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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

computers, Materials & Continua 2022年第2期70卷 3311-3319页

作者： Khattab Alheeti Abdulkareem Alaloosy Haitham Khalaf Abdulkareem Alzahrani Duaa Al_Dosary Department of Computer Networking System College of Computer Sciences and Information TechnologyUniversity of AnbarRamadiIraq College of Medicine University of AnbarRamadiIraq Department of Computer Science Faculty of Computer Science and Information TechnologyAlbaha UniversityAlbahaSaudi Arabia College of Computer Sciences and Information Technology University of AnbarRamadiIraq

Self-driving and semi-self-driving cars play an important role in our daily *** effectiveness of these cars is based heavily on the use of their surrounding areas to collect sensitive and vital ***,external infrastructures also play significant roles in the transmission and reception of control data,cooperative awareness messages,and caution *** this case,roadside units are considered one of themost important communication *** distribution of these infrastructures will overburden the spread of self-driving vehicles in terms of cost,bandwidth,connectivity,and radio coverage *** this paper,a new distributed roadside unit is proposed to enhance the performance and connectivity of these ***,this approach is based primarily on k-means to find the optimal location of each roadside *** addition,this approach supports dynamicmobility with a long period of connectivity for each ***,this system can adapt to various locations(e.g.,highways,rural areas,urban environments).The simulation results of the proposed system are reflected in its efficiency and ***,the system can achieve a high connectivity rate with a low error rate while reducing costs.

关键词： Self-driving cars roadside unit autonomous vehicles distributed systems connectivity

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A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features

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Chinese Journal of Electronics 2021年第2期30卷 289-295页

作者： LI Youjiao ZHUO Li LI Jiafeng ZHANG Jing Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Faculty of Information Technology College of Micro-electronics Beijing University of Technology College of Computer Science and Technology Shandong University of Technology

A two-level hierarchical scheme for video-based person re-identification(re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically,given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network(CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover,local features of Local maximal occurrence(LOMO) of the walking cycle are extracted to represent the pedestrian's appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and i LIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose,lighting, and camera viewpoint.

关键词： video surveillance image sequences two-level hierarchical scheme video content local features image representation video-based re-id methods pedestrian walking cycle video signal processing convolutional neural nets local maximal occurrence noisy frames lightweight PCANet complete walking cycle extraction pedestrian appearance information walking-cycle-based person re-id approaches LOMO features lightweight convolutional neural network learning (artificial intelligence) pedestrian appearance model consecutive frames video noises video-based person re-identification iLIDS-VID benchmark dataset LOMO PRID 2011 benchmark dataset video sequence image matching detected key frame object detection

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Study on Reduction of Steady-state Impedance in Magnetic Shielding Type Superconducting Fault Current Limiter

Study on Reduction of Steady-state Impedance in Magnetic Shi...

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International Conferece on Electrical Machines

作者： Mukai, Eiichi Tada, Akio Faculty of Information Sojo University Dept. of Computer System Technology Japan

ISBN: (纸本)9781424417353

A magnetic shielding type superconducting fault current limiter (MSFCL) has undesirable leakage impedance and thus the voltage drop relating to the leakage impedance occurs in the steady-state operation (the standby condition of a fault). Therefore, this paper proposes a divided MSFCL model to reduce the value of the leakage impedance in the steady-state operation. The four-divided model of MSFCL was actually constructed. Then experiments were carried out to demonstrate the advantage of the divided model. Moreover, the divided model was compared with a non-divided model with the same turns of the coils. We confirmed the advantage of the divided model through these experiments.

关键词： Superconducting fault current limiters

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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Corrections to “Effective Gait Abnormality Detection in Parkinson Patients for Multi-Sensors Surveillance system”

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IEEE Access 2025年 13卷 62892-62892页

作者： Abdulwahab Alazeb Mouazma Batool Naif Al Mudawi Mohammed S. Alshehri Sultan Almakdi Nouf Abdullah Almujally Asaad Algarni Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia Department of Computer Science Air University Islamabad Pakistan Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Presents corrections to the paper, (Corrections to “Effective Gait Abnormality Detection in Parkinson Patients for Multi-Sensors Surveillance system”).

