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International Conference on System Theory, control, and computing (ICSTCC)

作者： Ciocan Stefan-Robert Cristian Nicolae Butincu Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents a blockchain-based platform for managing and issuing academic certificates that features security, transparency, non-repudiation and traceability. Relying on the security provided by Ethereum blockchain and using asymmetrical cryptography in certificate generation, combined with certificate revocation mechanisms, the proposed platform offers a reliable, decentralized and secure solution that can be used by academic institutions to streamline the management of academic certificates and reduce bureaucracy.

关键词： Smart contracts Non-repudiation Information age control systems Fraud Reliability Certification

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Algorithmic approach for audio segmentation

Algorithmic approach for audio segmentation

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International Conference on System Theory, control, and computing (ICSTCC)

作者： Vasile-Silviu Sarmasanu Cristian Nicolae Butincu Vasile Ion Manta Andrei Stoleru Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents an algorithmic approach for segmenting news broadcasts produced by television stations. The proposed approach uses advanced signal processing techniques to extract and classify audio segments from Romanian TV news broadcasts. By using derivative of 1-dimensional Gaussian filter and binary classification models, this paper aims to automatically identify and classify news segments into categories such as studio news, field news, commercials and music breaks. The primary objective of this paper is to develop a segmentation methodology optimized for reduced computational memory and quick processing. By leveraging efficient computational techniques, the news segmentation can be performed in a timely manner without compromising accuracy. This optimization is crucial for real-time or near-real-time applications within TV networks, facilitating tasks such as content monitoring and analysis.

关键词： TV Content management Accuracy Computational modeling Signal processing algorithms Signal processing Filtering algorithms Real-time systems Classification algorithms Reliability

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A Review of Evaluation Metrics Algorithmic Used in Scholarly Research Recommender System

A Review of Evaluation Metrics Algorithmic Used in Scholarly...

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International Multidisciplinary Information Technology and engineering Conference (IMITEC)

作者： Gerald Ovono Travos Zuva Temidayo Otunniyi Department of Computer Faculty of Applied and Computer Sciences South Africa Department of Process Control and Computer Systems Faculty of Engineering and Technology VaalUniversity of Technology South Africa

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

The exponential growth of academic publications has made scholarly research recommender systems indispensable tools for researchers. These systems rely on diverse evaluation metrics to assess their effectiveness and relevance. This paper provides a comprehensive review and analysis of these metrics, categorizing them into accuracy, ranking, diversity, user-centric, coverage, temporal, and computational metrics. By employing a mixed-methods approach, combining systematic literature review and empirical analysis, we identify the prevalence and application of these metrics across various scholarly domains such as literature, conferences, and reviewers. Our findings highlight the widespread use of traditional metrics like precision, recall, and Fl-score, while emphasizing the growing importance of novel metrics such as unexpectedness and serendipity, especially in literature and conference recommendations. We also underscore the domain-specific nature of metric selection, with user-centric evaluations more prominent in literature recommendations, and ranking-focused metrics dominant in conference and reviewer recommendations. Furthermore, the study reveals a significant gap in the use of temporal and computational metrics, suggesting key areas for future research. This review offers valuable insights for researchers and practitioners, guiding them in metric selection and highlighting potential directions for enhancing the effectiveness, adaptability, and user satisfaction of scholarly recommender systems.

关键词： Measurement Accuracy Diversity reception Solids Information technology Recommender systems Systematic literature review

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A Machine Learning based Facial Expression and Emotion Recognition for Human computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. This recognition of facial expression is highly possible with the help of machine learning technology. This research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. This algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. The fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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CPU-GPU Parallelism Applied to Quality-Diversity Volume Rendering

CPU-GPU Parallelism Applied to Quality-Diversity Volume Rend...

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International Conference on System Theory, control, and computing (ICSTCC)

作者： Cristian Nicolae Butincu Marius Gavrilescu Department of Computer Science Faculty of Automatic Control and Computer Engineering "Gheorghe Asachi" Technical University of Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

This paper presents the performance analysis and the most significant design decisions taken to improve the runtime performance of a quality-diversity volume rendering framework, focusing on using parallel and distributed computing to optimize specific stages within the framework pipeline. After considering the benefits and trade-offs of several aspects like minimization of execution time, portability and hardware independence, we developed a hybrid CPU-GPU parallel computing model that increased the overall efficiency. Using this model, we achieved significant improvement in runtime speed for critical components of the framework compared to the sequential implementation. Additionally, this model can be applied in a wide range of scenarios, where the performance improvements provided by GPU processing can be complemented by CPU parallelization, with better runtime results.

关键词： Solid modeling Runtime Computational modeling Volume measurement Pipelines Performance gain Parallel processing Feature extraction Rendering (computer graphics) Hardware

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Streamlining Software Release Process and Resource Management for Microservice-based Architecture on multi-cloud 25

Streamlining Software Release Process and Resource Managemen...

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25th International Multi Topic Conference, INMIC 2023

作者： Herath, Isuru Pathum Jayawardena, Samuditha Fadhil, Ahamed Kodagoda, Nuwan Arachchillage, Udara Srimath S.Samaratunge Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Sri Lanka

ISBN: (纸本)9798350317701

The software development process is more flexible with the concept of containerization in the microservice platform. This research is on three key components to resolve problems faced by the developers and DevOps teams in the IT industry. First, the development phase expects a fully automated software release process from to the deployment phase and then optimize processes tailored to Docker, and Kubernetes, in microservice-based applications. Then streamline the process and leverage the container orchestration technologies to monitor the main aspect of the development lifecycle through the multi-cloud deployment on demand of the growth of day-today releases on multi-regions. A centralized monitoring platform is developed to monitor the deployed applications and that provides comprehensive visibility regarding performance and health of microservices. At the stage of scalarization in microservices, Vertical Pod Autoscaling (VPA) and Horizontal Pod Autoscaling (HPA) are available approaches for resource allocation, and they require measuring the minimum and maximum resource limits. As a result, an intelligent resource allocation system is proposed using a combination of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms to cater to dynamic resource allocation, optimizing scalability, and improving cost-efficiency. This research aims to achieve practical insights into the IT industry's automated deployment, managing, scaling, and monitoring of microservice-based applications through the mentioned components. © 2023 IEEE.

