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Corrigendum to “Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems” Appl. Soft Comput. 132 (2023) 109909

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Applied Soft Computing 2025年 169卷

作者： Hafiz Tayyab Rauf Jiechao Gao Ahmad Almadhor Ali Haider Yu-Dong Zhang Fadi Al-Turjman Centre for Smart Systems AI and Cybersecurity Staffordshire University Stoke-on-Trent United Kingdom Department of Computer Science University of Virginia USA Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia Department of Computer Science University of Gujrat Gujrat Pakistan School of Informatics University of Leicester Leicester LE1 7RH UK Research Center for AI and IoT Faculty of Engineering University of Kyrenia Mersin 10 Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Mersin 10 Turkey

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Immersive Hand Gesture for Virtual Museum using Leap Motion Sensor Based on K-Nearest Neighbor 2

Immersive Hand Gesture for Virtual Museum using Leap Motion ...

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2nd International Conference on computer engineering, Network, and Intelligent Multimedia, CENIM 2019

作者： Sumpeno, Surya Gede Aris Dharmayasa, I. Nugroho, Supeno Mardi Susiki Purwitasari, Diana Institut Teknologi Sepuluh Nopember Computer Engineering Dept Surabaya Indonesia Institut Teknologi Sepuluh Nopember Electrical Engineering Dept Surabaya Indonesia Institut Teknologi Sepuluh Nopember Informatics Dept Surabaya Indonesia

ISBN: (纸本)9781728129655

Virtual museum is a place where users can explore museum collection freely. In this study, we are discussing the 3D interactions presented in a virtual museum application using hand-sensing sensor named Leap Motion. In making 3D interaction, some hand gestures are needed to functions any interact in virtual world. To prevent miss-occurring in 3D interaction, it is necessary to do a hand pattern classification to improve accuracy and make it more precision so as not to reduce the quality of immersion in the virtual world. The classification method used in this study is K-Nearest Neighbor (KNN) classification methods. KNN is a method that is quite popular and simple. The first step is data acquisition processing that is used as training data using Leap Motion Controller which takes vector value data (x, y, z) from each fingertip. Then the data normalization process is carried out to facilitate the next process which is feature extraction process. Features are being extracted including angle value between fingers, angle value between fingertips, angle between fingertips and palms, distance vector between fingertips and palms, and elevation value between fingertips and palms. After that, extracted data are being trained and classified using K-Nearest Neighbor (KNN). © 2019 IEEE.

关键词： Data acquisition

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Impact of artificial intelligence on employees performance in information technology sector

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AIP Conference Proceedings 2023年第1期2587卷

作者： P. Lalitha Kumari Jay Kumar Pandey Karunendra Verma G. Taviti Naidu Ankur Gupta Malik Bader Alazzam Dept. of Computer Science and Engineering Visakha Institute of Engineering & Technology 57th Division Narava Visakhapatnam Andhra Pradesh India Dept. of Electronics and Communication Engineering Shri Ramswaroop Memorial University Dewa Road Barabanki Uttar Pradesh India Chitkara University School of Engineering & Technology Chitkara University Himachal Pradesh India Dept. of Management Dr. L. Bullayya College Visakhapatnam Andhra Pradesh India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

The focus of research is to propose smart approach to evaluate the performance of employees in IT sector. The research made use of artificial intelligence in order to fulfill the objectives. The different attributes that are influencing performance of employees are technical skill, salary, training, working environment, supervision, time schedule. A survey has been conducted where the ranking has been made considering votes of different employees in IT Company. Their person views are obtained considering influencing factors. The data set has been filtered using optimization mechanism and machine learning mechanism has been applied to train the network in order to predict the employee performance considering influencing factors. However there have been several existing researches but those research works have considered limited influencing factor. Moreover there is lack of machine learning approach that could provide suitable solution to build a mechanism that would be capable to design smart model considering influencing factors.