关键词： Surveillance

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AI-optimized electrochemical aptasensors for stable, reproducible detection of neurodegenerative diseases, cancer, and coronavirus

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Heliyon 2025年第1期11卷 e41338页

作者： Tayfour Ahmed, Amira Elsir Dhahi, Th.S. Attia, Tahani A. Elhassan Ali, Fawzia Awad Elobaid, Mohamed Elshaikh Adam, Tijjani Gopinath, Subash C.B. Department of Information System College of Science & Arts King Khalid University Asser Mohyel Saudi Arabia Health and Medical Technicals College Southern Technical University Basrah Iraq Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia DEEE Faculty of Engineering University of Khartoum Sudan Imam Abdulrahman Bin Faisal University Applied College Computer Department Saudi Arabia Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Perlis Arau 02600 Malaysia Institute of Nano Electronic Engineering Universiti Malaysia Perlis Perlis Kangar 01000 Malaysia Micro System Technology Centre of Excellence (CoE) Universiti Malaysia Perlis (UniMAP) Perlis Malaysia Center for Global Health Research Saveetha Medical College & Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Tamil Nadu Chennai 602 105 India Faculty of Chemical Engineering & Technology Universiti Malaysia Perlis (UniMAP) Perlis Arau 02600 Malaysia

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration. Sensitivity increases from 60 to 75 % in ordinary aptasensors to 85–95 %, while specificity improves from 70-80 % to 90–98 %. This enhanced performance allows for ultra-low detection limits, such as 10 fM for carcinoembryonic antigen (CEA) and 20 fM for mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), and 1 pM for prostate-specific antigen (PSA) with Differential Pulse Voltammetry (DPV). Similarly, Square Wave Voltammetry (SWV) and potentiometric sensors have detected alpha-fetoprotein (AFP) at 5 fM and epithelial cell adhesion molecule (EpCAM) at 100 fM, respectively. AI integration also enhances reproducibility, reduces false positives and negatives (from 15-20 % to 5–10 %), and significantly decreases response times (from 10-15 s to 2–3 s). These advancements improve data processing speeds (from 10 to 20 min per sample to 2–5 min) and calibration accuracy (<2 % margin of error compared to 5–10 %), while expanding application scope to multi-target biomarker detection. This review highlights how these advancements position AI-optimized electrochemical aptasensors as powerful tools for personalized treatment, point-of-care testing, and continuous health monitoring. Despite a higher cost ($500-$1,500/unit), their enhanced portability and diagnostic performance promise to revolutionize healthcare, environmental monitoring, and food safety, ultimately improving public health outcomes. © 2024

关键词： AI Aptasensors Cancer Coronavirus Electrochemical Integrated biosensor Molecular cells Neurodegenerative

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Designing a New Employee Acceptance Personality Test Applicat on Using Web-based Edward Personal Preference Schedule 2

Designing a New Employee Acceptance Personality Test Applica...

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2nd International Conference on Mechanical, Electronics, computer, and Industrial technology, MECnIT 2018

作者： Sitanggang, Delima Indra, Evta Banjarnahor, Jepri Purba, Windania Turnip, Mardi Girsang, Inka Septianingsih Laia, Yonata Gea, Asaziduhu Department of Information System Faculty of Technology and Computer Sciences Universitas Prima Indonesia Indonesia Department of Information System Faculty of Technology and Computer Sciences Universitas Methodist Indonesia Indonesia

The diversity of personality possessed by humans makes one hesitate to explore the work that interests him. The world of psychology has several test tools that can be used to analyze a person's personality which is the end result of a test that is used to see a person's personality tendency and what type of work is suitable for the interest, where current state and private companies still use manual tests by making questionnaires or a series of questions which will be given to the object which is employee candidate to study. Then the questionnaire is filled in by each object so that the value is collected and recalculated, by using a manual system an employee will wait a very long time to see the *** solve this problem, a system is designed to facilitate the analysis of the personality of prospective employees and to place them in the positions or professions. The system was designed using the Edward Personal Preference Schedule method is one of the personality test tools that is able to reveal 15 personalities. Based on the results of the processed data there are 3 types of personality, the first type is a personality with the highest score, namely autonomy with a score of 18, the second type is affiliation with a score of 17, and the third type is domination with a score of 17 so it can be concluded that respondents X were advised to work in military sections such as the TNI. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Surveys

