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A sentiment analysis of the Ukraine-Russia War tweets using knowledge graph convolutional networks

International Journal of Information Technology (Singapore) 2025年 1-18页

作者： Menaouer, Brahami Fairouz, Safa Meriem, Mohammed Boulekbachi Mohammed, Sabri Nada, Matta LABAB Laboratory Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria Computer Systems Engineering Department National Polytechnic School of Oran Oran Algeria TechCICO Laboratory University of Technology of Troyes Troyes France

Nowadays, social networks play a critical role in online social discourse, particularly during major events such as elections, health crises, and wars. Furthermore, individuals have spent significant time on social networks that consume news and express their opinions and viewpoints on various topics, namely, the Russia-Ukraine War. The Ukraine-Russia War is one of the most complicated and unfortunate events of this decade, with many aspects to be considered to have an informed opinion. For this reason, sentiment analysis research can be useful in analyzing sentiments or opinions about the Ukraine-Russia war, such as Twitter (X) posts. In this study, we propose a new deep learning-based sentiment classification model called "Knowledge Graph Convolutional Networks" that predicts and analyzes sentiments concerning the Russia-Ukraine war. There were 500,000 tweets collected in total, however only 410,428 were left for sentiment analysis after cleaning, lemmatization stemming, and deleting duplicate tweets. Results show that "war", "people", "world", "putin", "energy", "gas", "weapon", and "peace" were some of the most frequently occurring words in the tweets. The main aim of this work is to develop a robust system that provides an understanding of public sentiment towards the Russia-Ukraine war on the X platform. Experimental results demonstrate that it is possible to obtain more accurate sentiment classification results by the proposed method. They also have managerial, economic, and research implications. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Classification Decision support systems Graph Convolutional Networks (GCN) Knowledge graph Knowledge management Opinion mining Twitter sentiment analysis Ukraine-Russia War

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Comparative Assessment of Accessibility and Readability in Generative AI: A Case Study of GPT and Google BARD 4th

Comparative Assessment of Accessibility and Readability in G...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Afsal, C.P. Kuppusamy, K.S. Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9789819757022

The accessibility and readability of Generative Artificial Intelligence systems like GPT and Google BARD are crucial factors that require thorough examination. In today’s digitally connected world, where AI-generated content is becoming increasingly prevalent across various sectors, ensuring that such content is accessible to a broad audience, easy to understand, and user-friendly is essential. This research was motivated by the growing influence of AI-generated content on various user groups, including individuals with varying levels of familiarity with technology and diverse cognitive abilities. This paper comprehensively assesses and draws comparisons between the accessibility and readability of Google BARD and GPT to identify areas for enhancement. GPT exhibits various accessibility issues across different WCAG 2.0 conformance levels, contrasting with Google BARD, which shows no accessibility problems. The results underscore the importance of addressing accessibility concerns in AI models to ensure inclusivity and user-friendliness for individuals with disabilities. Google BARD consistently provides more readable content than GPT, underscoring the importance of selecting the appropriate tool based on the target audience and desired readability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ambient intelligence

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Traffic Prediction Using GPS Based Cloud Data Through RNN-LSTM-CNN Models: Addressing Road Congestion, Safety, and Sustainability in Smart Cities

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SN computer Science 2025年第2期6卷 1-18页

作者： Selvan, C. Senthil Kumar, R. Iwin Thanakumar Joseph, S. Malin Bruntha, P. Amanullah, M. Arulkumar, V. School of CSE REVA University Karnataka Bangalore India Department of Computer Science and Engineering (AIML) Faculty of Engineering and Technology Jain (Deemed-to-be) University Bangalore India Department of CSE Koneru Lakshmaiah Education Foundation Andhrapradesh Vaddeswaram India Department of ECE Karunya Institute of Technology and Sciences Coimbatore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

Smart cities that use technology and data for efficiency optimization, sustainability, and well-being of citizens face a lot of challenges. Because all of the aforementioned challenges share a common characteristic of complexity, achieving success will need careful preparation and coordinated effort. This research presents a novel approach utilizing deep learning models to address issues about road congestion, specifically by offering secure routes for pedestrians and cyclists. The Global Positioning System (GPS) data stored in the cloud is used as input for the proposed work. In the proposed work, the flow of vehicles, their speed, and the occupancy have been predicted. The need for deep learning to resolve the traffic problem is that deep learning methods are highly efficient when compared to statistical techniques as they provide more than 90% of accuracy in forecasting. The novel approaches used in this paper are integrated Recurring Neural Networks (RNN)-Long Short Term Memory (LSTM)- Convolutional Neural Networks (CNN) to form RLC (RNN-LSTM-CNN) models. The system encompasses appropriate methods for improving the transportation system’s efficiency by mitigating environmental impacts. The implementation of Recurrent Neural Networks (RNN) along with Long Short-Term Memory (LSTM) are used to analyze historical traffic flow data by predicting future traffic conditions by optimizing traffic signal timings, traffic flow, and public transportation schedules to reduce idling time and fuel consumption, leading to lower emissions by predicting Electric Vehicle (EV) charging demand patterns, optimize charging stations’ locations and driver routes, and manage energy distribution more efficiently. The proposed Deep Learning-based models perform better when compared to the other methods and hold the potential to transform urban mobility, making it more efficient, safer, and environmentally friendly in the smart cities of the future as it provides higher forecasting accuracy