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Trainable discrete feature embeddings for variational quantum classifier

arXiv

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arXiv 2021年

作者： Thumwanit, Napat Lortaraprasert, Chayaphol Yano, Hiroshi Raymond, Rudy Dept. of Computer Science The University of Tokyo Bunkyo-ku Tokyo Japan Dept. of Mechanical Engineering The University of Tokyo Bunkyo-ku Tokyo Japan Dept. of Applied Physics and Physico-Informatics Keio University Kohoku-ku Yokohama Japan IBM Quantum IBM Japan Chuo-ku Tokyo Japan

Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning. © 2021, CC BY.

关键词： Embeddings

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License plate detection and recognition: A study of review

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AIP Conference Proceedings 2023年第1期2834卷

作者： Naaman Omar Subhi R. M. Zeebaree Mohammed A. M. Sadeeq Rizgar R. Zebari Hanan M. Shukur Ahmed Alkhayyat Lailan M. Haji Shakir Fattah Kak Technical College of Administration Information Technology Management Dept. Duhok Polytechnic University Duhok-Iraq Technical College of Engineering Energy Dept. Duhok Polytechnic University Duhok-Iraq Computer Science Department Nawroz University Duhok –Iraq Computer Engineering Dept. Al-Kitab University Kirkuk-Iraq Technical Engineering Dept. Islamic University Najaf Najaf-Iraq Computer Science Dept. University Of Zakho Duhok-Iraq Technical College of Informatics Information Technology Dept. Duhok Polytechnic University Duhok-Iraq

The detection and recognition of automatic license plates (ALPs) is an important task for traffic surveillance and parking management systems, as well as for sustaining the flow of modern civic life. It’s been suggested that there are a variety of ways to detect and recognize ALPs thus far Image processing and machine learning techniques are typically used in these approaches. For object detection and license plate identification, this article reviews most of the approaches. In order to improve the suitability of the input pictures for subsequent processing, many pre-processing approaches including Gaussian filtering and adaptive image contrast augmentation were examined. Deep semantic segmentation networks and deep learning techniques are also utilized to find the license plate areas in the input picture… For example, deep encoder-decoder network architecture and convolutional neural network (CNN) models used to recognize license plate.

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Spatio-temporal Parking behaviour forecasting and analysis before and during COVID-19

arXiv

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arXiv 2021年

作者： Gong, Shuhui Mo, Xiaopeng Cao, Rui Liu, Yu Tu, Wei Bai, Ruibin School of Information Engineering China University of Geosciences Beijing Beijing China School of Computer Science University of Nottingham Ningbo China Ningbo China Dept. of LSGI & SCRI The Hong Kong Polytechnic University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China GD Key Lab. of Urban Informatics Dept. of Urban Informatics Shenzhen University Shenzhen China

Parking demand forecasting and behaviour analysis have received increasing attention in recent years because of their critical role in mitigating traffic congestion and understanding travel behaviours. However, previous studies usually only consider temporal dependence but ignore the spatial correlations among parking lots for parking prediction. This is mainly due to the lack of direct physical connections or observable interactions between them. Thus, how to quantify the spatial correlation remains a significant challenge. To bridge the gap, in this study, we propose a spatial-aware parking prediction framework, which includes two steps, i.e. spatial connection graph construction and spatio-temporal forecasting. A case study in Ningbo, China is conducted using parking data of over one million records before and during COVID-19. The results show that the approach is superior on parking occupancy forecasting than baseline methods, especially for the cases with high temporal irregularity such as during COVID-19. Our work has revealed the impact of the pandemic on parking behaviour and also accentuated the importance of modelling spatial dependence in parking behaviour forecasting, which can benefit future studies on epidemiology and human travel behaviours. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Topological Modes Observed in Si Photonics SSH Integrated Circuit

Topological Modes Observed in Si Photonics SSH Integrated Ci...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： R. Nakamura T. Nakama A. Balčytis T. Ozawa Y. Ota S. Iwamoto H. Ito T. Baba Dept of Electrical &Computer Eng. Yokohama National Univ. Hodogava-ku Yokohama Japan Integrated Photonics and Applications Centre (InPAC) School of Engineering RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku Univ. Sendai Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. Kohoku-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e...

