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IEEE International Conference on Intelligent Computing and Information systems (ICICIS)

作者： Mohamed L. Elrefai Mahmoud I. Khalil Hazem M. Abbas Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt

Multilingual language models have decreased the barrier between languages, as it will be helpful overcoming many problems, such as sentiment analysis because the importance of this task is to make good decisions and customize products. Obtaining information from one language can help other languages generalize and understand a task more effectively. In this paper, we propose a general method for sentiment analysis of data that includes data from many languages, which enables all applications to use sentiment analysis results in a language-blind or language-independent manner. We performed experiments on two language combinations (English and Arabic) for sentence-level sentiment classification and found that the model with the final setup after adding translations from one language to another and fine-tuning the multilingual language model for Twitter, was the best setup, achieving for two languages and 71.2% and 68.1% f1-score for English and Arabic, respectively.

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Design and Implementation of Low-Cost Smart Monitoring System for Micro-Grid PV Applications Using LoRa Technology

Design and Implementation of Low-Cost Smart Monitoring Syste...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Saadoon, Ghassan Mohammed Ghandour, Ahmad Mnati, Mohannad Jabbar Ali, Adnan Hussein Sabbar, Bayan Mahdi Kareem, Marwa Mahdi Islamic University of Lebanon Faculty of Engineering Department Communication Systems Department Beirut Lebanon Middle Technical University Institute of Technology Baghdad Department of Electrical Technology Baghdad Iraq Al-Mustaqbal University College of Engineering and Engineering Techniques Medical Instrumentation Engineering Techniques Department Babylon Iraq Middle Technical University Technical Management Institute Baghdad Computer Systems Techniques Department Baghdad Iraq

ISBN: (纸本)9798350309140

In this paper, an innovative smart monitoring system has been developed with a low cost for micro-grid photovoltaic systems using LoRa technology. This research addresses traditional monitoring solutions' limitations and explores the advantages of using LoRa technology in micro-grid PV systems. The literature review comprehensively highlights all aspects of micro-grid PV systems and underscores the significance of monitoring for performance enhancement and troubleshooting. The methodology section discusses the proposed system architecture, sensor selection, data collection, LoRa communication use, and data visualization. In the results sections, a real-life view of the effectiveness of a low-cost smart metering system is illustrated concerning issues such as the accuracy of data, transmission range, and energy consumption rate. Examples from real life are used to illustrate how the system improves performance, simplifies maintenance tasks, and helps identify problems in micro-grid PV systems. This research provides insights into exploiting LoRa technology to realize low-cost but efficient smart monitoring solutions in renewable energy microgrids. © 2024 IEEE.

关键词： Strain measurement

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Comparing microbial communities in mucilage and seawater samples: Metagenomic insights into mucilage formation in the Marmara Sea

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Environmental Science and Pollution Research 2024年第48期31卷 58363-58374页

作者： Gundogdu, Aycan Nalbantoglu, Ozkan Ufuk Karis, Gizem Sarikaya, Ilknur Erdogan, Meryem Nisa Hora, Mehmet Aslan, Herdem Department of Microbiology and Clinical Microbiology Faculty of Medicine Erciyes University Kayseri Turkey Erciyes University Melikgazi Kayseri38280 Turkey Department of Computer Engineering Faculty of Engineering Erciyes University Kayseri Turkey Department of Molecular Biology and Genetics Gevher Nesibe Genome and Stem Cell Institute Erciyes University Kayseri Turkey Department of Bioinformatics Systems Biology Institute of Health Sciences Erciyes University Kayseri Turkey Enbiosis Biotechnology Istanbul Turkey Department of Biology Faculty of Science Canakkale Onsekiz Mart University Canakkale Turkey

Marine environments are subject to various naturally occurring phenomena, including marine snow and mucilage. In 2021, the rapid emergence of mucilage in the Marmara Sea raised concerns about its environmental impact. This study investigates the microbial communities in mucilage and seawater samples from the Marmara Sea using metagenomic-scale comparative analyses. The results indicate significant differences in microbial composition and diversity, with mucilage samples showing higher levels of polysaccharide biosynthesis-related enzymes. Over 50% of reads in mucilage samples remained unclassified (dark matter), highlighting unknown microbial taxa. Clean seawater was characterized by a higher presence of Euryarchaeota, Proteobacteria, and Rhodothermaeota, while Chlamydiae and Fusobacteria were dominant in mucilage. The study underscores the necessity for comprehensive metagenomic analyses to understand microbial roles in mucilage formation and persistence. Early detection of microbial shifts could serve as a warning system for mucilage outbreaks, aiding in the development of management strategies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Seawater

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ConvMixer-UNet: A Lightweight Network for Breast Lesion Segmentation in Ultrasound Images

ConvMixer-UNet: A Lightweight Network for Breast Lesion Segm...

