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computer Science, Information technology and Engineering (ICCoSITE), International Conference on

作者： Meilia Nur Indah Susanti Yaya Heryadi Yusep Rosmansyah Widodo Budiharto Computer Science Departement Doctor of Computer Science Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia

Personalized learning has achieved the attention of many researchers in the Education field. Personalized learning is a teaching model in which students (learners) have a central role in the learning process. By using this approach, educational methods, and techniques are customized and adapted to be better suited for each learner, with their unique learning style, background, needs, and previous experiences. Based on what the learners have already learned, subjects have already known, and skills have already developed each student in a personalized learning process will get a "learning plan". This approach is different from a conventional approach or known as the "one size fits all" approach. The challenge of personalized learning is how to connect a learner’s previous knowledge, skills and with learning materials that will link that understanding with new knowledge. This paper presents a novelty technique to implement personalized learning by automating a predicted linkage between a student in higher education and elective courses based on previous learning achievement. In this study, Graph Convolutional Networks (GCNs) are used to address link prediction tasks between student and elective courses. The empirical results showed that the GCN model can be used to predict elective courses for a student with 62.5 % average accuracy.

关键词： Support vector machines Couplings computer science Education computer architecture Predictive models Data models

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Real-Time Autoregressive Forecast of Cardiac Features for Psychophysiological Applications

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IEEE Journal of Biomedical and Health informatics 2025年 PP卷 PP页

作者： Yaldiz, Cem O. Lin, David J. Gazi, Asim H. Cestero, Gabriela Chen, Chuoqi Bracken, Bethany K. Winder, Aaron Lynn, Spencer Sameni, Reza Inan, Omer T. Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Georgia Institute of Technology Department of Biomedical Engineering AtlantaGA30332 United States Charles River Analytics Inc. BostonMA02138 United States Emory University Department of Biomedical Informatics AtlantaGA30322 United States

Forecasting the near-exact moments of cardiac phases is crucial for several cardiovascular health applications. For instance, forecasts can enable the timing of specific stimuli (e.g., image or text presentation in psycholinguistic experiments) to coincide with cardiac phases like systole (cardiac ejection) and diastole (cardiac filling). This capability could be leveraged to enhance the amplitude of a subject's response, prompt them in fight-or-flight scenarios or conduct retrospective analysis for physiological predictive models. While autoregressive models have been employed for physiological signal forecasting, no prior study has explored their application to forecasting aortic opening and closing timings. This work addresses this gap by presenting a comprehensive comparative analysis of autoregressive models, including various forms of Kalman filter-based implementations, that use previously detected R-peak, aortic opening, and closing timings from electrocardiogram (ECG) and seismocardiogram (SCG) to forecast subsequent timings. We evaluate the robustness of these models to noise introduced in both SCG signals and the output of feature detectors. Our findings indicate that time-varying and multi-feature algorithms outperform others, with forecast errors below 2 ms for R-peak, below 3 ms for aortic opening timing, and below 10 ms for aortic closing timing. Importantly, we elucidate the distinct advantages of integrating multi-feature models, which improve noise robustness, and time-varying approaches, which adapt to rapid physiological changes. These models can be extended to a wide range of short-term physiological predictive systems, such as acute stress detection, neuromodulation sensor feedback, or muscle fatigue monitoring, broadening their applicability beyond cardiac feature forecasting. © 2013 IEEE.

关键词： Blood vessels

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Balanced TSP partitioning

arXiv

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arXiv 2025年

作者： Berendsohn, Benjamin Aram Kim, Hwi Kozma, László Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science and Engineering Pohang University of Science and Technology Korea Republic of Institut für Informatik Freie Universität Berlin Germany

The traveling salesman problem (TSP) famously asks for a shortest tour that a salesperson can take to visit a given set of cities in any order. In this paper, we ask how much faster k ≥ 2 salespeople can visit the cities if they divide the task among themselves. We show that, in the two-dimensional Euclidean setting, two salespeople can always achieve a speedup of at least 1/2 + 1/π ≈ 0.818, for any given input, and there are inputs where they cannot do better. We also give (non-matching) upper and lower bounds for k ≥ 3. Copyright © 2025, The Authors. All rights reserved.

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Drought Prediction based Standardized Precipitation Index Using Multilayer Perceptron Model

Drought Prediction based Standardized Precipitation Index Us...

