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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Basavaraja, V. Shivakumara, Palaiahnakote Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Department of Studies in Computer Science University of Mysore Mysore Karnataka India Faculty of Computer System and Information Technology University of Malaya Kuala Lumpur Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University Nanjing China University of Technology Sydney Australia

ISBN: (纸本)9781728128610

Real-time applications of handwriting analysis have increased drastically in the fields of forensic and information security because of accurate cues. One of such applications is human age estimation based on handwriting for the purpose of immigrant checking. In this paper, we have proposed a new method for age estimation using handwriting analysis using Hu invariant moments and disconnectedness features. To make the proposed method robust to both ruled and un-ruled documents, we propose to explore intersection point detection in Canny edge images of each input document, which results in text components. For each text component pair, we propose Hu invariant moments for extracting disconnectedness features, which in fact measure multi-shape components based on distance, shape and mutual position analysis of components. Furthermore, iterative k-means clustering is proposed for the classification of different age groups. Experimental results on our dataset and some standard datasets, namely, IAM and KHATT, show that the proposed method is effective and outperforms the state-of-the-art methods. © 2019 IEEE.

关键词： Security of data

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Requirements, Deployments, and Challenges of LoRa Technology: A Survey

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Computational intelligence and neuroscience 2023年第1期2023卷 5183062页

作者： Muhammad Ayoub Kamal Muhammad Mansoor Alam Aznida Abu Bakar Sajak Mazliham Mohd Su'ud Malaysian Institute of Information Technology (MIIT) Universiti Kuala Lumpur Kuala Lumpur 50250 Malaysia. Institute of Business and Management Karachi 75190 Pakistan. Riphah Institute of System Engineering (RISE) Faculty of Computing Riphah International University Islamabad 46000 Pakistan. Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia. Faculty of Engineering and Information Technology School of Computer Science University of Technology Sydney Australia. Malaysian France Institute (MFI) Universiti Kuala Lumpur Kuala Lumpur 50250 Malaysia.

LoRa is an ISM-band based LPWAN communication protocol. Despite their wide network penetration of approximately 20 kilometers or higher using lower than 14 decibels transmitting power, it has been extensively documented and used in academia and industry. Although LoRa connectivity defines a public platform and enables users to create independent low-power wireless connections while relying on external architecture, it has gained considerable interest from scholars and the market. The two fundamental components of this platform are LoRaWAN and LoRa PHY. The consumer LoRaWAN component of the technology describes the network model, connectivity procedures, ability to operate the frequency range, and the types of interlinked gadgets. In contrast, the LoRa PHY component is patentable and provides information on the modulation strategy which is being utilized and its attributes. There are now several LoRa platforms available. To create usable LoRa systems, there are presently several technical difficulties to be overcome, such as connection management, allocation of resources, consistent communications, and security. This study presents a thorough overview of LoRa networking, covering the technological difficulties in setting up LoRa infrastructures and current solutions. Several outstanding challenges of LoRa communication are presented depending on our thorough research of the available solutions. The research report aims to stimulate additional research toward enhancing the LoRa Network capacity and allowing more realistic installations.

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Deep learning insights into spatial patterns of stable isotopes in Iran’s precipitation: a novel approach to climatological mapping

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Isotopenpraxis Isotopes in Environmental and Health Studies 2024年第4期60卷

作者： Mojtaba Heydarizad Rogert Sori Masoud Minaei Hamid Ghalibaf Mohammadabadi Elham Mahdipour a State Key Laboratory of Marine Geology Tongji University Shanghai People’s Republic of China b Environmental Physics Laboratory (EPhysLab) Centro de Investigación Mariña Universidade de Vigo Ourense Spain c Department of Geography Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Irand Geographic Information Science/System and Remote Sensing Laboratory (GISSRS: Lab) Faculty of Letters and Humanities Ferdowsi University of Mashhad Mashhad Iran e Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran f Computer Engineering Department Khavaran Institute of Higher Education Mashhad Iran

