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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Anusha, G. Arora, Krishan Sharma, Himanshu Thota, Surya Prakash Verma, Chaman Raboaca, Maria Simona Filote, Constantin Lovely Professional University Dept. of Eee Punjab India Gnits Dept. of Eee Hyderabad India Eötvös Loránd University Faculty of Informatics Dept. of Media and Educational Informatics Budapest Hungary National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania

ISBN: (纸本)9798350321388

Multilevel inverters (MLI) have gained a significant emphasis in the area of renewable energy applications in recent years. The popularity of MLIs is due to their ability to efficiently convert the sustainable sources of energy, such as wind and solar power, into usable electrical energy. One key advantage of using reduced multilevel inverter configurations is that it can help to reduce the system overall cost by decreasing number of components required. The reduction of components in the inverter circuit results in simplification of design and lower expenditure. This paper proposes a fifteen-level modified multilevel inverter with cascaded h-bridge with a minimized switch count. The simulation results of the proposed inverter demonstrate that it is able to effectively attenuate harmonics and with reduced number of components required while still providing good performance. The suggested inverter design is also evaluated on its harmonic reduction characteristics, providing a cleaner output waveform and less distortion. © 2023 IEEE.

关键词： Solar energy

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Approximating Edit Distance in the Fully Dynamic Model

Approximating Edit Distance in the Fully Dynamic Model

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Annual IEEE Symposium on Foundations of computer science

作者： Tomasz Kociumaka Anish Mukherjee Barna Saha Max Planck Institute for Informatics Saarland Informatics Campus Saarbrücken Germany Dept. of Computer Science University of Warwick Coventry UK Dept. of Computer Science & Engineering University of California San Diego US

The edit distance is a fundamental measure of sequence similarity, defined as the minimum number of character insertions, deletions, and substitutions needed to transform one string into the other. Given two strings of length at most n, a simple dynamic programming computes their edit distance exactly in $\mathcal{O}\left(n^{2}\right)$ time, which is also the best possible (up to subpolynomial factors) assuming the Strong Exponential Time Hypothesis (SETH). The last few decades have seen tremendous progress in edit distance approximation, where the runtime has been brought down to subquadratic, to near-linear, and even to sublinear at the cost of approximation. In this paper, we study the dynamic edit distance problem where the strings change dynamically as the characters are substituted, inserted, or deleted over time. Each change may happen at any location of either of the two strings. The goal is to maintain the (exact or approximate) edit distance of such dynamic strings while minimizing the update time. The exact edit distance can be maintained in $\mathcal{O}\left(n \log ^{2} n\right)$ time per update (Charalampopoulos, Kociumaka, Mozes; 2020), which is again tight assuming SETH. Unfortunately, even with the unprecedented progress in edit distance approximation in the static setting, strikingly little is known regarding dynamic edit distance approximation. Utilizing the best near-linear-time (Andoni, Nosatzki; 2020) and sublinear-time (Goldenberg, Kociumaka, Krauthgamer, Saha; 2022) approximation algorithm, an old exact algorithm (Landau and Vishkin; 1988), and a generic dynamic strings implementation (Mehlhorn, Sundar, Uhrig; 1996), it is possible to achieve an $\mathcal{O}\left(n^{c}\right)$-approximation in $n^{0.5-c+o(1)}$ update time for any constant $c \in\left[0, \frac{1}{6}\right]$. Improving upon this trade-off, characterized by the approximation-ratio and update-time product $n^{0.5+o(1)}$, remains wide open. The contribution of this work is a dynami

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Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

arXiv

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arXiv 2024年

作者： Aarotale, Parshuram N. Rattani, Ajita Dept. of Biomedical Engineering Wichita State University United States Dept. of Computer Science and Engineering Uni. of North Texas United States

EMG-based hand gesture recognition uses electromyographic (EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand gestures. This paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of 97% using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of 94.95% using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)). © 2024, CC BY-NC-SA.