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2024 International Conference on Smart Computing, IoT and Machine Learning, SIML 2024

作者： Prabowo, Muhammad Agung Soekirno, Santoso Ananda, Naufal Asri, Devina Putri Yulizar, David Adi, Suko Prayitno Martarizal Suhandi, Nazori University of Indonesia Faculty of Mathematics and Natural Science Departement of Physics Depok Indonesia Bandung Institute of Technology Departement of Instrumentation and Control Bandung Indonesia Banten Climatology Station Indonesia Meteorology Climatology and Geophysics Agency South Tangerang Indonesia State College of Meteorology Climatology and Geophysics Tangerang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Department of Informatics Palembang Indonesia

ISBN: (纸本)9798350364101

Drought events have an impact on various sectors such as the economic, agricultural, environmental and social sectors. The impact of drought needs to be minimized by designing a drought prediction system, this is an effort to disseminate early warning information based on climate and hydrological aspects. Drought prediction uses Multi-Layer Perceptron (MLP) algorithm model to predict drought based on Standardized Precipitation Index (SPI) for 1 and 3 months. SPI is one of the drought indices calculated through rainfall analysis. Predictions were made using rainfall data obtained from the FY-4A QPE satellite, then processed into monthly rainfall accumulations for the 2019-2020 period. FY-4A QPE data is corrected based on rainfall observation data at the observation rain gauge. The corrected FY-4A QPE data is used to predict drought using the SPI1 (1 Month) and SPI3 (3 Months) indices. SPI1 shows fluctuations and a very small Nash-Sutcliffe Efficiency (NSE) value of -0.11, while SPI3 shows a model that is able to follow the peak and valley fluctuations of the actual value as well as an increase in the NSE value of 0.65 and a decrease in the Root Mean Square Error (RMSE) value of 1.43 in SPI1 to 0.77 in SPI3. Drought modeling using MLP was successfully implemented and it showed that as the time span increases, the SPI prediction performance gets better. © 2024 IEEE.

关键词： Drought

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Spectrum Utilization Improvement for Multi-Channel Cognitive Radio Networks with Energy Harvesting

Spectrum Utilization Improvement for Multi-Channel Cognitive...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Sun, Wendi Liu, Xiaoying Zheng, Kechen Xu, Yang Liu, Jia Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China Xidian University School of Computer Science and Technology Xi'an710071 China Center for Cybersecurity Research and Development National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9781665441582

Motivated by the dilemma in the multi-channel spectrum sensing by the multi-antenna user, this paper focuses on the scheme of spectrum sensing and energy harvesting to coordinate the time scheduling and energy management in the cognitive radio networks (CRNs), where a secondary transmitter (ST) exploits spectrum holes to transmit data through multichannel. To improve spectrum utilization efficiency, we investigate how the ST selects channels for sensing based on the residual energy, and adjusts the time scheduling of energy harvesting and data transmission with respect to the sensing results. To address this problem, we propose an adaptive scheme concerning spectrum sensing, channel selection, energy harvesting, and data transmission (SCED) for the ST. Moreover, we formulate the optimization of spectrum utilization efficiency as a Markov decision process (MDP) problem, which is challenging due to the system space and action space. Furthermore, we solve the MDP problem by a proposed value iteration algorithm. Numerical results show that the spectrum utilization efficiency under the SCED scheme is better than that under other schemes. © 2021 IEEE.

关键词： Energy harvesting

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CURRICULUM REINFORCEMENT LEARNING FOR QUANTUM ARCHITECTURE SEARCH UNDER HARDWARE ERRORS

arXiv

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arXiv 2024年

作者： Patel, Yash J. Kundu, Akash Ostaszewski, Mateusz Bonet-Monroig, Xavier Dunjko, Vedran Danaci, Onur Applied Quantum Algorithms Leiden University Netherlands Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Joint Doctoral School Silesian University of Technology Poland Warsaw University of Technology Institute of Computer Science Poland Lorentz Institute Leiden University Netherlands LIACS Leiden University Netherlands

The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational quantum algorithms (VQAs) offer a potential solution by fixing the circuit architecture and optimizing individual gate parameters in an external loop. However, parameter optimization can become intractable, and the overall performance of the algorithm depends heavily on the initially chosen circuit architecture. Several quantum architecture search (QAS) algorithms have been developed to design useful circuit architectures automatically. In the case of parameter optimization alone, noise effects have been observed to dramatically influence the performance of the optimizer and final outcomes, which is a key line of study. However, the effects of noise on the architecture search, which could be just as critical, are poorly understood. This work addresses this gap by introducing a curriculum-based reinforcement learning QAS (CRLQAS) algorithm designed to tackle challenges in realistic VQA deployment. The algorithm incorporates (i) a 3D architecture encoding and restrictions on environment dynamics to explore the search space of possible circuits efficiently, (ii) an episode halting scheme to steer the agent to find shorter circuits, and (iii) a novel variant of simultaneous perturbation stochastic approximation as an optimizer for faster convergence. To facilitate studies, we developed an optimized simulator for our algorithm, significantly improving computational efficiency in simulating noisy quantum circuits by employing the Pauli-transfer matrix formalism in the Pauli-Liouville basis. Numerical experiments focusing on quantum chemistry tasks demonstrate that CRLQAS outperforms existing QAS algorithms across several metrics in both noiseless and noisy environments. © 2024, CC BY.

