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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Al-Mahmud Shimada, Kazutaka Kyushu Institute of Technology Dept. of Computer Science and Systems Engineering 680-4 Kawazu Iizuka 850-8502 Japan

ISBN: (纸本)9798350316001

Nowadays, more people and entities express opinions on online platforms. With this increasing trend of online opinion mass information, it is impossible to analyze them manually. For this reason, an automatic approach to opinion and sentiment analysis is essential and useful in the real world. Several approaches based on machine learning techniques in sentiment analysis have been proposed. However, machine learning essentially needs massive data to generate a robust model. On the other hand, collecting the training data is costly, especially in low-resource language. To solve this problem, we introduce stepwise learning. Stepwise learning is a technique that improves the model capacity for a target task (i.e., main task) by exploiting auxiliary information from source tasks. It is effective when the data in the target tasks are inadequate. The target task of this paper is sentiment analysis for reviews in the Bangla language. Our proposed method consists of two stages: source and target tasks. For the source task, we train a transformers-based model, such as BanglaBERT, by a large dataset translated into the Bangla language;the original dataset is written in English. For the target task, we retrain and evaluate the model with a relatively small target dataset. We further investigate the effect of opinion expression style of writers between source and target data in stepwise learning. We call it 'nativeness.' The experimental result shows that nativeness has an important role in improving performance. © 2023 IEEE.

关键词： Sentiment analysis

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Performance of the Barzilai-Borwein fixed-step optimization approach in non-invasive microwave imaging 8

Performance of the Barzilai-Borwein fixed-step optimization ...

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8th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2022

作者： Zanoon, Tareq F.A. Arab American University Computer Systems Engineering Dept. Jenin Palestine

ISBN: (数字)9781665488006

ISBN: (纸本)9781665488006

In this paper, the performance of the Barzilai-Borwein non-line search descent is investigated as an inversion strategy for solving the nonlinear inverse scattering problem of Ultra-Wide Band (UWB) microwave tomographic imaging. The appealing feature of reducing the computational time by avoiding extensive line search calculations is tested. The capability of this method in solving cases of high nonlinearity, due to dense and complicated tissue structures, is evaluated and compared to the classical steepest decent, and the conjugate gradient approaches. Cross-sectional slices of glandular human breast tissue structures derived from MRI numerical data phantoms are used and tested with the presence of cancerous tissue. Reconstructed images are presented and discussed with particular attention to the image reconstruction time, and the accuracy of the outcome. Results indicate that the Barzilai-Borwein fixed-step approach outperforms the others in terms of accuracy, overall convergence, and susceptibility to local minima traps, although requiring a greater number of iterations. © 2022 IEEE.

关键词： Image reconstruction

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Self-Optimizing Memory Controllers: Proposing Request-level Scheduling 2

Self-Optimizing Memory Controllers: Proposing Request-level ...

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2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Shawky, Alaa Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Faculty of Engineering Electronics and Communications Dept. Cairo Egypt Siemens Digital Industries Software Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Dept. Cairo Egypt

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Self-Optimizing Memory Controllers present a great potential in the future of memory controllers. As they alleviate the burden of designing an optimal memory scheduling policy, while providing adaptability to different workloads and systems. Previous state-of-art controllers scheduled memory accesses on the command level. This causes the agent to lose sight of the relation between commands and can lead to the issuing of unuseful sequences of precharge/activate of rows or closing banks that should have been left open, and vice versa. In this work we propose a bank-aware self-optimizing memory controller that schedules memory references on the request-level, presenting to the agent the actual problem it needs to tackle. This simplifies the evaluation of system attributes and eliminates the need for correction mechanisms that was needed in previous controllers. With the proposed algorithm a maximum of 18% increase in the data bus utilization is achieved compared to the commonly used FR-FCFS algorithm. © 2022 IEEE.

关键词： Reinforcement learning

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Data Security Enhancement in Cloud Computing by Proposing a DKE Encryption Protocol

Data Security Enhancement in Cloud Computing by Proposing a ...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Ebadinezhad, Sahar Abdulmajed, Halmat Ayub Department of Computer Information Systems Near East University Nicosia Cyprus Near East University Nicosia Cyprus Dept. Engineering and Computer Systems Lebanese French University Erbil Iraq

ISBN: (纸本)9798400708268

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security. © 2023 Copyright held by the owner/author(s).

关键词： Cryptography

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POCS-based Image Compression: An Empirical Examination 7

POCS-based Image Compression: An Empirical Examination

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Do, Truong-Dong Tran, Le-Anh Nguyen, Thanh-Dat Truong, Nghe-Nhan Park, Dong-Chul Le, My-Ha Sejong University Dept. of Aerospace Systems Engineering Seoul Korea Republic of Myongji University Dept. of Electronics Engineering Yongin Korea Republic of Dept. of Research and Development Ocst Co. Ltd. Seoul Korea Republic of Sejong University Dept. of Computer Science & Engineering Seoul Korea Republic of Hcmc University of Technology & Education Dept. of Electrical & Electronics Engineering Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350386073

This paper investigates the applicability of the Pro-jection onto Convex Set (POCS)-based clustering algorithm to image compression tasks. The POCS-based clustering approach treats all data points in a given dataset as non-intersecting convex sets and performs POCS-based parallel projections from each cluster prototype onto corresponding member data points to minimize an objective function and update cluster prototypes. The POCS-based clustering algorithm has been proven to be able to yield promising results against other prevailing clustering approaches in terms of convergence time and clustering error on general clustering tasks. In this study, a comparison of various clustering schemes for image compression applications has been conducted. The evaluations and analyses on various standard test images verify that the POCS-based clustering algorithm can perform competitively against other conventional clustering methods in image compression problems. © 2024 IEEE.