关键词： CNN LSTM Occupancy RNN Speed Traffic flow prediction

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Exploring Few-Shot Learning to Enhance NLP's Cross-Domain Capabilities

Exploring Few-Shot Learning to Enhance NLP's Cross-Domain Ca...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Balakrishnan, T. Suresh Gururama Senthilvel, P. Ebenezar, U. Samson Karthikeyan, L. Kishan, B.S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Saveetha University Department of Computer Science and Engineering Chennai India Bms Institute of Technology and Management Department of Mechanical Engineering Bangalore India

ISBN: (纸本)9798331538538

Here, few shot learning (FSL) has been identified as a valuable method for improving the flexibility of NLP when dealing with new and different datasets with small amounts of training data. Most of the traditional NLP models are affected by shift since they are based on a big data set of a different domain. To overcome this challenge, FSL facilitates training of models to extrapolate from a few examples and not necessarily from numerous samples that may be present in LMMs. In this paper, I delve into meta-learning, prompt-based learning, and transfer learning as some of the methods that can be applied to boost NLP's cross-domain performance. We explain how these methods contribute to enhance performance in the use of fewer resources and evaluate the efficiency of those techniques on textual classification, sentiment analysis, and machine translation. Also, the pretrained GPT and BERT are beneficial to few-shot learning as it takes place through contextual learning. Based on our results, the conclusion is made that the application of FSL techniques in NLP pipelines enhances generalization and reduces the time and expenses on data annotation. Yet, some of the challenges that are associated with text analytics are data bias, sensibility of the prompts and computational problems. Finally, we propose some future works for FSL such as: the proposal of a new FSL model that combines traditional supervised and unsupervised learning;the presentation of a new FSL model based on a reinforcement learning theory. Hence, with the use of FSL, NLP models can perform well in different domains of application with little to no labeled data hence transferring knowledge to different domains to increase efficiency and scale in knowledge. © 2025 IEEE.

关键词： Sentiment analysis

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Predictive Maintenance Analysis using Multivariate Machine Performance Data for Industrial Downtime Reduction 3

Predictive Maintenance Analysis using Multivariate Machine P...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Saddala, Rajeswara Reddy Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita school of computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Tool downtime prediction requires efficient monitoring because it helps maximize industrial operation quality while maintaining uninterrupted plant operations. The analysis utilizes machine learning algorithms with Decision Trees to identify machine downtime events through operational measurements which include hydraulic pressure and coolant temperature and spindle speed and vibration levels. Data normalization alongside Forward Selection feature selection methods were used on a balanced dataset to boost model performance for prediction purposes. The Decision Tree model functioned to differentiate machine defects into two discrete categories—failure and no failure—thus proving its competence in such predictive classification activities. The study conducted a comparative evaluation between Decision Trees and Random Forest along with other algorithms which included SVM and Logistic Regression and Adaboost in addition to CatBoost and XGBoost and Naive Bayes and KNN using KD-Tree and Ball-Tree variations. The experimental findings show that Decision Trees provide clear predictions coupled with high precision and fair runtimes to support predictive maintenance implementations. The research delivers fundamental findings that help improve downtime prediction methods to boost operational effectiveness. © 2025 IEEE.

关键词： Predictive maintenance

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HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual Relation Classification on Mosquito Vector Biocontrol Agents 8th

HyPRETo: Hybrid Pre-trained Ontology Approach for Contextual...

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8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Jeyakodi, G. Bala, P. Shanthi Department of Computer Science School of Engineering and Technology Pondicherry University Puducherry India

ISBN: (纸本)9783031736162

Pre-trained Language Model facilitates contextual relation classification by capturing contextual information, addressing word ambiguity, encoding global sentence context, enabling transfer learning, handling out-of-vocabulary words, and improving performance with limited labelled data. Existing pre-training approaches suffer in size, bias, interpretability, generalization, and the lack of domain specificity. To address this, HyPRETo, the hybrid model that combines the strength of token replacement and dynamic masking is proposed to achieve upgraded performance to increase classification accuracy. The Mosquito Vector Biocontrol Agents data is used for implementing the model for a contextual relation classification task. HyPRETo uses ontology to provide structured knowledge. HyPRETo is pre-trained by ELECTRA and fine-tuned by RoBERTa models. Feedforward and softmax activation function is used for classification. The Natural Language Processing technique and SQL database are used to develop an automated question-answering system. The HyPRETo was evaluated with state-of-art models and achieved 98.42% accuracy. As a contribution, the manually annotated input dataset on the mosquito vector control agent is prepared for the classification task. Subsequently, the enhanced model is developed. The interface for an automated question-answering system for mosquito vector biocontrol agents is developed to assist public health applications such as mosquito vector control, disease control, ecosystem management, environmental conservation, and so on. © IFIP International Federation for Information Processing 2025.

