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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Sugimoto, Hitoshi Funatsu, Ryotaro Hirata, Kouichi Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Kawazu 680-4 Iizuka820-8502 Japan Kyushu Institute of Technology Department of Artificial Intelligence Kawazu 680-4 Iizuka820-8502 Japan Softmax Co. Ltd Japan

ISBN: (纸本)9798350324228

A Geckler classification is a criterion how the image is quality based on the number of buccal squamous epithelial (BSE) cells and leukocytes in the Gram stained smears images per 100 × field for sputum. The Geckler classification then determines which of images is valuable to microscope testing for the Gram stained smears images per 1, 000 × field for sputum. In this paper, we predict the Geckler classes by using two methods, image classification and object detection. For the image classification, we predict the Geckler classes from the training images for every Geckler class, without annotating BSE cells and leukocytes. On the other hand, for the object detection, we detect BSE cells and leukocytes from the training images annotated by them and determine the Geckler class by the number of them. Hence, we compare the experimental results of predicting Geckler classes obtained by the image classification with those by the object detection. © 2023 IEEE.

关键词： Object detection

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Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation

引用

Analytical Chemistry 2024年第4期96卷 1538-1546页

作者： Yao, Lantian Guan, Jiahui Li, Wenshuo Chung, Chia-Ru Deng, Junyang Chiang, Ying-Chih Lee, Tzong-Yi Kobilka Institute of Innovative Drug Discovery School of Medicine The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China School of Medicine The Chinese University of Hong Kong Shenzhen 2001 Longxiang Road Shenzhen518172 China Department of Computer Science and Information Engineering National Central University Taoyuan320317 Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://***/lantianyao/ATPfinder/ (data sets and code) and https://***/dbAMP/*** (software). © 2024 American Chemical Society

关键词： Peptides

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Deep Learning Approaches for Stock Price Prediction: A Comparative Study of LSTM, RNN, and GRU Models

Deep Learning Approaches for Stock Price Prediction: A Compa...

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International Multidisciplinary Conference on computer and Energy science (SpliTech)

作者： Suresh Chavhan Praveen Raj Prayas Raj Ashit Kumar Dutta Joel J.P. C. Rodrigues Department of Computer Science and Engineering Indian Institute of Information and Technology Raichur Karnataka India Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh Kingdom of Saudi Arabia Amazonas State University Manaus AM Brazil

ISBN: (数字)9789532901351

ISBN: (纸本)9798350390797

Stock price prediction has been a longstanding challenge in financial markets, and recent advances in deep learning techniques have spurred increased interest in developing accurate and reliable models for forecasting. The comparative study is conducted using historical stock price data from diverse financial markets and periods. The dataset is preprocessed to address issues such as non-linearity, volatility, and noise. Subsequently, the LSTM, RNN, and GRU models are implemented and fine-tuned to optimize their performance in predicting stock prices. Performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are employed to evaluate and compare the predictive accuracy of each model. Additionally, the study investigates the models' abilities to capture long-term dependencies and adapt to changing market conditions. The results of the comparative analysis provide insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction. The findings contribute to the ongoing discourse on the applicability of deep learning techniques in financial forecasting and offer practical recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions. In conclusion, the study not only contributes valuable insights into the relative strengths and weaknesses of LSTM, RNN, and GRU models for stock price prediction but also emphasizes the practical utility of the predictions by visually displaying the differences through charts and graphs. This dual approach advances the understanding of deep learning techniques in financial forecasting and provides actionable recommendations for researchers and practitioners seeking to enhance the precision of stock price predictions.

关键词： Deep learning Adaptation models Visualization Recurrent neural networks Accuracy Predictive models Reliability

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A Code-Switched Arabic-English Sentiment Analysis Approach Based on Deep-Learning

A Code-Switched Arabic-English Sentiment Analysis Approach B...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Tasneem S. Almasah Gamal A. Ebrahim Marwa A. Abdelaal Electrical Engineering Department Faculty of Engineering The British University in Egypt Cairo Egypt Computer and Systems Engineering Department Faculty of Engineering Ain Shams University Cairo Egypt Computer Science Department Faculty of Computers and Information Sadat Academy for Management Sciences Cairo Egypt