关键词： Gesture recognition

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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Context-aware Container Orchestration in Serverless Edge Computing

Context-aware Container Orchestration in Serverless Edge Com...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peiyuan Guan Chen Chen Ziru Chen Lin X. Cai Xing Hao Amir Taherkordi Dept. of Informatics University of Oslo Oslo Norway Dept. of Computer Science and Technology University of Cambridge Cambridge UK Dept. of Electrical and Computer Engineering Illinois Institute of Technology Chicago USA School of Information Science and Technology Northwest Univesity Xi’an China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Adopting serverless computing to edge networks benefits end-users from the pay-as-you-use billing model and flexible scaling of applications. This paradigm extends the boundaries of edge computing and remarkably improves the quality of services. However, due to the heterogeneous nature of computing and bandwidth resources in edge networks, it is challenging to dynamically allocate different resources while adapting to the burstiness and high concurrency in serverless workloads. This article focuses on serverless function provisioning in edge networks to optimize end-to-end latency, where the challenge lies in jointly allocating wireless bandwidth and computing resources among heterogeneous computing nodes. To address this challenge, We devised a context-aware learning framework that adaptively orchestrates a wide spectrum of resources and jointly considers them to avoid resource fragmentation. Extensive simulation results justified that the proposed algorithm reduces over 95% of converge time while the end-to-end delay is comparable to the state of the art.

关键词： Wireless communication Simulation Serverless computing Bandwidth Quality of service Containers Heterogeneous networks Delays Global communication Edge computing

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Advancing Healthcare Diagnostics with a Hybrid AI Model

Advancing Healthcare Diagnostics with a Hybrid AI Model

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Benbrik Jihane Rattal Salma Ghoumid Kamal Ar-Reyouchi El Miloud Dept. Electronics Informatics and Telecommunications ENSAO Mohammed I University Oujda Morocco Dept. Telecommunication and computer science Abdelmalek Essaâdi University Tétouan Morocco

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Artificial intelligence (AI) has significantly impacted the field of healthcare by advancing medical diagnostics and decision-making processes. This paper presents a unified framework that integrates Convolutional Multi-Layer Perceptrons (Conv MLP), Convolutional Neural Networks (ConvNet), and Vision Transformers (ViTs) to address two critical tasks: sentiment analysis of medical reports and medical image recognition. By leveraging the distinct strengths of each architecture, the proposed hybrid model improves performance, effectively balancing computational efficiency, localized feature extraction, and global dependency modeling. The findings demonstrate that the model reduces task-specific errors and enhances convergence rates, achieving superior results in sentiment and image recognition tasks. This study advances the field of medical AI by overcoming the limitations of single-architecture models and establishing the feasibility of hybrid systems for robust, multi-modal applications.

关键词： Deep learning Sentiment analysis Image recognition Computational modeling Decision making Transformers Feature extraction Medical diagnosis Artificial intelligence Medical diagnostic imaging

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An Exploratory Study on Architectural Smell Refactoring Using Large Languages Models

An Exploratory Study on Architectural Smell Refactoring Usin...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Gabriele Pandini Antonio Martini Adela Nedisan Videsjorden Francesca Arcelli Fontana Dept. of Computer Science University of Milano Bicocca Milano Italy Dept. of Informatics University of Oslo Oslo Norway Sustainable Communication Technologies SINTEF Digital Oslo Norway

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

Architectural smells are abundant in codebases and regularly hinder the development of stable and maintainable code. Understanding and removing these elements can consume a huge amount of developers' time, who often need to prioritize implementing new features. This causes a substantial increase in Technical Debt, compromising the scalability and maintainability of the codebases, at time bringing the development to a standstill. Meanwhile, the use of Large Language Models for small error correction is constantly growing, bringing the attention of an ever-wider audience to these technologies. This study explores a first approach to use Large Language Models to suggest refactoring for architectural smells, with a focus on Cyclic Dependencies smells. We study the use of detailed prompt and Retrieval-Augmented Generation (RAG) to enhance LLMs, and we study local vs cloud LLMs. The results are promising, also validated with a series of interviews with students and developers, and highlight how additional and precise context is key to enhance the use of LLMs to propose refactoring suggestions. A multi-agent approach seems to be more suited when increasing the complexity of the smells.

关键词： Codes Software architecture Large language models Scalability Retrieval augmented generation Error correction Complexity theory Interviews

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Recommender System based on Food and Exercise Ontologies to Find the Suitable Fitness Exercise Plan with the Aid of Python 5

Recommender System based on Food and Exercise Ontologies to ...

