scale expensive problems teaching-learning-based optimization

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MD-TransUNet: TransUNet with Multi-attention and Dilated Convolution for Brain Stroke Lesion Segmentation 19th

MD-TransUNet: TransUNet with Multi-attention and Dilated C...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Jie Wan, Jian Zhang, Xin School of Computer Science and Technology Hangzhou Dianzi University Zhejiang310018 China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Ministry of Education Beijing China School of Information and Electronic Engineering Zhejiang University of Science and Technology Zhejiang China Key Laboratory of Marine Ecosystem Dynamics Second Institute of Oceanography Ministry of Natural Resources Hangzhou China

ISBN: (纸本)9783031545276

The accurate segmentation of stroke lesion regions holds immense significance in shaping treatment strategies and rehabilitation protocols. Due to the large difference in the volume of stroke lesion areas and the great similarity between lesion areas and normal tissues, most of the existing methods for lesion segmentation cannot deal with these problems well. This paper proposes a novel network named MD-TransUNet for the segmentation of stroke lesions, whose framework is based on the UNet architecture. To fully obtain deep image features, it uses ResNet50 for downsampling. MD (multi-dilated) module is employed as the skip connection to gain more receptive fields. Different receptive fields can adapt to varying volumes of lesion areas. Then, a feature extraction module with multi-level attention mechanism is designed using ConvLSTM, non-local spatial attention, and channel attention modules to suppress useless information expression in skip connections and upsampling processes while focusing more on effective spatial and channel information in features. The experiments show that our proposed network gets superior performance than benchmark methods and indicates the generalization and effectiveness of the proposed model. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Convolution

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Efficient Joint Key Authentication Model in E-Healthcare

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computers, Materials & Continua 2022年第5期71卷 2739-2753页

作者： Muhammad Sajjad Tauqeer Safdar Malik Shahzada Khurram Akber Abid Gardezi Fawaz Alassery Habib Hamam Omar Cheikhrouhou Muhammad Shafiq Department of Computer Science Air University Multan CampusMultan60000Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur 63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia Faculty of Engineering Moncton UniversityNBE1A3E9Canada CES Laboratory National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Many patients have begun to use mobile applications to handle different health needs because they can better access high-speed Internet and *** devices and mobile applications are now increasingly used and integrated through the medical Internet of Things(mIoT).mIoT is an important part of the digital transformation of healthcare,because it can introduce new business models and allow efficiency improvements,cost control and improve patient *** the mIoT system,when migrating from traditional medical services to electronic medical services,patient protection and privacy are the priorities of each ***,it is recommended to use different user authentication and authorization methods to improve security and *** this paper,our prosed model involves a shared identity verification process with different situations in the e-health *** aim to reduce the strict and formal specification of the joint key authentication *** use the AVISPA tool to verify through the wellknown HLPSL specification language to develop user authentication and smart card use cases in a user-friendly *** model has economic and strategic advantages for healthcare organizations and healthcare *** medical staff can increase their knowledge and ability to analyze medical data more *** model can continuously track health indicators to automatically manage treatments and monitor health data in real ***,it can help customers prevent chronic diseases with the enhanced cognitive functions *** necessity for efficient identity verification in e-health care is even more crucial for cognitive mitigation because we increasingly rely on mIoT systems.

关键词： E-health systems joint key authentication mutual authentication

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EvoPath: Evolutionary Meta-path Discovery with Large Language Models for Complex Heterogeneous information Networks

arXiv

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arXiv 2025年

作者： Liu, Shixuan Cheng, Haoxiang Wang, Yunfei He, Yue Fan, Changjun Liu, Zhong Laboratory for Big Data and Decision College of Systems Engineering National University of Defense Technology Hunan Changsha China National Key Laboratory of Information Systems Engineering College of Systems Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science and Technology Tsinghua University Beijing China

Heterogeneous information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination. This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework. © 2025, CC BY-NC-ND.

关键词： Semantics

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