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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5666-5675页

作者： Karim, Tasmin Shaon, Md. Shazzad Hossain Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Chen, Li Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh University of Saskatchewan Division of Biomedical Engineering SaskatoonSKS7N 5A9 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSKS7N 5A9 Canada Health Informatics Research Laboratory Department of Computer Science and Engineering Birulia Dhaka Savar1216 Bangladesh Mawlana Bhashani Science and Technology University Group of Bio-photomati Information and Communication Technology Tangail Santosh1902 Bangladesh

Antimicrobial peptides (AMPs) play a vital role in the immune defence systems of various organisms and have garnered significant attention for their potential applications in biotechnology and medicine. There are several approaches to identifying AMPs including clinical isolation and characterization, functional genomics, microbiology techniques, and others. However, these methods are mostly expensive, time-consuming, and require well-equipped labs. To overcome these challenges, machine learning models are a potential solution due to their robustness and high predictive capability with less time and cost. In this study, we explored the efficacy of stacking-based ensemble machine-learning techniques to identify AMPs with higher accuracy and precision. Five distinct feature extraction methods, namely amino acid composition, dipeptide composition, moran autocorrelation, geary autocorrelation, and pseudoamino acid composition, were employed to represent the sequence characteristics of peptides. To build robust predictive models, different traditional machine learning algorithms were applied. Additionally, we developed a novel stacking classifier, aptly named StackAMP, to harness the collective power of these algorithms. Our results demonstrated the exceptional performance of the proposed StackAMP ensemble method in AMP identification, achieving an accuracy of 99.97%, 99.93% specificity, and 100% sensitivity. This high accuracy underscores the effectiveness of our approach, which has promising outcomes for the rapid and accurate identification of AMPs in various biological contexts. This study not only contributes to the growing body of knowledge in the field of AMP recognition but also offers a practical tool with potential applications in drug discovery, biotechnology, and disease prevention. © 2024 IEEE.

关键词： Amino acids

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VLC positioning in low data rate for V2V communication 2024

VLC positioning in low data rate for V2V communication

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12th Euro-American Conference on Telematics and Information Systems, EATIS 2024

作者： Giraldo, Luis Miguel Soler, Joaquin Perez Botella-Mascarell, Carmen Juan, Vicent Girbes Varea, Sandra Roger Torres, Julio Martos Olcina, Raimundo Garcia Electronical Engineering Department Universitat de València Burjassot Valencia46100 Spain Computer Science Department Informatics Department Universitat de València Burjassot Valencia46100 Spain

ISBN: (纸本)9798400717338

The creation and implementation of a system that bridges visible light communication (VLC) with selected identifiers, envisioned as informational beacons, are outlined. This system is adept at controlling LED panels and orchestrating communication between LED lights and the receiving unit, laying the groundwork for vehicle-to-vehicle (V2V) communication systems tailored for precise positioning. The design and assessment of test-bed requirements cater to the anticipation of future V2V optical wireless communication scenarios. Through analysis, it is demonstrated that commercial outdoor lighting can be effectively utilized for V2V positioning, capable of operating under daylight with a data transmission rate of 100 Kbps. Crucially, the system's ability to accurately detect positioning beacons facilitates the localization of vehicles within designated areas of interest, paving the way for the advancement of V2V communication technologies. This comprehensive approach not only validates the feasibility of using VLC for vehicle positioning but also sets a precedent for future developments in V2V communication systems. © 2024 Owner/Author.

关键词： Optical communication

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

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2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al/Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics. © 2023 IEEE.

关键词： Support vector machines

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Handwritten Text Recognition of Letters in Kyrgyz Language 24

Handwritten Text Recognition of Letters in Kyrgyz Language

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25th International Conference on computer Systems and Technologies, CompSysTech 2024

作者： Zhumaev, Ilgiz Shambetova, Burul Isaev, Ruslan Computer Science Department Ala-Too International University Bishkek Kyrgyzstan Faculty of Engineering and Informatics Ala-Too International University Bishkek Kyrgyzstan

ISBN: (纸本)9798400716843

This article deals with a new handwritten dataset, Kyrgyz-MNIST, and recognition of handwritten Kyrgyz letters through the Artificial Neural Network. There were collected 89 023 manuscript samples of 36 Kyrgyz letters. Since 33 of the 36 letters in the Kyrgyz language are similar to the Russian letters, the special letters "H", "Y", and "φ"were studied with special emphasis. The system of handwriting recognition works with Image processing methods and Machine Learning algorithms helped to recognize Kyrgyz manuscripts and convert them into computer letters. As a result, the CNN method recognized the handwriting letters more accurately. In total 36 letters were recognized with an average accuracy of 99.1%. Newly studied, the special letters in Kyrgyz Language are also recognized with more than 99% correct. © 2024 ACM.