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5th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2021

作者： Basnayake, Chamali Peiris, Chandika Wickramarathna, Harshani Jayathunga, Poornima Uva Wellassa University Dept. of Computer Science and Informatics Badulla Sri Lanka

ISBN: (纸本)9781665406550

In the modern world, professionals of diverse industrial sectors have severely become victims of Obese and Overweight conditions. Obese and Overweight conditions can be minimized by having proper dietary plans, physical activities and minimizing alcohol-based relaxation. In this research context, we try to address the issue of having poor physical exercise. We guide professionals with suitable exercises to reduce their weight in order to have the required Body Mass Index(BMI). The user body measurements that are recommended by the domain experts to concern such as sex, height, weight, exercise preferences, age, diet details and medical history used to calculate the degree of obesity of each individual. Then the degree of obesity is mapped with the knowledge base along with the predefined rules in order to match respective exercises suitable for the particular individuals that are compatible with the user's medical history. Two ontologies for foods and exercises were developed using Protege 4.3.0 and were retrieved by running Simple Protocol and Resource Description Framework Query Language (SPARQL) queries. Python 3 is used as the backend language for ontology and interface integration. Frontend developed using Tkinter GUI in Python 3 and is presented for the users to ease the interaction with the system. Two ontological files of Foods and Exercises are loaded and tested for consistency using the HermiT reasoner with the aid of Owlready2. Accuracy and Correctness are checked by addressing the competency questions and by domain experts' inspections. © 2021 IEEE.

关键词： Python

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Can Large Language Models Predict the Outcome of Judicial Decisions?

arXiv

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arXiv 2025年

作者： Kmainasi, Mohamed Bayan Shahroor, Ali Ezzat Al-Ghraibah, Amani Computer Science and Engineering Dept. Qatar University Doha Qatar School of Computing and Data Science OUC in partnership with LJMU Doha Qatar Biomedical Engineering Dept. Jordan University of Science and Technology Irbid Jordan

Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.23B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a comprehensive evaluation framework that integrates both quantitative metrics (such as BLEU, ROUGE, and BERT) and qualitative assessments (including Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate that fine-tuned smaller models achieve comparable performance to larger models in task-specific contexts while offering significant resource efficiency. Furthermore, we investigate the impact of fine-tuning the model on a diverse set of instructions, offering valuable insights into the development of a more human-centric and adaptable LLM. We have made the dataset, code, and models publicly available to provide a solid foundation for future research in Arabic legal NLP1 Copyright © 2025, The Authors. All rights reserved.

关键词： Prediction models

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Fairness in Healthcare: Assessing Data Bias and Algorithmic Fairness

Fairness in Healthcare: Assessing Data Bias and Algorithmic ...

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Medical Information Processing and Analysis (SIPAIM), International Symposium on

作者： Farzaneh Dehghani Nikita Malik Joanna Lin Sayeh Bayat Mariana Bento dept. Biomedical Engineering University of Calgary Calgary Canada dept. Information Technology Manipal Institute Of Technology MAHE Manipal India dept. Computer Science University of Calgary Calgary Canada dept. Biomedical Engineering dept. Geomatics Engineering University of Calgary Calgary Canada dept. Biomedical Engineering dept. Electrical and Software Engineering University of Calgary Calgary Canada

ISBN: (数字)9798331528652

ISBN: (纸本)9798331528669

With the wide employment of Artificial Intelligence-based systems in healthcare, there is an increasing need to further quantify and analyze potential biases in data and algorithms, mitigating health disparities. We aim to investigate publicly available healthcare datasets to identify potential biases, and investigate the impact of these biases on the predictive performance of the models. The results indicate that bias in these datasets exerts a negative impact on the fairness of the learned models. In the context of a highly imbalanced dataset, we further investigate the impact of various imbalance handling techniques on both the performance and fairness of the models. This study underscores the importance of early detection of bias to mitigate the risk of introducing such biases in real-world applications, particularly within a sensitive domain, such as healthcare.

关键词： Measurement Prevention and mitigation Decision making Pipelines Medical services Predictive models Stroke (medical condition) Prediction algorithms Reliability Artificial intelligence

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