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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sivagami, S. Sathya, A. Afsin, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Bio Informatics Tamil Nadu Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for early detection and accurate diagnosis. Ultrasound imaging serves as a non-invasive diagnostic tool for identifying breast abnormalities, yet the complexity of these images presents significant interpretation challenges. This study explores the application of Machine Learning (ML) models, specifically Decision Tree (DT) and Random Forest (RF) classifiers, for the classification of breast cancer ultrasound images into benign, malignant, and normal categories. Data augmentation techniques are employed to address class imbalance and enhance model generalization using the Breast Cancer Ultrasonic Image dataset. Among preprocessing steps, pixel values were normalized to provide a consistent input for the models. RF model performed better than DT with an accuracy of 88.75% against an accuracy of 86.25% and those masks for ultrasound images contribute a lot to the overall classification accuracy. In summary, the results of our study can provide support for grand challenges in health and emphasize implications such as the potentiality of ML models to advance breast cancer diagnosis and it implies a necessity for an even further expansion that will allow complete patient outcomes optimization. © 2024 IEEE.

关键词： Radio frequency Accuracy Ultrasonic imaging Training data Predictive models Breast cancer Data models Decision trees Random forests Tumors

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Reduced Number of Parameters for Predicting Post-Stroke Activities of Daily Living Using Machine Learning Algorithms on Initiating Rehabilitation

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Informatica (Slovenia) 2021年第4期45卷 571-581页

作者： Alqudah, Ali Mohammad Al-Hashem, Munder Alqudah, Amin Department of Biomedical Systems and Informatics Engineering Yarmouk University Irbid Jordan Department of Computer Engineering Yarmouk University Irbid Jordan

The estimation of the Barthel Index scale (BI) is a significant method for measuring the performance of Activities Daily Living (ADL), where the prediction of ADL is crucial for providing rehabilitation care management and recovery for patients after stroke, therefore in this paper, nine various Machine Learning (ML) algorithms were implemented in a medical dataset contains 776 records from 313 patients 208 of them are men: 208 and 150 are women with multiple features collected from them for predicting and classifying the BI status as clinical decision support for determining the ADL of post-stroke patients. Meanwhile, we have applied feature selection using the chi-squared test to reduce the number of features in the dataset. The results showed that the Decision Tree (DT), XGBoost (XGB), and AdaBoost (ADB) classifiers performed the highest performance achieved with 100% correctness in terms of accuracy, sensitivity, specificity, error, and Area Under Curve (AUC) on both the full and reduced features datasets. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Patient rehabilitation

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An enhanced donkey and smuggler optimization algorithm for choosing the precise job applicant

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Iran Journal of computer Science 2023年第3期6卷 233-243页

作者： Hasan, Nazir M. Rashid, Tarik A. Alsadoon, Abeer Qosaeri, Ahmed S. Abualigah, Laith Yaseen, Zaher Mundher Computer Science and Engineering Department University of Kurdistan Hawler KR Erbil Iraq Charles Sturt University Sydney Australia Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan Civil and Environmental Engineering Department King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia

Throughout the last few decades, Nature-Inspired Algorithms (NIA) have become very popular in solving real-world problems by getting inspiration from nature. This work suggests the Modified Donkey and Smuggler Optimization (MDSO) algorithm for solving the selection problem to choose suitable job applicants for a specific position. The original Donkey and Smuggler Optimization algorithm (DSO) has two modes: smuggler and donkey mode (non-adaptive and adaptive). In the smuggler mode, the algorithm tries to find the best solutions, which is the path to send the donkey to the destination;once the best path is found, the smuggler will send the donkey through the selected path. The donkey's actions will start when the smuggler mode's best solution is no longer the best one. Certain modifications have been made in the smuggler mode, including replacing the original fitness with a new fitness equation since that method can work more accurately. The Human Resource (HR) department in Korek-telecom has been used as a resource to achieve real-world data to test the original and Modified DSO. Using MDSO, not only will organizations be able to choose suitable job applicants more accurately but also will manage to do so at a faster pace as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Donkey and smuggler algorithms Human resource management MDSO Nature-inspired algorithms Optimization algorithm Swarm intelligence

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Retain and Adapt: Online Sequential EEG Classification With Subject Shift