关键词： Curricula

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Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

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computers, Materials & Continua 2021年第7期68卷 961-981页

作者： Oday Ali Hassen Sarmad Omar Abter Ansam A.Abdulhussein Saad M.Darwish Yasmine M.Ibrahim Walaa Sheta Department of Computer Techniques Engineering Imam Al-Kadhum College(IKC)WasitIraq University of Information Technology and Communications Media Technology Engineering DepartmentIraq University of Information Technology and Communications Baghdad10081Iraq Department of Information Technology Institute of Graduate Studies and ResearchAlexandria University63 Horreya AvenueEl–ShatbyAlexandria21526Egypt Informatics Research Institute City of Scientific Research and Technology ApplicationsBorg EL Arab21934Egypt

Medical image segmentation has consistently been a significant topic of research and a prominent goal,particularly in computer *** tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy *** prevent or minimize manual segmentation error,automated tumor segmentation,and detection became the most demanding process for radiologists and physicians as the tumor often has complex *** methods for detection and segmentation presently exist,but all lack high *** paper’s key contribution focuses on evaluating machine learning techniques that are supposed to reduce the effect of frequently found issues in brain tumor ***,attention concentrated on the challenges related to level set *** study proposed in this paper uses the Population-based Artificial Bee Colony Clustering(P-ABCC)methodology to reliably collect initial contour points,which helps minimize the number of iterations and segmentation errors of the level-set *** proposed model measures cluster centroids(ABC populations)and uses a level-set approach to resolve contour differences as brain tumors vary as they have irregular form,structure,and *** suggested model comprises of three major steps:first,pre-processing to separate the brain from the head and improves contrast ***,P-ABCC is used to obtain tumor edges that are utilized as an initial MRI sequence *** level-set segmentation is then used to detect tumor regions from all volume slices with fewer *** suggest improved model efficiency compared to state-of-the-art methods for both datasets BRATS 2019 and BRATS *** BRATS 2019,dice progress was achieved for Entire Tumor(WT),Tumor Center(TC),and Improved Tumor(ET)by 0.03%,0.03%,and 0.01%*** BRATS 2017,an increase in precision for WT was reac

关键词： 3D-MRI tumor diagnosis bio-inspired clustering ABC optimization multimodal detection

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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A Cloud-based Distributed Computing Approach for Extracting Molecular Descriptors 6

A Cloud-based Distributed Computing Approach for Extracting ...

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6th International Conference on Algorithms, Computing and Systems, ICACS 2022

作者： Didachos, Christos Kintos, Dionisis Panagiotis Fousteris, Manolis Gerogiannis, Vassilis C. Son, Le Hoang Kanavos, Andreas Department of Computer Engineering & Informatics University of Patras Patras Greece Department of Pharmacy Laboratory of Medicinal Chemistry University of Patras Patras Greece Department of Digital Systems University of Thessaly Larissa Greece Vnu Information Technology Institute Vietnam National University Hanoi Viet Nam Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781450397407

Feature extraction is an initial and essential part for the development of accurate predictive machine learning classifiers. In the research field of drug discovery and development, the usage of molecular descriptors, which can be defined as mathematical representations of molecules' chemical properties, is a challenging task not only for machine learning studies but even for "classical wet lab"approaches. However, a high diversity of these descriptors is required in order to exploit all the available knowledge and, consequently, to maximize the potentially predictive power of approaches that could be applied for the discovery of new bioactive compounds against one or more molecular targets. Furthermore, the representation and normalization of these information is considered a rather time-consuming process. Herein, we present an approach that employs the power of cloud and distributing computing for the extraction, processing and representation of big datasets, leading to the generation of molecular descriptors in a reasonable time frame. © 2022 ACM.

关键词： Big data

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Yogi, Kottala Sri Sharma, Avinash Dankan Gowda, V. Saxena, Rini Barua, Tarkeshwar Mohiuddin, Khalid Dept of Operations Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune Telangana Hyderabad India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India King Khalid University College of Business Department of Business Informatics PO Box-3247 Abha61471 Saudi Arabia

ISBN: (纸本)9798350382723

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area. © 2024 IEEE.

关键词： Internet of things

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