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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Anser Pasha, C.A. Sampath, S. Computer Science & Engineering Department Adichunchanagiri Institute of Technology Karnataka Chikmagaluru India Visvesvaraya Technological University Belagavi590018 India Information Science & Engineering Department Adichunchanagiri Institute of Technology Karnataka Chikmagaluru India

ISBN: (纸本)9798350383867

Fake news poses a significant threat to social media and their users. Hybrid models for detection of fake news on social media, analyzing their strengths, weaknesses, and future directions are surveyed in it. We present an overview of fake news detection, discuss the limitations of individual approaches, and emphasis pros of combining them. We then describe various hybrid models categorized by the variety of techniques they integrate, including content analysis, network analysis, and knowledge reasoning. Additionally, we compare different hybrid models using key metrics namely correctness, precision, recall, and F1-score, presenting the results in tables and diagrams. Finally, we discuss open challenges and promising future directions for research in this domain. Additionally, it serves as a roadmap for future research directions, guiding the development of more robust and ethically sound fake news detection systems. ©2024 IEEE.

关键词： content analysis Fake news hybrid models knowledge reasoning machine learning network analysis social media source credibility assessment

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Identification of Epileptic Seizures Utilising a Computationally Powerful Spiking Neuron

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SN computer science 2024年第8期5卷 1-13页

作者： Hussain, Irshed Thounaojam, Dalton Meitei Computer Science and Information Technology Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Computer Science and Engineering National Institute of Technology Silchar Assam Silchar 788010 India

Epilepsy is considered a complex neurological condition that causes the central nervous system to malfunction, which results in seizures. Therefore, in-depth identification of the condition with high accuracy is required, and Electroencephalogram (EEG) plays a vital role. To classify seizures from the EEG signals, a computationally efficient Spiking Neural Networks (SNN) classifier inspired by WOLIF that utilises the error-function adjusted by the Grey Wolf Optimizer (GWO) and derived from spiking neurons with Leaky Integrate and Fire (LIF) is employed in this paper. It works on the principle of SNN, which is computationally very efficient and does not need hidden layer(s) even though the data is highly non-linear, such as EEG signals. The features are extracted from the raw EEG signals utilising the Discrete Wavelet Transform (DWT). Then, the information is encoded using the population coding technique since SNN works with temporal information. This study makes use of the epileptic seizures dataset from Bonn University. Four binary classifications between healthy and seizure classes from five data sets were done. Unlike WOLIF, α-kernel is used as the synapse model rather than the double-decaying kernel function, improving the overall accuracy. The results shown by the proposed approach are very appealing if both computational efficiency and accuracy are considered the metrics for analysis, which is very important. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computationally efficient DWT EEG Epilepsy SNN WOLIF

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A Novel Transformer Based Deep Learning Approach of Sentiment Analysis for Movie Reviews 6

A Novel Transformer Based Deep Learning Approach of Sentimen...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Saad, Tayef Billah Ahmed, Mohiuddin Ahmed, Boshir Sazan, Saad Ahmed Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Language and organ signs allow humans to convey their feelings. When a large number of people express their emotions about a particular topic, it indicates a point of col-lective assessment, in short people's sentiment. Due to the vast production, sharing, and transition of data, opinions, and product reviews online and in media, sentiment analysis is essential. It evaluates the information and categorizes it as positive or negative. Numerous machine learning and deep learning algorithms can be deployed to interpret the sentiment behind any opinion or text. The sequential processing inherent in sequence models often leads to prolonged execution times. However, the Transformer model parallelized processing reduces calculation time. To that aim, our research suggests a comprehensive deep learning approach that mitigates the drawbacks associated with sequential models while combining the favorable aspects of both Transformer and sequence models. This architecture uses the IMDB dataset of 50K movie reviews to analyze the reviewers' expressions and the proposed model has been experimented with three advanced transformer-based deep-learning approaches such as BERT, RoBERTa, and DistilBERT. Of these, the Robustly optimized BERT approach (RoBERTa) fares better than the other two, obtaining an accuracy of 95.02 %, which is higher than 94.12% of BERT and 92.82% of DistilBERT approaches as well as the state-of-the-art models. This study highlights the importance of sentiment analysis as a method for understanding the emotions and attitudes conveyed in movie reviews. The high score also forecasts the efficacy of a film by taking into account the mean emotion of all the critiques. © 2024 IEEE.