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ACS/IEEE International Conference on computer systems and Applications

作者： Sara AbdElhakem Sohier Basiony Marwan Torki Computer and Systems Engineering Department Faculty of Engineering Alexandria University Alexandria Egypt

Breast cancer is one of the most common types of cancer among women. It occurs when abnormal cells in the breast grow and divide uncontrollably. Early diagnosis and treatment are crucial in preventing its spread to the rest of the body. In this paper, we propose a ConvMixer-UNet network for ultrasound image segmentation. The objective is to identify the lesion in the ultrasound image. We design our network that consists of convolutional layers at the early level and ConvMixer layers at the latent level. ConvMixer is an extremely simple and parameter-efficient module that incorporates depthwise and pointwise convolutional layers. This model was evaluated using a breast ultrasound dataset (BUSI); it achieved an improvement in the value of Intersection over Union (IoU). We achieved 68.17% IoU and 80.60% Dice score. These scores are obtained via careful tuning for the network hyperparameters. Quantitative and qualitative comparisons ensure the value of our proposed network. Moreover, ConvMixer-UNet is considered a lightweight network compared to the leading medical segmentation network UNet and its extensions. We show that our network provides a significant reduction in the number of parameters to only 1.77 M parameters, in contrast to UNet which has 31.1 M parameters.

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Achieving a Zero False Negative Rate Unsupervised Detection on Controller Area Network (CAN) Bus with LSTM-Based Autoencoder

Achieving a Zero False Negative Rate Unsupervised Detection ...

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

作者： Karim Kamal Afify Hazem M. Abbas Bassem A. Abdullah Faculty of Engineering Computer and Systems Engineering Ain Shams University Cairo Egypt

The increasing complexity and connectivity of modern automotive systems have introduced new challenges in ensuring the security and reliability of Controller Area Network (CAN) bus communication. Cyber-attacks on CAN bus networks can lead to severe consequences, including vehicle malfunctions and compromise of passenger safety. As a result, there is a pressing need for efficient and effective anomaly detection mechanisms to identify and mitigate potential security breaches. TThis paper presents a practical anomaly detection approach that harnesses the capabilities of autoencoders and Long Short-Term Memory (LSTM) neural networks to fortify CAN bus cybersecurity with a focus on real-world implementation. The proposed method leverages the autoencoder's ability to learn the normal behavior of the CAN bus traffic and identify deviations from it, while the LSTM architecture provides temporal sequence modeling to capture dynamic patterns in the data.

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Deep Left Ventricular Motion Estimation Methods in Echocardiography: A Comparative Study

Deep Left Ventricular Motion Estimation Methods in Echocardi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Sofia Ferraz Miguel Coimbra João Pedrosa Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Faculty of Engineering of the University of Porto (FEUP) Porto Portugal Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Faculty of Sciences of the University of Porto (FCUP) Porto Portugal

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Motion estimation in echocardiography is critical when assessing heart function and calculating myocardial deformation indices. Nevertheless, there are limitations in clinical practice, particularly with regard to the accuracy and reliability of measurements retrieved from images. In this study, deep learning-based motion estimation architectures were used to determine the left ventricular longitudinal strain in echocardiography. Three motion estimation approaches, pretrained on popular optical flow datasets, were applied to a simulated echocardiographic dataset. Results show that PWC-Net, RAFT and FlowFormer achieved an average end point error of 0.20, 0.11 and 0.09 mm per frame, respectively. Additionally, global longitudinal strain was calculated from the FlowFormer outputs to assess strain correlation. Notably, there is variability in strain accuracy among different vendors. Thus, optical flow-based motion estimation has the potential to facilitate the use of strain imaging in clinical practice.

关键词： Training Accuracy Echocardiography Motion estimation Pipelines Noise Strain measurement Reliability Optical flow Strain

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Probability Based Regression Analysis for the Prediction of Cardiovascular Diseases

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computers, Materials & Continua 2023年第6期75卷 6269-6286页

作者： Wasif Akbar Adbul Mannan Qaisar Shaheen Mohammad Hijji Muhammad Anwar Muhammad Ayaz Department of Computer Science NFC Institute of Engineering and TechnologyMultanPakistan Faculty of Computer Science&Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science and Information Technology The Islamia University of BahawalpurRahim Yar Khan CampusPunjabPakistan Faculty of Computers and Information Technology University of TabukTabuk47512Saudi Arabia Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahorePakistan Sensor Networks and Cellular Systems(SNCS)research center University of TabukTabuk47512Saudi Arabia