关键词： Convolutional neural networks

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SP-DSTS-MIMO Scheme-Aided H.266 for Reliable High Data Rate Mobile Video Communication

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computers, Materials & Continua 2023年第1期74卷 995-1010页

作者： Khadem Ullah Nasru Minallah Durre Nayab Ishtiaque Ahmed Jaroslav Frnda Jan Nedoma Department of Computer Systems Engineering University of Engineering and Technology PeshawarPeshawar25000Pakistan National Centre in Big Data and Cloud Computing University of Engineering and Technology Peshawar(NCBC-UETP)Peshawar25000Pakistan Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026ZilinaSlovakia Department of Telecommunications Faculty of Electrical Engineering and Computer ScienceVSB-Technical University of OstravaOstrava-PorubaCzech Republic

With the ever growth of Internet users,video applications,and massive data traffic across the network,there is a higher need for reliable bandwidth-efficient multimedia *** Video Coding(VVC/H.266)is finalized in September 2020 providing significantly greater compression efficiency compared to Highest Efficient Video Coding(HEVC)while providing versatile effective use for Ultra-High Definition(HD)*** article analyzes the quality performance of convolutional codes,turbo codes and self-concatenated convolutional(SCC)codes based on performance metrics for reliable future video *** advent of turbo codes was a significant achievement ever in the era of wireless communication approaching nearly the Shannon *** codes are operated by the deployment of an interleaver between two Recursive Systematic Convolutional(RSC)encoders in a parallel *** RSC encoders may be operating on the same or different architectures and code *** proposed work utilizes the latest source compression standards H.266 and H.265 encoded standards and Sphere Packing modulation aided differential Space Time Spreading(SP-DSTS)for video transmission in order to provide bandwidth-efficient wireless video ***,simulation results show that turbo codes defeat convolutional codes with an averaged E_(b)/N_(0) gain of 1.5 dB while convolutional codes outperformcompared to SCC codes with an E_(b)/N_(0) gain of 3.5 dBatBit ErrorRate(BER)of 10−*** Peak Signal to Noise Ratio(PSNR)results of convolutional codes with the latest source coding standard of H.266 is plotted against convolutional codes with H.265 and it was concluded H.266 outperform with about 6 dB PSNR gain at E_(b)/N_(0) value of 4.5 dB.

关键词： H.265 RSC turbo codes SCC SP-DSTS BP-CNN BER PSNR

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LIME: Long-Term Forecasting Model for Desalination Membrane Fouling to Estimate the Remaining Useful Life of Membrane 1

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36th International Conference on Industrial, engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2023

作者： Eltanbouly, Sohaila Erradi, Abdelkarim Tantawy, Ashraf Said, Ahmed Ben Shaban, Khaled Qiblawey, Hazim Computer Science and Engineering Department College of Engineering Qatar University Doha Qatar School of Computer Science and Informatics De Montfort University Leicester United Kingdom Chemical Engineering Department College of Engineering Qatar University Doha Qatar

ISBN: (数字)9783031368226

ISBN: (纸本)9783031368219

Membrane fouling is one of the major problems in desalination processes as it can cause a severe drop in the quality and quantity of the permeate water. This paper presents a data-driven approach for long-term forecasting of fouling behavior in membrane-based desalination processes. The proposed Long-term forecastIng ModEl (LIME) consists of two intertwined machine learning models trained separately by historical operating conditions of ultrafiltration for pretreatment of reverse osmosis seawater where transmembrane pressure is used as a fouling indicator. The first model predicts the increase in fouling due to filtration. This output is fed to the second model to predict the fouling reduction due to membrane cleaning. In turn, this output is used as the initial fouling condition for predicting the next filtration cycle. The forecasted fouling is used to estimate the membrane’s remaining useful life (RUL), which ends when cleaning no longer reduces the fouling below a safety threshold. Evaluation results show that the model can predict the membrane fouling for 1400 cycles with an R-squared score of 0.8. Moreover, the RUL is estimated for various thresholds with an average percentage error of 7%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Desalination

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A Survey on Privacy-Enhancing Techniques in the Era of Artificial Intelligence 4th

A Survey on Privacy-Enhancing Techniques in the Era of A...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Athena Research and Innovation Center Industrial Systems Institute Patras Greece Department of Informatics and Computer Engineering University of West Attica Athens Greece

