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17th IEEE International Symposium on Applied Computational Intelligence and informatics, SACI 2023

作者： Krutilla, Zsolt Kovari, Attila Óbuda University Doctoral School of Applied Informatics and Applied Mathematics Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty buda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary University of Dunaújváros Institute of Engineering Department of Natural Science Dunaújváros Hungary John von Neumann University Gamf Faculty of Engineering and Computer Science Kecskemét Hungary

ISBN: (纸本)9798350321104

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a 'business as usual' (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models. © 2023 IEEE.

关键词： Information retrieval systems

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Complexity of Computing the Anti-Ramsey Numbers for Paths

SSRN

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SSRN 2024年

作者： Popa, Alexandru Amiri, Saeed Akhoondian Roghani, Mohammad Shahkarami, Golnoosh Soltani, Reza Vahidi, Hossein University of Cologne Cologne Germany University of Bucharest Romania National Institute of Research and Development in Informatics Bucharest Romania Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Sharif University of Technology Tehran Iran Saarbrücken Graduate School of Computer Science Saarbrücken Germany

The anti-Ramsey numbers are a fundamental notion in graph theory, introduced in 1978, by Erd˝os, Simonovits and Śos. For given graphs G and H the antiRamsey number ar(G,H) is defined to be the maximum number k such that there exists an assignment of k colors to the edges of G in which every copy of H in G has at least two edges with the same ***, combinatorists study extremal values of anti-Ramsey numbers for various classes of graphs. There are works on the computational complexity of the problem when H is a star. Along this line of research, we study the complexity of computing the anti-Ramsey number ar(G,Pk), where Pk is a path of length k. First, we observe that when k is close to n (the number of vertices in G), the problem is hard;hence, the challenging part is the computational complexity of the problem when k is a fixed *** provide a characterization of the problem for paths of constant length. Our first main contribution is to prove that computing ar(G,Pk) for every integer k > 3 is NP-hard. We obtain this by providing several structural properties of such coloring in graphs. We also study the exact complexity of the precolored version and show that there is no subexponential algorithm for the problem unless ETH fails for any fixed constant k. © 2024, The Authors. All rights reserved.

关键词： Computational complexity

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Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method 6

Classification of Batik Authenticity Using Convolutional Neu...

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6th International Conference on informatics and Computing, ICIC 2021

作者： Putra, Farrel Athaillah Jamil, Dwi Anggun Cahyati Prabandanu, Briliantino Abhista Faruq, Suhaili Pradana, Firsta Adi Alya, Riqqah Fadiyah Santoso, Heru Agus Al Zami, Farrikh Saputra, Filmada Ocky Dian Nuswantoro University Informatics Engineering Semarang Indonesia Diponegoro University Computer Engineering Semarang Indonesia Telkom Institute of Technology Informatics Engineering Purwokerto Indonesia

ISBN: (纸本)9781665421553

Batik is one of Indonesia's cultural heritages that UNESCO has recognized as an Intangible Cultural Heritage, so we should be proud and preserve it. However, there are problems in the batik industry related to the labelling of traditional and modern batik products. The prevalence of fraud in printed batik, which is given a price equivalent to written batik, which is much more expensive, and public ignorance of the aesthetic value and authenticity of written batik, can disrupt the traditional batik industry in Indonesia. Based on these problems, the authors innovate to develop a machine learning model that aims to classify the authenticity of batik using the Convolutional Neural Network Algorithm with Transfer Learning Method. The classification process consists of several stages: collecting datasets, preprocessing data, developing CNN models with transfer learning, and compiling and training models. The development of the machine learning model that has been trained produces an accuracy of 96.91%. The author hopes that this research can make it easier for people to distinguish between written and printed batik, minimize the existence of batik price fraud, and increase consumer confidence in batik transactions by ensuring the originality of batik products. © 2021 IEEE.