Machine Learning(ML)has changed clinical diagnostic procedures *** in Cardiovascular Diseases(CVD),the use of ML is indispensable to reducing human *** studies focused on disease prediction but depending on multiple parameters,further investigations are required to upgrade the clinical ***-layered implementation of ML also called Deep Learning(DL)has unfolded new horizons in the field of clinical *** formulates reliable accuracy with big datasets but the reverse is the case with small *** paper proposed a novel method that deals with the issue of less data *** by the regression analysis,the proposed method classifies the data by going through three different *** the first stage,feature representation is converted into probabilities using multiple regression techniques,the second stage grasps the probability conclusions from the previous stage and the third stage fabricates the final *** experiments were carried out on the Cleveland heart disease *** results show significant improvement in classification *** is evident from the comparative results of the paper that the prevailing statistical ML methods are no more stagnant disease prediction techniques in demand in the future.

关键词： Machine learning heart disease cardiac disease deep regression regression learning

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Data Migration from Relational to NoSQL Database: Review and Comparative Study

Data Migration from Relational to NoSQL Database: Review a...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Saadouni, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Ziti, Soumia Department of Computer Science Intelligent Processing Systems and Security Team Mohammed V University Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Faculty of Sciences Mohammed V University Rabat Morocco

ISBN: (纸本)9783031263835

New storage requirements, analysis, and visualization of Big Data, which includes structured, semi-structured, and unstructured data, have caused the developers in the past decade to begin preferring Big Data databases, such as NoSQL, that comes to overcome the boundaries of relational Databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. Due to those benefits ensured by this new technology, quite recently, considerable attention has been paid to data migration from relational Database to NoSQL Database. So, in this context, the purpose of this paper is to provide a comparative study of some recent approaches proposed by various researchers to migrate data from relational to NoSQL databases, as well as discussing and analyzing the results of those proposed techniques. This research allowed us to gain a basic understanding of the contributions and approaches that have already been made, as well as the benefits and limits of each proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Human Emotions Classification Using EEG via Audiovisual Stimuli and AI

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computers, Materials & Continua 2022年第12期73卷 5075-5089页

作者： Abdullah A Asiri Akhtar Badshah Fazal Muhammad Hassan A Alshamrani Khalil Ullah Khalaf A Alshamrani Samar Alqhtani Muhammad Irfan Hanan Talal Halawani Khlood M Mehdar Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajranSaudi Arabia Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Software Engineering University of MalakandDir LowerPakistan Department of Computer Science and Information Systems College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia

Electroencephalogram(EEG)is a medical imaging technology that can measure the electrical activity of the scalp produced by the brain,measured and recorded chronologically the surface of the scalp from the *** recorded signals from the brain are rich with useful *** inference of this useful information is a challenging *** paper aims to process the EEG signals for the recognition of human emotions specifically happiness,anger,fear,sadness,and surprise in response to audiovisual *** EEG signals are recorded by placing neurosky mindwave headset on the subject’s scalp,in response to audiovisual stimuli for the mentioned *** a bandpass filter with a bandwidth of 1-100 Hz,recorded raw EEG signals are *** preprocessed signals then further analyzed and twelve selected features in different domains are *** Random forest(RF)and multilayer perceptron(MLP)algorithms are then used for the classification of the emotions through extracted *** proposed audiovisual stimuli based EEG emotion classification system shows an average classification accuracy of 80%and 88%usingMLP and RF classifiers respectively on hybrid features for experimental signals of different *** proposed model outperforms in terms of cost and accuracy.

关键词： Electroencephalography emotion classification signal processing multilayer perceptron random forest

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Operation of a Genetic Algorithm Using an Adjustment Function 1

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20th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2023

作者： Pinto, Francisco João Department of Study Center Scientific Research and Advanced Training in Computer Systems and Comunication Faculty of Engineering Agostinho Neto University University Campus of the Camama S/N Luanda Angola

ISBN: (数字)9783031383335

ISBN: (纸本)9783031383328

In this work we describe in some details the operation of a genetic algorithm (GA), using an adjustment function to compare solutions and determine which is the best. The three basic processes of GAs are: selection of solutions based on their adjustment or adquate to the environment, reproduction for genes crossover, and mutation, which allows random changes to occur in genes. Through these processes, GAs will find better and better solutions to a problem as species evolve to better adjustment their environments. A basic process of a GA begins by randomly generating solutions or "chromosomes" to the problem. Posteriorly, an iterative process is carried out in which, at each step, the good solutions are selected and the crossing between species is carried out. Occasionally we can have mutations on certain solutions. Through the selection of good solutions in the iterative process, the computer will develop better and better solutions. The results of our experiment show that there is a general improvement over the initial population, both in the total adjustment, as well as the medium and maximum. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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