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4479-4492页

作者： Duan, Tiehang Wang, Zhenyi Shen, Li Doretto, Gianfranco Adjeroh, Donald A. Li, Fang Tao, Cui Mayo Clinic Department of Artificial Intelligence and Informatics JacksonvilleFL32216 United States University of Maryland College ParkMD20740 United States Jd Explore Academy Beijing100101 China West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26506 United States

Large variance exists in Electroencephalogram (EEG) signals with its pattern differing significantly across subjects. It is a challenging problem to perform online sequential decoding of EEG signals across different subjects, where a sequence of subjects arrive in temporal order and no signal data is jointly available beforehand. The challenges include the following two aspects: 1) the knowledge learned from previous subjects does not readily fit to future subjects, and fast adaptation is needed in the process;and 2) the EEG classifier could drastically erase information of learnt subjects as learning progresses, namely catastrophic forgetting. Most existing EEG decoding explorations use sizable data for pretraining purposes, and to the best of our knowledge we are the first to tackle this challenging online sequential decoding setting. In this work, we propose a unified bi-level meta-learning framework that enables the EEG decoder to simultaneously perform fast adaptation on future subjects and retain knowledge of previous subjects. In addition, we extend to the more general subject-agnostic scenario and propose a subject shift detection algorithm for situations that subject identity and the occurrence of subject shifts are unknown. We conducted experiments on three public EEG datasets for both subject-aware and subject-agnostic scenarios. The proposed method demonstrates its effectiveness in most of the ablation settings, e.g. an improvement of 5.73% for forgetting mitigation and 3.50% for forward adaptation on SEED dataset for subject agnostic scenarios. © 2020 IEEE.

关键词： Brain computer interface

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Designing R-CNN Algorithm to Detect Halal Composition of Korean Food and Beverages 8

Designing R-CNN Algorithm to Detect Halal Composition of Kor...

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8th International Conference on Wireless and Telematics, ICWT 2022

作者： Harika, Maisevli Ramdania, Diena Rauda Rahmadika, Sandi Suwastika, Novian Anggis Delilah, Gina Giftia Azmiana Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia UIN Sunan Gunung Djati Department of Informatics Bandung Indonesia Universitas Negeri Padang Department of Informatics Engineering Padang Indonesia Telkom University Department of Informatics Engineering Bandung Indonesia Uin Sunan Gunung Djati Bandung Department of Religious Studies Bandung Indonesia

ISBN: (数字)9781665451222

ISBN: (纸本)9781665451222

The Korean Wave also influences the consumption patterns of Indonesian people, one of which is the culinary field. Thousands of tons of snacks typical of the country of Ginseng have entered Indonesia since 2015. Unfortunately, some products were found to contain non-halal ingredients. Ignorance of Korean food and beverage composition is crucial for Muslim communities who are obliged to consume halal food. An application was created to detect the halal composition of Korean product packaging. The R-CNN algorithm is applied to this application because of its fast detection ability. A total of 150 packaging product data has been involved in this application. A total of 37 data on haram and doubtful products were obtained. Based on data compiled from the Korean Muslim Federation (KMF), Amir Korea, the Korean Muslimah Indonesia group, and the Korean Muslim group. Application testing is done through the black-box testing method. The study results show that the halal detection application can detect the composition of the packaging in Korean products and provide a warning if there are no halal components. © 2022 IEEE.

关键词： Packaging

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Deep Learning Based License Plate Number Recognition for Smart Cities

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computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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Research on the Design Method of Prefabricated Concrete Structure Based on BIM

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computer-Aided Design and Applications 2023年 20卷 148-164页

作者： Yang, Fan Hussain, Saddam Fadhel, Mustafa Abbas Ghafoor, Kayhan Zrar Gannan University of Science and Technology Jiangxi Ganzhou China Department of Civil and Architecture Engineering Kyushu Institute of Technology Fukuoka Kitakyushu8040015 Japan University of Al Muthanna Samawa66001 Iraq Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq

With the gradual development in industrialization, the advent of computer and information technology has evolved in the field of engineering and construction industry. BIM technology has become a valuable part of prefabricated concrete engineering with the advancement of modern technology based on 3D modeling simulation. In order to realize the industrialization and automation of prefabricated concrete structures, this paper analyzes the characteristics of fabricated concrete structure and BIM, combining the two techniques. This work applies BIM to fabricated concrete design and chooses finite element analysis and calculation software ETABS to analyze the structure model. The results show that the mass participation ratio of the model studied in this paper reaches more than 90%, the interlayer displacement angle is less than 1/550, and the modal and shell internal force analysis results meet the requirements. In this paper, modal analysis, shell internal force analysis under live load and structural dynamic analysis under response spectrum load conditions were carried out on the ETABS model converted from the Revit model and the model directly established by ETABS. The ETABS model converted from the Revit model by the data interface plug-in can be used for structural analysis. © 2023, CAD Solutions, LLC. All rights reserved.

关键词： Finite element method

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A Non-Iterative LMI-Based Design of Static Output Feedback Control with L2 Gain Performance for Linear Systems 6

A Non-Iterative LMI-Based Design of Static Output Feedback C...

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6th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Oya, Hidetoshi Asada, Daiki Nagai, Shunya Nakano, Kazushi Tokyo City University Department of Computer Science Tokyo Japan Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan

ISBN: (纸本)9798350380040

In this paper, we present a new LMI-based non-iterative design strategy for static output feedback controllers with L2 Gain Performance for linear systems. In our approach, on the basis of the well-known necessary and sufficient condition for the existence of the static output feedback control for linear systems, the non-iterative LMI-based design procedure for the proposed static output feedback controller is derived. In this paper, we show the LMI-based non-iterative design procedure for static output feedback controllers with L2 Gain Performance and the effectiveness of the proposed static output feedback control is presented through a simple illustrative example. © 2024 IEEE.

关键词： Feedback control

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Monitoring of Heart and Respiration Rate Using a 3D Knitted T-shirt with Textile-based Electrodes

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IFAC-PapersOnLine 2024年第30期58卷 49-54页

作者： Nada Al-azzawi Irem Yunculer Asli Tuncay Atalay Ozgur Atalay Gökhan Ince Faculty of Computer and Informatics Engineering Computer Engineering Department Istanbul Technical University Istanbul 34469 Turkey Faculty of Textile Technologies and Design Textile Engineering Department Istanbul Technical University Istanbul 34437 Turkey

The emergence of e-textiles has facilitated impressive growth in the field of wearable technologies in recent years. Textile-based electrodes enabled long-period electrocardiogram monitoring within the health care field as they are less irritant to the skin and can be naturally embedded into the clothes. Beyond the specific health-oriented applications, physiological signals monitoring is also beneficial during other activities such as daily activities and sports. In this work, a system of a knitted t-shirt embedded with textile electrodes that targets measuring heart rate and respiration rate during various positions and activities is created. The performance of the implemented system is benchmarked against the performance of an off-the-shelf product using root mean square error. It has been shown that the proposed system can achieve good performance with heart rate respiration rate values close to that of the benchmark device.

关键词： E-textile technology smart t-shirt wearable technology heart rate monitoring ECG ECG derived respiration

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Unveiling the Black Box: Investigating the Interplay between AI Technologies, Explainability, and Legal Implications 8

Unveiling the Black Box: Investigating the Interplay between...

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8th International Conference on computer Science and engineering, UBMK 2023

作者： Ardoganyilmaz, Cihan Mengunogul, Berkay Balci, Muhammet Marmara University Computer Engineering Department Istanbul Turkey Northeastern University Informatics Massachusetts United States Bahcesehir University Law Faculty Istanbul Turkey

ISBN: (纸本)9798350340815

Discovering patterns is the process of understanding, while explainability refers to the ability to represent the discovered patterns in a way that the target audience can comprehend. If we adapt the relevant proposition to explainability in law, jurists initially extract the essential components from a concrete case and then establish meaningful connections among these components to reach a conclusion. The second stage, where they explain how they reached the conclusion through the connections established between the components, is referred to as generating reasoning. This study focuses on the first stage of explainability, which involves extracting significant components and establishing basic-level connections with the conclusion. The proposed methodology presents an explainability approach that is independent of the model and can be applied to all classification problems. Furthermore, the best performance in the literature is achieved on the task of predicting the outcomes of Turkish Constitutional Court decisions. © 2023 IEEE.

关键词： Artificial intelligence

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