关键词： Vectors

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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Virtual Learning Machine for Tiny Devices 20th

Virtual Learning Machine for Tiny Devices

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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Kitagawa, Nozomi Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Recent developments in artificial intelligence (AI) have increased the demand for high-performance computational devices. However, edge devices are highly restricted in terms of computational power and memory capacity. To address this, a learning engine called swap kernel regression (SKR) [Masaharu et al. 2019] was developed that enables both inference and learning using an edge device. The key feature of SKR is ability to extend beyond the real physical capacity by collaborating with a second storage device. SKR thereby enables the virtual construction of a large-scale learning machine on a tiny device. We developed an improved version of SKR that maximizes performance by solving the thrashing problem and further reduces the computational cost. Using this improved version, we aim to achieve few-shot class-incremental learning (FSCIL), which continuously adapts to new tasks with limited samples, envisioned for on-site implementation. The proposed method uses a kernel machine that operates in a restricted environment in collaboration with a secondary storage system. The kernel parameters, which are not essential for calculating the output values for upcoming inputs, are stored in secondary storage to create a space in the main memory. The essential kernel parameters stored in secondary storage are loaded into main memory when required. Using this strategy, the system can realize recognition or regression tasks without compromising its generalization capability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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A Wirelessly Powered, Battery-less, Miniaturized Microchip for Implantable Biopotential-Monitoring Applications

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IEEE Sensors Journal 2025年第11期25卷 19545-19554页

作者： Jang, Jaeeun Habibagahi, Iman Mathews, Roshan P. Gwak, Wonseop Rahmani, Hamed Babakhani, Aydin Kwangwoon University School of Semiconductor Systems Engineering Seoul Korea Republic of University of California at Los Angeles Department of Electrical Engineering Los AngelesCA90095 United States New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States

This paper presents a low-power, battery-less biopotential-recording integrated circuit (IC) designed for implantable leadless cardiac monitoring applications, addressing the full autonomy based on wireless energy harvesting and closed-loop control. A key technical feature is the closed-loop capability with integration of a dual-band radio frequency (RF) energy harvester and communication system utilizing industrial, scientific, and medical (ISM) bands at 40.68 MHz for downlink and 915 MHz for uplink, ensuring efficient wireless power transfer and robust bi-directional data communication. The RF harvester achieves a peak conversion efficiency exceeding 40%, a critical design challenge in maintaining consistent performance under variable power conditions. Also, proposed IC overcomes the need for a local oscillator by utilizing an externally provided clock, reducing system complexity and power consumption. Operating with a clock frequency of 100 kHz, the IC consumes only 8.8 μW, a major achievement in energy efficiency for wireless, battery-less systems. Fabricated in a 180 nm complementary metal-oxide-semiconductor (CMOS) process, the IC successfully demonstrates cardiac signal measurement and heart rate extraction under a fully battery-less environment with air medium, providing a promising solution for next-generation leadless cardiac monitoring systems. © 2001-2012 IEEE.

关键词： Inductive power transmission

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Ultrasound-based AI for COVID-19 detection: a comprehensive review of public and private lung ultrasound datasets and studies

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Multimedia Tools and Applications 2025年 1-55页

作者： Morshed, Abrar Al Shihab, Abdulla Jahin, Md Abrar Al Nahian, Md Jaber Hossain Sarker, Md Murad Ibne Wadud, Md Sharjis Uddin, Mohammad Istiaq Imtiaz Siraji, Muntequa Anjum, Nafisa Shristy, Sumiya Rajjab Rahman, Tanvin Khatun, Mahmuda Rubel Dewan, Md Hossain, Mosaddeq Sultana, Razia Chakma, Ripel Emon, Sonet Barua Islam, Towhidul Hussain, Mohammad Arafat Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka1208 Bangladesh Department of Electrical and Telecommunication Engineering Daffodil International University Dhaka1216 Bangladesh Khulna9203 Bangladesh Okinawa904-0412 Japan Department of Information and Communication Technology Bangladesh University of Professionals Dhaka1216 Bangladesh Department of Information and Communication Technology Comilla University Cumilla3506 Bangladesh Department of Biomedical Physics and Technology University of Dhaka Dhaka1000 Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chittagong4318 Bangladesh Department of Electrical and Electronic Engineering Islamic University of Technology Gazipur1704 Bangladesh Department of Electronics and Telecommunication Engineering Chittagong University of Engineering and Technology Chittagong4349 Bangladesh Department of Computer Science and Engineering Notre Dame University Bangladesh Dhaka1000 Bangladesh Department of Electrical and Electronic Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh Department of Computer Science and Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Electronics and Communication Engineering Institute of Science and Technology Dhaka1209 Bangladesh Department of Mathematics Jahangirnagar University Dhaka1342 Bangladesh Department of Electrical and Electronic Engineering Daffodil International University Dhaka1216 Bangladesh Department of Electrical and Electronic Engineering Dhaka University of Engineering and Technology Gazipur1707 Bangladesh Department of Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali3814 Bangladesh Department of Computer Science and Engineering Northern University Bangladesh Dhaka1230 Bangladesh Boston Children’s Hospital Harvard Medical School BostonMA02

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： COVID-19

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