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Recurrent CNN for 3D gaze estimation using appearance and shape cues 29

Recurrent CNN for 3D gaze estimation using appearance and sh...

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29th British Machine Vision Conference, BMVC 2018

作者： Palmero, Cristina Selva, Javier Bagheri, Mohammad Ali Escalera, Sergio Dept. Mathematics and Informatics Universitat de Barcelona Spain Computer Vision Center Campus UAB Bellaterra Spain Dept. Electrical and Computer Eng University of Calgary Canada Dept. Engineering University of Larestan Iran

Gaze behavior is an important non-verbal cue in social signal processing and human-computer interaction. In this paper, we tackle the problem of person- and head pose-independent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on EYEDIAP dataset, further improved by 4% when the temporal modality is included. © 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Human computer interaction

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Spatial Mapping of Diphtheria Vulnerability Level in East Java, Indonesia, using Analytical Hierarchy Process – Natural Break Classification

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Journal of Physics: Conference Series 2021年第1期1803卷

作者： A Fariza A Basofi M D Aryani Informatics and Computer Engineering Dept. Politeknik Elektronika Negeri Surabaya Surabaya Indonesia

Diphtheria is a serious infectious disease induced by the Corynebacterium Diphtheriae bacteria and often causes outbreaks (extraordinary events) in various regions. Based on data from the Ministry of Health, East Java is the biggest benefactor to diphtheria cases in Indonesia. Diphtheria cases in East Java tend to increase, especially in 2018 there were 753 diphtheria cases in 38 districts. Efforts made to prevent, treat, and control diphtheria outbreaks by the government are to analyze the level of susceptibility to diphtheria. This paper proposes a new approach to analyze the level of diphtheria susceptibility using the analytical hierarchy process (AHP) and natural breaks classification in East Java Province, Indonesia. AHP method is used to obtain diphtheria susceptibility values based on seven criteria, such as the number of sufferers, number of deaths, DPT 1, DPT 2, DPT 3 immunization, population density, and humidity. Natural break classification is used to classify the vulnerability values from AHP into three levels of vulnerability, consisting of low, medium, and high. The results of the grouping are displayed in the form of spatial mapping in the form of a web-based geographic information system (web-GIS) on the determination of the diphtheria vulnerability level using the AHP-natural breaks classification. The GVF evaluation for 2016, 2017, and 2018 are respectively 0.66, 0.67, and 0.65 (more than 0.5), which means that the proposed method achieved accurate and significant classification. Spatial-temporal analysis for 2013-2018 also achieves accurate and significant with a GVF value of 0.77. The spatial-temporal analysis can predict the high potential for vulnerability.

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Icons for the Mass: An Approach towards Text Free Smart Interface 1

Icons for the Mass: An Approach towards Text Free Smart Inte...

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1st International Conference on Advances in Science, engineering and Robotics Technology, ICASERT 2019

作者： Ahmed, Eshtiak Hasan, Md. Mahade Faruk, Mirza Omar Hossain, Muhammad Farhad Rahman, Md. Arifur Islam, Ashraful Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh University of Louisiana at Lafayette School of Computing and Informatics Louisiana United States

ISBN: (纸本)9781728134451

A major population of 3rd world countries have problems reading out texts because of the low literacy rate. While smartphones have given them a hope to lead a better life, interacting with them needs a certain level of literacy which a majority of them lack. Given the rapid growth of variation in this user base, it is evident that interactive graphical representations can give a better understanding of contents especially to the children and to the illiterate people. This work emphasizes on making the Google Play App Store more usable as this is the most used app for smartphone users. In this study, new icons have been designed and then compared side by side with 16 existing Google Play Store app category icons to initially find out the legitimacy of the new icons. Further, a survey between smartphone users has been conducted and the results show that the new icons fare better while representing app categories. Based on the survey results and more widely accepted icons, some key factors to design icons have been identified. © 2019 IEEE.

关键词： Smartphones

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