关键词： Sentiment analysis

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Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation

HighTech and Innovation Journal

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HighTech and Innovation Journal 2024年第3期5卷 572-602页

作者： Yuniarti, Wenty D. Hartati, Sri Priyanta, Sigit Surjono, Herman D. Department of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Information Technology Universitas Islam Negeri Walisongo Semarang50185 Indonesia Department of Informatics Engineering Education Faculty of Engineering Universitas Islam Negeri Walisongo Yogyakarta55281 Indonesia

E-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. © Authors retain all copyrights.

关键词： Contrastive Learning

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NIDR: Network Interference Detection and Rectification 4

NIDR: Network Interference Detection and Rectification

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Rose Mary, K.A. Joy, Celestine Devis, Denin Fransisco, Irene Devassy, Deepa George, Livya Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307757

In the contemporary digital landscape, ensuring the continuous functionality of computer networks is vital for business and critical infrastructure success. This research project utilizes cutting-edge technologies like machine learning and anomaly detection to monitor network traffic and system logs. Through in-depth analysis of traffic patterns, identification of attack signatures, and prompt anomaly detection, the system provides accurate and timely identification of network attacks. Key components include real-time monitoring, comprehensive attack signature databases, and intelligent alerting for swift response, aiming to empower small-scale organizations in safeguarding their data and effectively responding to evolving cyber threats. © 2024 IEEE.

关键词： Anomaly detection

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Enhancing coordinated target tracking: innovative particle filters with meta-heuristic integration and advanced model validation

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Soft Computing 2025年第7期29卷 3307-3338页

作者： Das, Nitish Kulkarni, Nilima Department of Computer Science and Engineering MIT Art Design and Technology University Loni Kalbhor Pune412201 India

Coordinated target tracking and its augmented variant represent significant challenges in modern surveillance. These tasks are essential for accurately localizing and predicting the movement of dynamic targets. This article introduces an advanced particle filter algorithm with a packet dropout mechanism to enhance tracking accuracy. Additionally, it proposes three meta-heuristic-driven particle filters: the Particle Filter integrated with the Chaotic Gravitational Search Algorithm (PF-CGSA), the Particle Filter integrated with the Improved Grey Wolf Optimizer (PF-IGWO), and the Particle Filter integrated with the Imperialist Competitive Algorithm with Regional Domination Policy (PF-ICARD). These methods are tested using three sophisticated models: the Coordinated Turn Target Tracking Model (CT model), the Augmented Coordinated Turn Target Tracking Model with Cartesian Velocity (ACT1 model), and the Augmented Coordinated Turn Target Tracking Model with Polar Velocity (ACT2 model). Experimental results demonstrate that PF-CGSA, PF-IGWO, and PF-ICARD achieve higher convergence speeds and accuracy. They also effectively mitigate premature convergence phenomena. Comparative analysis based on Root Mean Squared Error (RMSE) metrics underscores their superiority in real-world target tracking challenges, highlighting the potential of these advanced particle filtering techniques in enhancing precision and efficiency in target tracking. Particularly, PF-ICARD achieves the highest accuracy, reducing RMSE by up to 18.64% compared to PF-CGSA and PF-IGWO, making it ideal for high-precision applications. PF-CGSA excels in specific scenarios, notably improving X-coordinate tracking in the ACT2 model by up to 11.39%. Meanwhile, PF-IGWO offers a balanced solution with moderate accuracy and enhanced computational efficiency, suitable for real-time applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Aircraft detection

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A unified user behavior model for trajectory-based tasks with different types of path constraints

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science China(information sciences) 2023年第10期66卷 316-318页

作者： Hao ZHANG Jin HUANG Huawei TU Feng TIAN Guozhong DAI Hongan WANG Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Department of Computer Science and Information Technology La Trobe University

An important research branch of human-computer interaction(HCI) is to develop predictive models for human performance in fundamental interactions [1]. On today's graphical user interface(GUI), users often implicitly perform various trajectory-based interactions, such as navigating through menus [2], entering the boundary of a button,