关键词： Unsupervised learning

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Report on the 28th Foundation of software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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Optimizing Mobility IoT Device Networks with Dynamic RIS in mMIMO-Cooperative NOMA 6G systems 1

Optimizing Mobility IoT Device Networks with Dynamic RIS in ...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Hassan, Khalil Hassan, Mohamed Hamid, Khalid Hassan, Elmuntaser Mokhtar, Rania A. Saeed, Mamoon M. Dept. of Comm. Systems Uni. of Science & Technology Omdurman Sudan Dept. of Electrical Eng. Omdurman Islamic Uni. Omdurman Sudan Dept. of Computer Science Al-Neelain University Khartoum Sudan Department of Computer Engineering Taif University Taif Saudi Arabia Yemen

ISBN: (纸本)9798331533557

Advanced technologies like reconfigurable intelligent surfaces (RISs), massive multiple-input multiple-output (mMIMO), and non-orthogonal multiple access (NOMA) are crucial for the upcoming 6G communications landscape, particularly for IoT applications. This study uniquely explores the effects of RIS dynamics on the performance of mMIMO downlink (DL) cooperative NOMA systems in dense IoT environments. It introduces a novel methodology for modeling dynamic traffic distribution and integrating RIS, focusing on key performance metrics such as network capacity, spectral efficiency (SE), latency, and energy efficiency (EE). Various scenarios are evaluated, considering different distributions of IoT devices and signal-to-noise ratios (SNRs). Using simulation software, the research analyzes performance under unstable channel conditions with varying device distances, speeds, and power locations, employing 256-QAM modulation and successive interference cancellation (SIC). The results demonstrate that dynamic RIS significantly enhances capacity, spectrum efficiency, latency, and EE, paving the way for optimized IoT network architecture in future wireless systems. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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Automated, Unsupervised, and Auto-parameterized Inference of Data Patterns and Anomaly Detection

arXiv

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arXiv 2024年

作者： Qin, Qiaolin Li, Heng Merlo, Ettore Lamothe, Maxime Dept. of Computer and Software Engineering Polytechnique Montreal Montreal Canada

With the advent of data-centric and machine learning (ML) systems, data quality is playing an increasingly critical role for ensuring the overall quality of software systems. Data preparation, an essential step towards high data quality, is known to be a highly effort-intensive process. Although prior studies have dealt with one of the most impacting issues, data pattern violations, these studies usually require data-specific configurations (i.e., parameterized) or use carefully curated data as learning examples (i.e., supervised), relying on domain knowledge and deep understanding of the data, or demanding significant manual effort. In this paper, we introduce RIOLU: Regex Inferencer autO-parameterized Learning with Uncleaned data. RIOLU is fully automated, automatically parameterized, and does not need labeled samples. RIOLU can generate precise patterns from datasets in various domains, with a high F1 score of 97.2%, exceeding the state-of-the-art baseline. In addition, according to our experiment on five datasets with anomalies, RIOLU can automatically estimate a data column’s error rate, draw normal patterns, and predict anomalies from unlabeled data with higher performance (up to 800.4% improvement in terms of F1) than the state-of-the-art baseline, even outperforming ChatGPT in terms of both accuracy (12.3% higher F1) and efficiency (10% less inference time). A variant of RIOLU, with user guidance, can further boost its precision, with up to 37.4% improvement in terms of F1. Our evaluation in an industrial setting further demonstrates the practical benefits of *** Codes 68P01 © 2024, CC BY.

关键词： Self-supervised learning

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Analysis and optimization of clinical pathways using timed continuous Petri nets. 28

Analysis and optimization of clinical pathways using timed c...

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28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Arzola, César Mahulea, Cristian Júlvez, Jorge Universidad de Zaragoza Dept. of Computer Science and Systems Engineering Spain Universidad de Zaragoza Aragón Institute for Engineering Research Spain

ISBN: (纸本)9798350339918

This paper presents a novel approach to analyze and optimize healthcare systems based on clinical pathways, using timed continuous Petri nets (TCPNs) under infinite server semantics. TCPNs allow for an efficient continuous-time analysis of the patient flow and resource utilization dynamics of these types of systems. We demonstrate the feasibility and effectiveness of our method through a case study of a hip fracture pathway at the "Lozano Blesa"University Clinical Hospital, in Zaragoza Spain. Additionally, we propose a method to optimize the behavior of the modeled system by taking a control theory approach, which enables the system to achieve maximum throughput more efficiently. Finally, we provide simulation results to demonstrate the effectiveness of the proposed controller in practical settings. © 2023 IEEE.

关键词： Nonlinear analysis

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