Sentiment analysis within Online Social Networks (OSNs) becomes a major challenge. Mainly, because of the large amount of data on social networks and the mix of different languages that can be used in these environments. Due to the lack of existing research and datasets on code-switched Arabic-English sentiment analysis, this study creates a new dataset collected from various online social networks. The data is subjected to several preprocessing steps before being analyzed using various deep learning architectures. Moreover, a thorough evaluation of the preprocessing techniques and the performance of the deep learning models have been conducted. This is achieved using comprehensive Twitter datasets in Arabic and English. The study reveals that individual deep learning architectures, specifically, Convolutional Neural Networks (CNNs), Bidirectional Long Short Term Memory (Bi-LSTM), and LSTM demonstrated superior results compared to their hybrid counterparts. Notably, CNN emerged as the most accurate model with an accuracy of 83%, followed closely by Bi-LSTM at 82.3%. Additionally, the paper presents the deployment of an automated machine learning tool, which is Auto-Keras. Furthermore, it designs an optimized sentiment analysis model that aims to enhance accuracy. This approach leads to an increase in the model accuracy to 86%.

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Revolutionizing Human Assessment: AI-Driven Analysis of SQ, EQ, and IQ

Revolutionizing Human Assessment: AI-Driven Analysis of SQ, ...

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Cyber Resilience (ICCR), International Conference on

作者： Amer Ibrahim Wael Ali Nidal A. Al-Dmour Taher M. Ghazal American University in the Emirates Dubai United Arab Emirates Department of Computer Engineering College of Engineering Mutah University Jordan Computer Science Department Centre for Cyber Physical Systems Faculty of Information Science and Technology Khalifa University Center for Cyber Security Universiti Kebangsaan Malaysia (UKM) Bangi Selangor Malaysia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This study focuses on the estimate and analysis of social quotient (SQ), emotional quotient (EQ), and intellectual quotient (IQ) to investigate the synergy between artificial intelligence (AI) and human competence evaluation. Traditional evaluation procedures confront intrinsic limitations, necessitating a deeper look at AI's ability to revolutionize existing methods. AI's ability to absorb massive quantities of data, comprehend complicated behavioral patterns, and refine the identification of social, emotional, and intellectual qualities opens the door to more nuanced evaluations. This article digs at artificial intelligence AI's crucial position in data collecting, algorithmic processing, and the ethical concerns inherent in this expanding sector. This study intends to explain the progress and consequences of altering our knowledge of human capacities across multiple dimensions by scrutinizing the transformational potential and challenges of incorporating AI into SQ, EQ, and IQ evaluations.

关键词： Ethics Behavioral sciences Appraisal Artificial intelligence Resilience

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Machine Learning in Higher Education using RF and SVM for Pedagogical Enhancement

Machine Learning in Higher Education using RF and SVM for Pe...

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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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Adaptive Manoeuvring Control for Planar Snake Robots in Uncertain Friction Environments

Adaptive Manoeuvring Control for Planar Snake Robots in Unce...

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American Control Conference (ACC)

作者： Hareesh Chitikena Irja Gravdahl Kristin Ytterstad Pettersen Alireza Mohammadi Filippo Sanfilippo Øyvind Stavdahl Shugen Ma Department of Engineering Sciences University of Agder (UiA) Grimstad Norway Department of Engineering Cybernetics Norwegian University of Science and Technology (NTNU) Trond-heim Norway Department of Electrical and Computer Engineering University of Michigan - Dearborn Dearborn MI United States Robotics & Autonomous Systems Thrust Systems Hub HKUST (GZ) China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The locomotion dynamics of a snake robot are greatly influenced by the way it interacts with its surroundings, particularly in terms of friction. Given the variable frictional characteristics of the environments in which holonomic snake robots locomote, this paper introduces a novel friction-adaptive body-shape controller that dynamically adjusts the robot's locomotion parameters in response to changes in friction. One of this paper's contributions is based on deriving a novel linear regression-based representation of the ground friction forces acting on the snake robot, which results in coupled dynamics between the gait tracking and friction parameter estimation errors. Using these coupled dynamics and our proposed friction adaptation law, we prove practical stability of the gait tracking and friction parameter estimation errors in the general case, as well as asymptotic convergence of the gait tracking errors and boundedness of the friction parameter estimation errors in a special case. Simulation results show that the proposed controller is stable and that the gait tracking error and friction parameter estimation errors are converging and bounded.