关键词： Long short-term memory

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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

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Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization

arXiv

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arXiv 2025年

作者： Pezone, Francesco Barbarossa, Sergio Caire, Giuseppe Di Lorenzo, Paolo Kountouris, Marios Bennis, Mehdi Sapienza University of Rome Department of Computer Control and Management Engineering Italy Technische Universität Berlin Faculty 4 - Electrical Engineering and Computer Science Germany

As digital technologies continue to advance, modern communication networks face unprecedented challenges in handling the vast amounts of data produced daily by connected intelligent devices. Autonomous vehicles, smart sensors, IoT systems etc., are gaining more and more interest and new communication paradigms are needed. This thesis addresses these challenges by combining semantic communication with generative models to optimize image compression and resource allocation in edge networks. Unlike traditional bit-centric communication systems, semantic communication prioritizes the transmission of meaningful data specifically selected to convey the meaning rather than obtain a faithful representation of the original data. The communication infrastructure can benefit of the focus solely on the relevant parts of the data due to significant improvements in bandwidth efficiency and latency reduction. Central to this work is the design of semantic-preserving image compression algorithms, utilizing advanced generative models such as Generative Adversarial Networks and Denoising Diffusion Probabilistic Models. These algorithms compress images by encoding only semantically relevant features and exploiting the generative power at the receiver side. This allows for the accurate reconstruction of high-quality images with minimal data transmission. The thesis also introduces a Goal-Oriented edge network optimization framework based on the Information Bottleneck problem and stochastic optimization, ensuring that communication resources are dynamically allocated to maximize efficiency and task performance. By integrating semantic communication into edge networks, the proposed system achieves a balance between computational efficiency and communication effectiveness, making it particularly suited for real-time applications. The thesis compares the performance of these semantic communication models with conventional image compression techniques, using both classical and semantic-awar

关键词： Image compression

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An Improved Heap-Based Optimizer for Challenging Landscapes and Real-World engineering Applications

SSRN

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SSRN 2024年

作者： Eita, Mohammad A. Elaziz, Mohamed Abd Department of Computer and Control Engineering Faculty of Engineering Tanta University Tanta31733 Egypt Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

In this paper, the exploitation capability and balance of Heap-Based Optimizer (HBO) are improved by making some modifications to propose two variants of the improved heap-based optimizer. To improve the exploitative behavior, HBO is integrated with two well-known algorithm which have an excellent exploitation. The first one is Interior Point (IP) which is a derivative-based optimizer. The second one is the Nelder-Mead (NM) algorithm which is a derivative-free optimizer. Moreover, the two variants maintain the excellent exploratory behavior of HBO. Subsequently, The proposed algorithms perform a superior balance between the exploration and exploitation. The performance of proposed algorithms is evaluated through ninety seven benchmark functions including twenty nine very challenging CEC-BC2017 benchmark functions versus eight highly-cited optimizers including the best algorithm of the CEC-BC-2017 competition (EBO-CMAR). The exploration and exploitation capability of the proposed algorithms is tested using forty four multimodal and twenty four unimodal functions, respectively. In the first two experiments, Friedman mean rank test shows HBO-IP and HBO-NM outperforms and achieve 1st and 2nd ranks, respectively. In the last experiment, twenty nine CEC-BC-2017 benchmark functions are used. Friedman test shows that HBO-IP and HBO-NM secure 2nd and 3rd ranks, respectively, after EBO-CMAR;however, Bonferroni method based multiple comparison test (MCT) shows that the difference in ranks between EBO-CMAR and the proposed algorithms is statistically insignificant. Furthermore, Friedman test shows that the overall ranks of the HBO-IP and HBO-NM are 1st and 2nd for all ninety seven functions. The applicability of the proposed algorithms is shown through solving three engineering problems. The behavior is superior or equivalent to the other optimizers. © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Novel Cryptocurrency Prediction Method Using Optimum CNN

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computers, Materials & Continua 2022年第4期71卷 1051-1063页

作者： Syed H.Hasan Syeda Huyam Hasan Mohammed Salih Ahmed Syed Hamid Hasan Iowa State University AmesUSA University of Texas at Arlington USA Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia

In recent years,cryptocurrency has become gradually more significant in economic regions *** cryptocurrencies,records are stored using a cryptographic *** main aim of this research was to develop an optimal solution for predicting the price of cryptocurrencies based on user opinions from social *** is used as a marketing tool for cryptoanalysis owing to the unrestricted conversations on cryptocurrencies that take place on social media ***,this work focuses on extracting Tweets and gathering data from different sources to classify them into positive,negative,and neutral categories,and further examining the correlations between cryptocurrency movements and Tweet *** paper proposes an optimized method using a deep learning algorithm and convolution neural network for cryptocurrency prediction;this method is used to predict the prices of four cryptocurrencies,namely,Litecoin,Monero,Bitcoin,and *** results of analyses demonstrate that the proposed method forecasts prices with a high accuracy of about 98.75%.The method is validated by comparison with existing methods using visualization tools.

关键词： Cryptocurrency litecoin monero bitcoin ethereum twitter optimal CNN price prediction sentiment analysis

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