ISBN: (纸本)9783031733437

In the era of Big Data and Artificial Intelligence (AI), the unprecedented scale and complexity of data collection, processing, and analysis pose significant privacy challenges. This paper presents a survey, providing a comprehensive and focused overview of privacy-enhancing technologies (PETs) designed to mitigate these challenges and ensure the protection of sensitive information. We explore cryptographic and non-cryptographic techniques including differential privacy (DP), homomorphic encryption (HE), secure multi-party computation (SMPC), and federated learning (FL). Each of these techniques is examined in terms of its basic principles and advantages. Also, some key challenges and ethical issues for implementing PETs are briefly discussed. Finally, we conclude the survey by denoting our future research directions in the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Federated learning

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Comparison of Transformer Based and Traditional Models on Sentiment Analysis on Social Media Datasets 6

Comparison of Transformer Based and Traditional Models on Se...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Lubis, Arif Ridho Fatmi, Yulia Witarsyah, Deden Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia School of Industrial and System Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350345162

In the field of computers and natural language processing, there is an interesting sub-field, namely sentiment analysis. Currently, the use of social media Twitter to actively communicate between individuals contains various review data, opinion data, and emotional data from discussions of topics between users on social media. So that the data generated by Twitter social media can identify patterns in the data for the field of sentiment analysis. In the sentiment analysis sub-sector, there are approaches, namely transformer-based models and traditional models, the traditional model includes the naïve Bayes algorithm, support vector machine, and regression have weaknesses in overcoming data complexity in Indonesian language tweets on Twitter social media while the transformer model includes the BERT (Bidirectional Encoder Representations from Transformers) algorithm is often applied because it has advantages in overcoming the complexity of Indonesian sentences in tweets obtained from social media and in the process this model goes through a training stage with large amounts of data then is adjusted in the concept of sentiment analysis. © 2023 IEEE.

关键词： bert naïve bayes sentiment analysis social media

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Electrohysterogram Data Augmentation Using Generative Adversarial Network for Pregnancy Outcome Prediction

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IEEE Access 2025年 13卷 92468-92482页

作者： Cheema, Muhammad Omar Din, Zia Mohy Ud Imran, Azhar Khan, Fawad Salam Al-Rawi, Mahmood Basil A. El-Meligy, Mohammed Gul, Jahan Zeb Air University Department of Biomedical Engineering Islamabad44000 Pakistan Air University Faculty of Computing & AI Department of Creative Technologies Islamabad44000 Pakistan King Saud University College of Applied Medical Sciences Department of Optometry Riyadh Saudi Arabia Amman Arab University College of Computer Science and Informatics Amman Jordan Maynooth University Department of Electronic Engineering Ireland

There are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term and preterm pregnancies. However, the effectiveness of machine learning classifiers is hampered by the inherent class imbalance in datasets. In order to overcome this constraint, this study uses a Generative Adversarial Network to produce high-quality synthetic EHG data while maintaining the statistical properties of actual signals. By successfully reducing class imbalance, the enhanced dataset makes it possible for machine learning models to be trained with resilience. Real and synthetic features did not differ significantly, according to statistical validation using a t-test (p > 0.05). With this method, the Random Forest classifier obtained a ROC-AUC of 0.99 and a 10-fold cross-validation accuracy of 98%. The suggested GAN-based method’s simplicity and efficacy demonstrate its promise for resolving class imbalance and developing reliable classification systems for diagnostic applications. © 2013 IEEE.

关键词： Generative adversarial networks

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Performance Comparison of the Invasive Weed Optimization K-Means Algorithm With Particle Swarm Optimization K-Means for Banking Data 6

Performance Comparison of the Invasive Weed Optimization K-M...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Suwirmayanti, Ni Luh Gede Pivin Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Setyaningsih, Emy Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Computer Systems Engineering Faculty of Applied Science Universitas AKPRIND Indonesia Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Banking produces extensive and diverse data, so a clustering process is needed to understand customer behavior patterns and transactions more effectively. This clustering has been widely utilized with the K-Means algorithm due to its simplicity and efficiency. However, this algorithm is limited in determining the optimal cluster center, affecting the accuracy of the clustering results. This study proposes applying two metaheuristic algorithms, Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO), as additional solutions combined with K-Means. These two algorithms were chosen because of their ability to find global solutions and convergence speed to handle large and complex banking data. The results show the superiority of IWOKM compared to PSOKM in terms of SSE and DBI. From the SSE value, IWOKM produces a lower value (3029.77), which indicates that this method is better at minimizing clustering errors and producing more accurate clustering than PSOKM (80007.09). From the DBI value, IWOKM also produces a better value (1.6684) than PSOKM (1.6782), which shows that the clusters produced by IWOKM are more compact and more separated. However, in terms of computation time, PSOKM is more efficient, with a faster average computation time. Although IWOKM produces better clustering quality, PSOKM offers advantages in terms of processing speed. This finding confirms that the selection of the IWO algorithm is more appropriate for use on datasets with characteristics such as the German Credit Dataset. © 2024 IEEE.

关键词： Invasive weed optimization

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