关键词： Classification (of information)

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Application of Neural Network in the Prediction Models of Machine Learning Based Design

Application of Neural Network in the Prediction Models of Ma...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gehlot, Anita Ansari, Bushra Khatoon Arora, Deepika Anandaram, Harishchander Singh, Bhagwant Arias-Gonzáles, José Luis Uttaranchal University Uttaranchal Institute of Technology India Institute of Engineering and Technology AKTU Department of Civil Engineering Lucknow India Lloyd Institute of Engineering & Technology Computer Science & Engineering Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Pontificia Universidad Católica Del Peru Peru

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This research examines way to pass, among the most dynamic and quickly-evolving technology fields in the telecommunication sector. Data may be sent between two locations wirelessly without the usage of digital downloads like wires and cables. Business telecom companies have advanced as a consequence and joined the big data age. Among the most potential machine learning methods for interpreting this deluge of information is computer science, which is employed in a range of business and scholarly research situations. This paper gives a broad overview of technology advancements and big data analytics, as well as possible uses for them in succeeding cellular networks. Apply sophisticated analytics to determine what mobile users' demands are, and then utilise that information to raise the effectiveness of "social wireless channels of communication."The huge, integrated, information machine educational approach it offers, which consists of extracting the features, simulation models, and forecasting enhancements, is particularly noteworthy. The key benefit of the proposed methodology is the logic of robust mathematical methods, issue conceptualization within the context of cellular networks, and reliance on a vast amount of data that represents both the bandwidth as well as other stringent user needs. and the ability to create a procedure. In a communication network, data is normally sent from the transmitter to the receiver across a distance. Using communication systems, the transmitter may be positioned somewhere within a few metres. We will examine existing strategies, their advantages and disadvantages, and potential developments in underwater sensors right now. We had discovered a fresh method of investigation. © 2022 IEEE.

关键词： Information management

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Blockchain-Based Secure e-KYC Solution for Banking System

Blockchain-Based Secure e-KYC Solution for Banking System

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Zoya Tabassum Sobiya Arsheen Khaleel Ahmad Gulcihan Ozdemir Dept. of Computer Science and Information Technology Maulana Azad National Urdu University Hyderabad India Informatics Institute Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Blockchain technology offers a secured, digital, decentralized, ever-growing ledger that keeps records tamper-proof. The distributed ledger means that, even if one node is attacked or hacked, data may be accessed from other nodes, making the system more resistant to attacks and ensuring data availability. The hash function is used to strengthen the data integrity. E-KYC stands for electronic Know Your Customer, a way to verify a customer's identity. The banks frequently require the customer's identity documents for verification. Therefore, they spend an unwanted cost, effort, and abundant time during the KYC process. Sometimes, the information stored locally can be misleading. With unique features, blockchain-enabled e-KYC systems can protect sensitive data, reduce data breach risks, and ensure the integrity and confidentiality of e-KYC procedures. With its distributed ledger, blockchain technology stores e-KYC data across nodes and cryptographic techniques and can effectively increase e-KYC safety. Decentralization and immutability make blockchain technology a strong foundation for securing sensitive customer data. Strong authentication and public key cryptographic methods to protect e-KYC data.

关键词： Resistance Hash functions Costs Distributed ledger Data integrity Data breach Public key cryptography Blockchains Safety Computational intelligence

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Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks 1

Human Activity Recognition in Smart Cities from Smart Watch ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kandpal, Meenakshi Sharma, Bhisham Barik, Rabindra K. Chowdhury, Subrata Patra, Sudhansu Shekhar Dhaou, Imed Ben KIIT Deemed to Be University School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India KIIT Deemed to Be University School of Computer Applications Bhubaneswar India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities. © 2023 IEEE.

关键词： Watches

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Iinuma, Takahiro Ebato, Yudai Nobukawa, Sou Tran, Anh Tu Wagatsuma, Nobuhiko Inagaki, Keiichiro Doho, Hirotaka Yamanishi, Teruya Nishimura, Haruhiko Graduate School of Information and Computer Science Chiba Institute of Technology Chiba Japan Chiba Institute of Technology Department of Computer Science Japan Toho University Faculty of Science Department of Information Science Chiba Japan Chubu University Department of Artificial Intelligence and Robotics Aichi Japan Kouchi University Faculty of Education Teacher Training Division Kochi Japan Osaka Seikei University Faculty of Data Science Osaka Japan Yamato University Faculty of Informatics Osaka Japan

ISBN: (纸本)9798350337020

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications. © 2023 IEEE.

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Prediction of Stroke Risk Within 7-Years Follow Up Using Machine Learning Models

Prediction of Stroke Risk Within 7-Years Follow Up Using Mac...