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Glaucoma identification with retinal fundus images using deep learning: systematic review

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Informatics in Medicine Unlocked 2025年 56卷

作者： Meedeniya, Dulani Shyamalee, Thisara Lim, Gilbert Yogarajah, Pratheepan Department of Computer Science and Engineering University of Moratuwa Moratuwa 10400 Sri Lanka First-Year Division Faculty of Computing Sri Lanka Institute of Information Technology Malabe 10115 Sri Lanka SingHealth AI Health Program SingHealth Group Singapore 168582 Singapore School of Computing Engineering and Intelligent System Ulster University Londonderry BT48 7JL United Kingdom

Glaucoma is a leading cause of blindness, affecting millions of people worldwide. It is a chronic eye condition that damages the optic nerve and, if left untreated, can lead to vision loss and a decreased quality of life. Therefore, there is a need to explore practical and reliable mechanisms for glaucoma identification. This study systematically reviews deep-learning approaches for glaucoma identification using retinal fundus images from 2018 to 2024. Compared to existing survey studies, we cover the latest research, including several public retinal fundus image datasets, and focus on segmentation, classification based on convolutional neural networks and vision transformers, and explainability. The findings of this study, including comparisons of existing methods and key insights, will assist researchers and developers in identifying the most suitable techniques for glaucoma detection. © 2025 The Authors

关键词： Artificial intelligence Classification Deep learning Explainability Glaucoma Segmentation Trustworthiness

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AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

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computers, Materials & Continua 2021年第10期69卷 1039-1055页

作者： Fahad Alotaibi Muhammad Attique Yaser Daanial Khan Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Management and TechnologyLahore54000Pakistan Department of Information Technology University of GujratGujrat50700Pakistan

Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial ***,the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides(“AIP”)are usually very costly and remain ***,before wet-lab investigations,it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development *** anti-inflammatory prediction tools have been proposed in the recent past,yet,there is a space to induce enhancement in prediction performance in terms of precision and *** exceedingly accurate antiinflammatory prediction model is proposed,named AntiFlamPred(“Antiinflammatory Peptide Predictor”),by incorporation of encoded features and probing machine learning algorithms including deep *** proposed model performs best in conjunction with deep *** testing and validation were applied including cross-validation,self-consistency,jackknife,and independent set *** proposed model yielded 0.919 value for area under the curve(AUC)and revealed Mathew’s correlation coefficient(MCC)equivalent to 0.735 demonstrating its effectiveness and ***,the proposed model was also extensively probed in comparison with other existing *** performance of the proposed model also out-performs other existing *** outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based ***,it has the potential to assiduously support medical and bioinformatics research.

关键词： Prediction feature extraction machine learning bootstrap aggregation deep learning bioinformatics computational intelligence antiinflammatory peptides

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Organizational perspective of CRM implementation factors in hospital

Organizational perspective of CRM implementation factors in ...

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2011 International Conference on Research and Innovation in information systems, ICRIIS'11

作者： Monem, Hossein Hussin, Ab Razak Che Behboodian, Navid Faculty of Computer Science and Information System University Technology Malaysia Malaysia

ISBN: (纸本)9781612842950

Customer Relationship Management (CRM) projects implementation have been popular in the organizations and during three consecutive years, CRM related issues were in the top nine lists most concern for the chief executive officers in the USA because failure rate of IT project implementation is quite high. In reality, there have been many failed CRM projects. According to previous literatures, failure rate is between 30% and 70%. Although many investigations had been done on Critical Success Factors (CSF) and were produced long list of CSFs and Critical failure factors (CFF), but guidance in this era is very little and need more study. Specifically, more powerful theoretical models of CRM system implementation are needed for managers to get better understanding of the success factors. To this end, current article focused on organizational CRM implementation success factors in hospital and proposed a model to clarify the potential risk by reviewing and introducing effective success factors for implementing CRM. This paper zoomed on the organizational factors and software and patient factors are considered for further work. Practical investigations are needed to validate proposed model. © 2011 IEEE.

关键词： Hospitals

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