关键词： Asymptotic stability Parameter estimation Friction Simulation Snake robots Dynamics Stability analysis

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Communication-efficient Vertical Federated Learning via Compressed Error Feedback

arXiv

引用

arXiv 2024年

作者： Valdeira, Pedro Xavier, João Soares, Cláudia Chi, Yuejie CMU IST United States IST NOVA CMU United States Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Systems and Robotics Instituto Superior Técnico Portugal Department of Computer Science NOVA School of Science and Technology

Communication overhead is a known bottleneck in federated learning (FL). To address this, lossy compression is commonly used on the information communicated between the server and clients during training. In horizontal FL, where each client holds a subset of the samples, such communication-compressed training methods have recently seen significant progress. However, in their vertical FL counterparts, where each client holds a subset of the features, our understanding remains limited. To address this, we propose an error feedback compressed vertical federated learning (EF-VFL) method to train split neural networks. In contrast to previous communication-compressed methods for vertical FL, EF-VFL does not require a vanishing compression error for the gradient norm to converge to zero for smooth nonconvex problems. By leveraging error feedback, our method can achieve a O(1/T) convergence rate for a sufficiently large batch size, improving over the state-of-the-art O(1/√T) rate under O(1/√T) compression error, and matching the rate of uncompressed methods. Further, when the objective function satisfies the Polyak-Lojasiewicz inequality, our method converges linearly. In addition to improving convergence, our method also supports the use of private labels. Numerical experiments show that EF-VFL significantly improves over the prior art, confirming our theoretical results. The code for this work can be found at https://***/Valdeira/EF-VFL. © 2024, CC BY.

关键词： Federated learning

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Implementation of a Physically Unclonable Function using LEDs and LDRs 12

Implementation of a Physically Unclonable Function using LED...

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12th International Conference on Modern Circuits and systems Technologies, MOCAST 2023

作者： Hristov, Emil Picos, Rodrigo De Benito, Carol Stavrinides, Stavros G. Arul, Tolga Anagnostopoulos, Nikolaos A. Moner Al Chawa, M. Universitat de les Illes Balears Industrial Engineering Dept. Palma Spain International Hellenic University Physics Department Kavala Greece University of Passau Faculty of Computer Science and Mathematics Passau Germany Technical University Dresden Institute of Circuits and Systems Dresden Germany

ISBN: (纸本)9798350321074

This work introduces a novel Physically Unclonable Function (PUF) based on the characteristics of Light-Emitting Diodes (LEDs) and Light-Detecting Resistors (LDRs) as long as these are combined. Specifically, we profit from the variations in the optical output of an LED caused by process variations, as well as from the variations in LDRs. In this way, our work allows for providing a PUF based on optical components, whose electrical response is a function of the used components. Notice that the electrical response refers not only to the static but also to the dynamical behavior. This way, a unique, double-component system can be easily constructed. © 2023 IEEE.

关键词： Light emitting diodes

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CCT5: A Code-Change-Oriented Pre-Trained Model

arXiv

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arXiv 2023年

作者： Lin, Bo Liu, Yepang Wang, Shangwen Xia, Xin Liu, Zhongxin Mao, Xiaoguang College of Computer Science National University of Defense Technology Changsha China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Zhejiang University Hangzhou China Key Laboratory of Software Engineering for Complex Systems China Research Institute of Trustworthy Autonoumous Systems United Kingdom

Software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or identifying the defect-prone code changes. Considering that the cost of dealing with these tasks can account for a large proportion (typically around 70 percent) of the total development expenditure, automating such processes will significantly lighten the burdens of developers. To achieve such a target, existing approaches mainly rely on training deep learning models from scratch or fine-tuning existing pre-trained models on such tasks, both of which have weaknesses. Specifically, the former uses comparatively small-scale labelled data for training, making it difficult to learn and exploit the domain knowledge of programming language hidden in the large-amount unlabelled code in the wild;the latter is hard to fully leverage the learned knowledge of the pre-trained model, as existing pre-trained models are designed to encode a single code snippet rather than a code change (i.e., the difference between two code snippets). We propose to pre-train a model specially designed for code changes to better support developers in software maintenance. To this end, we first collect a large-scale dataset containing 1.5M+ pairwise data of code changes and commit messages. Based on these data, we curate five different tasks for pre-training, which equip the model with diverse domain knowledge about code changes. We fine-tune the pre-trained model, CCT5, on three widely-studied tasks incurred by code changes and two tasks specific to the code review process. Results show that CCT5 outperforms both conventional deep learning approaches and existing pre-trained models on these tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Domain Knowledge

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