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3rd International Conference on 6G Networking, 6GNet 2024

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350378597

The Network Digital Twin is emerging as a promising technology for future networks, including 6G, as it allows to gain deep knowledge of its Physical Twin and to perform 'what-if' analyses in a controlled environment. However, while the concept of Digital Twin reached maturity in other fields (such as manufacturing), still the application of this concept to networks presents some relevant challenges. This paper studies the problem of synchronization between the Digital and the Physical Twin, a key functionality to enable the Digital Twin to accurately replicate the behaviour of the Physical Twin. Indeed, accurate synchronization is challenging due to the amount of data to exchange and the delays due to acquisition, modeling and transmission of the related information. The paper analyzes the delay chain in the synchronization process and introduces the concept of 'negative delay' through accurate prediction in order to minimize the synchronization delay and enable real-time or 'near' real-time synchronization between the Physical and the Digital Twin. © 2024 IEEE.

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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Personality Analysis Using Graphology and Image Processing Techniques 1

Personality Analysis Using Graphology and Image Processing T...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Jadon, Anshika Gutpa, Harsh Shyan, Er. Hatesh Tyagi, Aayush Raj, Tejaswi Chandigarh University Dept. of Computer Science & Engineering Mohali140413 India

ISBN: (纸本)9798331532956

This research studies the role of advanced image processing and graphology at a higher level of complexity, that is, predicting grief by interpreting handwriting. The aim of this study is to establish a reliable model, which gets a connection between the deep learning techniques and a certain set of handwriting parameters that would depict a person's mental state. This method combines the foundations of traditional graphology with the newest advancements in computer technology, such as TensorFlow and Keras, and use CNNs for feature extraction. Advanced machine learning methods including PCA, Random Forest, SVMs, and Gradient Boosting are used to improve the model's ability to classify emotional states from handwriting. The results show that the model has established discriminative capability in classification of 85% of samples with precision and recall values at 88% and 82%, respectively. This research amalgamating the traditional graphology with modern practices in psychology provides an opportunity for further enhancements in mental health evaluation through handwriting-based approaches. © 2024 IEEE.

关键词： Photointerpretation

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The Tree Autoencoder Model, with Application to Hierarchical Data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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Energy-Based Controller Design for Decentralized AC Inverter-Base Micro-grids

Energy-Based Controller Design for Decentralized AC Inverter...

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2023 IEEE International Conference on Control, Electronics and computer Technology, ICCECT 2023

作者： Bozorgmehrian, Mohsen Attarsharghi, Samareh Ramezani, Amin Tarbiat Modares University Dept. of Electrical and Computer Engineering Tehran Iran University of Doha for Science and Technology Dept. of Electrical Engineering Doha Qatar

ISBN: (纸本)9798350399028

Micro-grids, which utilize photo-voltaic (PV) cells, wind turbines, and batteries, are gaining widespread adoption as a viable solution for renewable and sustainable energy infrastructures. However, ensuring the reliability and stability of these power grids is critical, and several control strategies have been developed to achieve these goals. In this study, we have modeled the decentralized behavior of microgrids using the port-Hamiltonian formulation and a PI controller to control the inverter voltage and output power. To demonstrate the effectiveness of our control method, we have conducted simulations that consider both maximum and worst-case system fluctuations. © 2023 IEEE.

关键词： Hamiltonians

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Survey on Deep Learning-based Speech Technologies in Voice Chatbot Systems

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KSII Transactions on Internet and Information Systems 2025年第5期19卷 1406-1440页

作者： Ma, Seunghee Oh, Junseok Kim, Minseo Kim, Ji-Hwan Dept. of Artificial Intelligence Sogang University Seoul Korea Republic of Dept. of Computer Science and Engineering Sogang University Seoul Korea Republic of Dept. of Artificial Intelligence Technology LOTTE INNOVATE Company Seoul Korea Republic of

Recent advancements in large language models (LLMs) such as ChatGPT have contributed to the development of chatbot systems. Specifically, speech has been recognized as the optimal tool for interactive dialogue, leading to increased interest in voice chatbots. Voice chatbots offer information and services through voice interactions, enhancing user experience and improving service accessibility. This survey paper introduces the latest developments in the core technologies of voice chatbot systems, including deep learning-based automatic speech recognition, speech synthesis, and speech emotion recognition. It focuses on advanced research to enhance speed and performance, which is crucial for applying speech technologies in voice chatbots. In automatic speech recognition, we introduce methodologies such as Connectionist Temporal Classification (CTC), Attention based Encoder-Decoder (AED), and Recurrent Neural Network Transducer (RNN-T), along with studies optimizing Transformer-based models for real-time automatic speech recognition. In speech emotion recognition, we explore the use of pre-trained models and the latest techniques for accurate emotion prediction. For speech synthesis, the focus extends to two-stage and End-to-End (E2E) approaches, with additional research on integrating emotional information to generate natural speech. Copyright © 2025 KSII.