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Yoga pose classification: a CNN and MediaPipe inspired deep learning approach for real-world application

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16551-16562页

作者： Garg, Shubham Saxena, Aman Gupta, Richa Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

Yoga is a centuries-old style of exercise followed by sports personnel, patients, and physiotherapist as their regime. A correct posture and technique are the key points in yoga to reap the maximum benefits. Hence, developing a model to classify yoga postures correctly is a recently emerging research topic. The paper presents a novel architecture that aims to classify various yoga poses. The proposed model estimates and classifies yoga poses into five broad categories with low latency. In the proposed architecture, the images are skeletonized before inputting into the model. The skeletonization process is done using the MediaPipe library for body keypoint detection. The paper compares the performance of various deep learning models with and without skeletonization. Different learning models showed the optimum result with the training of skeletonized images to the network. The comparison is drawn to establish the positive impact of skeletonization on the results obtained by various models. VGG16 achieves the highest validation accuracy on non-skeletonized images (95.6%), followed by InceptionV3, NASNetMobile, YogaConvo2d (proposed model) (89.9%), and lastly, InceptionResNetV2. In contrast, the proposed model YogaConvo2d using skeletonized images reports a validation accuracy of 99.62%, followed by VGG16, InceptionResNetV2, NASNetMobile, and InceptionV3. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： computer vision

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Advanced Natural Language Processing Techniques for Efficient Sentiment An alysis of US Airline Twitter Data: A High-Performance Framework for Extracting Insights from Tweets 6

Advanced Natural Language Processing Techniques for Efficien...

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6th International Conference on Electrical engineering and information and Communication technology, ICEEICT 2024

作者： Sazan, Saad Ahmed Ahmed, Mohiuddin Saad, Tayef Billah Roy, Mrittika Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Sentiment analysis is a critical aspect of natural language processing that can provide valuable insights into customer satisfaction and opinions. Understanding sentiment, particularly in the aviation industry, can help organizations enhance their services and improve customer experiences. To advance sentiment analysis in this field, our research analyzed Twitter data related to US airlines. We employed a data augmentation technique using a pre-trained RoBERTa model to address class imbalance and applied the fastText classifier from Facebook's AI Research (FAIR) lab to gain valuable insights. Our study aimed to comprehend and enhance customer satisfaction in the aviation industry by presenting a comprehensive approach to sentiment analysis. The results not only demonstrate the effectiveness of our methodology but also provide airlines with actionable insights to proactively improve services based on real-time customer sentiments expressed on social media. This work represents a significant stride in leveraging advanced natural language processing techniques to provide superior customer experiences. Our approach achieved an impressive F1 score of 92.05% for ternary classification and 96.23% for binary classification, surpassing state-of-the-art models. This high score highlights the robustness of our methodology, establishing it as a potent tool for extracting meaningful sentiment from social media data in the airline domain. © 2024 IEEE.

关键词： Sentiment analysis

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Exploring Transformer and Recurrent Neural Models for Sentiment Analysis of Mobile App Reviews 2

Exploring Transformer and Recurrent Neural Models for Sentim...

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2nd International Conference on information and Communication technology, ICICT 2024

作者： Roy, George Tonmoy Biswas, Dipannita Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (纸本)9798331508227

Sentiment analysis is one of the important aspects of discovering user opinions and enhancing the quality of products like mobile applications. This study assesses the effectiveness of six prominent Natural Language Processing (NLP) models—BERT, RoBERTa, XLNet, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—in classifying sentiments from a large dataset of Google Play Store reviews. We collected 73,483 reviews from 45 apps, preprocessed the data, and employed two labeling methods: a pre-trained DistilBERT model and K-means clustering. Our results compare transformer-based models, BERT and XLNet, with traditional recurrent models for sentiment analysis of mobile application reviews. The results show that transformer-based models achieve higher accuracy, precision, recall, and F1 scores with DistilBERT pre-trained model labeling. However, no single model strikes a balance between recall and precision using K-means clustering labels. These findings suggest that the choice of labeling method, in addition to the use of advanced transformer models and embeddings, significantly affects model performance. ©2024 IEEE.

关键词： Long short-term memory

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