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2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Tsarapatsani, Konstantina-Helen Sakellarios, Antonis Tsakanikas, Vasilis D. Rudolf, Henrik Trampisch, Hans J. Pezoulas, Vasileios C. Matsopoulos, George K. Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering IoanninaGR 45110 Greece Biomedical Research Institute Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany National Technical University of Athens School of Electrical and Computer Engineering Athens Greece

ISBN: (纸本)9798350383386

A stroke, also known as brain attack, occurs when blood supply to your brain is interrupted. Primary prevention relies on prompt prediction of a stroke. While currently there are several clinical risk scores, machine learning (ML) models seems to be more suitable tools for accurate prediction of stroke events. Therefore, this work focuses on the prediction of stroke within 7 years follow-up in patients who have not suffered from a stroke or TIA event at baseline. LightGBM (LGBM), Extreme Grading Boosting (XGBoost), Support Vector Machine (SVM) and Decision Tree were employed in the getABI dataset, which includes 5,897 participants. The performance of models was calculated by Accuracy (ACC), Sensitivity (SENS), Specificity (SPE) and area under the receiver operating characteristic curve (AUC) of each model. According to the comparison analysis's results, LGBM has been shown to be the most trustworthy algorithm, with accuracy 68 %. Moreover, sex, age, status of peripheral artery disease (PAD), history of myocardial infarction, angina pectoris, amputation and diabetes and pulse status of different arteries can be used as a simple and cost-effective way to predict *** Relevance: A fatal medical emergency, stroke may be anticipated using artificial intelligence, and the sooner it is predicted, the more cerebrovascular disease occurrences can be avoided. © 2023 IEEE.

关键词： Forecasting

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Exploring Music Similarity through Siamese CNNs using Triplet Loss on Music Samples

Exploring Music Similarity through Siamese CNNs using Triple...

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International Research Conference on Smart Computing and Systems Engineering (SCSE)

作者： Gibran Kasif Ganesha Thondilege School of Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

In the rapidly evolving digital music landscape, identifying similarities between musical pieces is essential to help musicians avoid unintended copyright infringement and maintain the originality of their work. However, detecting such similarities remains a complex and computationally challenging problem. A novel approach to address this issue is a song similarity detection system that utilizes a Siamese Convolutional Neural Network (CNN) with Triplet Loss for effective audio input comparison. The model is trained on a custom dataset from WhoSampled, an extensive database of information on sampled music, cover songs, and remixes. The dataset comprises pairs of audio samples and interpolations, making it suitable for the Siamese CNN approach. Incorporating Triplet Loss enhances the model’s performance by learning discriminative features for improved comparison. The performance of this system is assessed using a confidence interval-based metric, achieving a 96.86% accuracy at a 99.7% confidence level in determining the similarity between music samples. The solution provides a helpful tool for musicians to actively compare their creations with existing songs, helping to reduce the likelihood of unintentional plagiarism and possible legal issues.

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MetaSignal: Meta Reinforcement Learning for Traffic Signal Control via Fourier Basis Approximation

TechRxiv

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TechRxiv 2023年

作者： Huang, Shuning Ota, Kaoru Dong, Mianxiong Zhou, Huan Department of Sciences and Informatics Muroran Institute of Technology Hokkaido Muroran Japan College of Computer and Information Technology China Three Gorges University Yichang China

Traffic signal control plans significantly impact transportation system efficiency by regulating traffic conditions at intersections. Adaptive traffic plans that can adjust to real-time road conditions are more effective as a result. Reinforcement learning succeeds at adapting strategies based on feedback derived from the environment, and is thus proficient in dealing with complex traffic scenarios that change dynamically. However, current RL methods rely on significant computational periods to obtain precise functioning mechanisms within the scenarios, posing barriers to their adoption for new scenarios. In addition to directly optimizing the RL model itself to enable fast learning from scratch, another idea is to make the model transferable or reusable with the learned experience. Given the diversity of migration scenarios, the underlying control algorithm should guarantee convergence and endeavor to be parameter-insensitive. From the above concern, we proposed MetaSignal, an efficient meta-reinforcement learning method for traffic signal control. Specifically, our approach utilizes the Fourier basis as the value function approximation in reinforcement learning, distinguishing it from methods like neural network approximation. This linear approximation offers advantages such as convergence facilitation, error bound achievement, and reduced parameter dependence. The meta-learning framework adopts a model-agnostic approach, enabling effective adaptation of the base model to the target scenario with limited training cost. Empirically, the proposed method shows promising and stable performance for traffic signal control through comprehensive comparison experiments in both synthetic and real-world traffic networks. © 2023, CC BY.

关键词： Reinforcement learning

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