关键词： Chatbots

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Deciphering Deception: Unmasking Fake News in Multilingual Contexts 5

Deciphering Deception: Unmasking Fake News in Multilingual C...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Agarwal, Ashutosh Singh, Yash Pratap Rai, Vipin Galgotias University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

A major global worry in the ever-expanding digital ecosystem is the spread of incorrect information. The ability to recognize false news in non-English languages is still lacking, despite great advancements in the identification of false news in English. This research looks at the unique opportunities and problems that these languages bring. It offers a thorough assessment and comparative analysis of existing approaches, highlighting their advantages and disadvantages. By doing comparative study of various differ techniques we have found that Multilingual-Fake Model currently works the best. The results of the study highlight how critical it is to use inclusive and linguistically varied tactics in order to successfully counter disinformation. It emphasizes how crucial it is to create trustworthy tools and processes that can adjust to the subtleties of many languages and their socio-cultural settings. It highlights how crucial it is to comprehend these components completely in order to create detection methods that are more potent. The study admits that the dissemination of incorrect information is an issue that is deeply rooted in the social environment of linguistic groups and transcends language borders. A discussion of possible future study areas closes the report. In a multilingual culture, this study marks a substantial breakthrough toward the development of more thorough and efficient techniques for spotting bogus news. © 2024 IEEE.

关键词： Bidirectional Transformer fake news multi-lingual

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DubasAlert: AI-Powered Detection System for Dubas Infestation on Palm Leaves

DubasAlert: AI-Powered Detection System for Dubas Infestatio...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Nagaraju, Y. Venkatesh Uvce Bangalore University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different stages of their growth. Existing models perform ineffectively and have large trained weights, making integration with edge devices difficult. To address these issues, we propose a pruned YOLOv7 model which is lighter and has fewer parameters, resulting in 75% lower FLOPs. Pruned Yolov7 outperforms baseline models with a mAP@0.5 of 80.1%. Our approach enables real-time detection and seamless integration into edge devices, resulting in practical solutions to combat Dubas insect infestations. © 2024 IEEE.

关键词： Plant diseases

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks 6

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Chowdhury, Rituparna Das, Rudra Faruk Ananna, Fowzia Binta Saha, Arnob Nawar, Sadia Hosen, Md. Hamid East Delta University Dept. of Computer Science & Engineering Chittagong Bangladesh

ISBN: (纸本)9798350385779

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. © 2024 IEEE.

关键词： Lime

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FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOALS 25

FOOTBALL DATA ANALYSIS: THE PREDICTIVE POWER OF EXPECTED GOA...

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25th International Conference on Intelligent Games and Simulation, GAME-ON 2024

作者： Roccetti, Marco Berveglieri, Filippo Cappiello, Giuseppe Dept. of Computer Science and Engineering University of Bologna Italy Dept. of Management University of Bologna Italy

ISBN: (纸本)9789492859327

Major football events, such as the World Cup, have popularized the European football (i.e., soccer) beyond its traditional geographical borders, for example with a growing number of Americans who have begun to incorporate European football into their daily sport consumption. Many video game series based on this sport have further contributed to this increasing development. Not only that, but many performance indicators inspired by football computer games have become metrics that are now part of the normal conversation about real football, being used to analyse tactics, teams and players. Among these indicators, of prominent importance is the role of expected goals (xG) that measure the quality of a shot by calculating the likelihood that it will be scored from a particular position during a particular phase of play. Often, someone talks about xG leading to imprecise conclusions and to a distorded point of view on this performance indicator. In this paper, using a non-parametrical Kolmogorov Smirnov hypothesis testing method, we show that there is no statistically significant difference if, in a given football tournament, the final scores are awarded to the participating teams either with real goals or based on xGs, thus demystifying some wrong assumptions about this indicator. Data for this study come from all the matches played during the 2020 UEFA European Football Championship. © 2024 25th International Conference on Intelligent Games and Simulation, GAME-ON 2024. All rights reserved.

关键词： Predictive analytics

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