ABSTRACTStable isotope techniques are precise methods for studying various aspects of hydrology, such as precipitation characteristics. However, understanding the variations in the stable isotope content in precipitation is challenging in Iran due to numerous climatic and geographic factors. To address this, forty-two precipitation sampling stations were selected across Iran to assess the fractional importance of these climatic and geographic parameters influencing stable isotopes. Additionally, deep learning models were employed to simulate the stable isotope content, with missing data initially addressed using the predictive mean matching (PMM) method. Subsequently, the recursive feature elimination (RFE) technique was applied to identify influential parameters impacting Iran’s precipitation stable isotope content. Following this, long short-term memory (LSTM) and deep neural network (DNN) models were utilized to predict stable isotope values in precipitation. Interpolated maps of these values across Iran were developed using inverse distance weighting (IDW), while an interpolated reconstruction error (RE) map was generated to quantify deviations between observed and predicted values at study stations, offering insights into model precision. Validation using evaluation metrics demonstrated that the model based on DNN exhibited higher accuracy. Furthermore, RE maps confirmed acceptable accuracy in simulating the stable isotope content, albeit with minor weaknesses observed in simulation maps. The methodology outlined in this study holds promise for application in regions worldwide characterized by diverse climatic conditions.

关键词： Deep learning DNN isotope hydrology LSTM Iran precipitation stable isotopes simulation

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Research on E-book Text Copyright Protection and Anti-tampering Technology

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International Journal of Network Security 2021年第5期23卷 739-749页

作者： Chou, Yung-Chen Anggriani, Kurnia Wu, Nan-I Hwang, Min-Shiang iSchool Feng Chia University China Department of Computer Science & Information Engineering Asia University 500 Lioufeng Rd Wufeng TaichungTaiwan41354 China Faculty of Engineering University of Bengkulu Indonesia Department of Digital Multimedia Lee-Ming Institute of Technology No.2-2 Lijhuan RdTaishan Township Taipei County 243 Taiwan Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan China

The emergence of e-books makes reading more colorful. Unlike paper books, e-books provide multimedia supplementary content such as hyperlinks, music, videos, and pictures. Readers can have a lot of fun through e-book reading. At present, the development and use of e-books have not yet become widespread. One of the most important factors is the challenge of maintaining the copyright of e-book content. How to ensure that the copyright of e-books is well controlled has become a significant issue. First of all, after understanding the presentation of e-book text, we will develop and use text encoding and HTML attribute settings to hide the digital watermark in the ebook text. Secondly, we also use different text encoding fonts to encode symbols and strengthen the watermark through various embedding methods;third, we use digital signature technology to generate verification codes for the text content of the e-book. And we use confidential information sign-in technology to sign the digital signature into the content of the e-book file to facilitate the verification of the integrity of the text content in the future. Since e-books use XML to integrate multimedia resources, we have developed validation information that effectively generates XML and multimedia resources. Furthermore, we develop effective embedding technology to embed the verification code into the XML of the e-book. In addition, we also use the embeddable Javascript function of EPub 3 to develop technologies to prevent tampering with e-book content. © 2021, International Journal of Network Security. All Rights Reserved.

关键词： XML

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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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EEG Neurofeedback Training Among Adult with Attention Deficit: A Review Article

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IOP Conference Series: Materials science and Engineering 2021年第1期1077卷

作者： Eshrak Alaros Dini Oktarina Dwi Handayani Hamwira Yaacob Muharman Lubis School of Computer Science & Engineering Faculty of Innovation & Technology Taylor's University Selangor Malaysia Computer Science Department Kulliyyah of Information and Communication Technology International Islamic University Malaysia Selangor Malaysia Information System Faculty of Industrial Engineering Telkom University Indonesia

Neurofeedback is considered a promising therapy for boosting cognitive skills, which 10% of the US medical sector is using as a new training method among patients. Attention is a significant mechanism for learning and is essential for life in general. As the person grows up the level of responsibilities also increases, which needs high concentration in doing normal activities. To succeed in any activity, neurofeedback therapy needs to check brain waves to locate the problem in the brain and train it. This paper aims to review the attention deficit treatment based on neurofeedback using a systematic review method. The results found that the common features are the area of EEG (Frontal, central) in the brain, type of tools and software, time of training and the impact upon the trainer. Neurofeedback showed improvement of brain attention-ability for a longer time as well as reduction in recovery time in case of a disturbance interruption.

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The Localized Subtraction Approach For EEG and MEG Forward Modeling

arXiv

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arXiv 2023年

作者： Höltershinken, Malte B. Lange, Pia Erdbrügger, Tim Buschermöhle, Yvonne Wallois, Fabrice Buyx, Alena Pursiainen, Sampsa Vorwerk, Johannes Engwer, Christian Wolters, Carsten H. Institute for Biomagnetism and Biosignalanalysis University of Münster Münster Germany Institute of Medical Informatics University of Münster Münster Germany Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience University of Münster Münster Germany INSERM U1105 Research Group on Multimodal Analysis of Brain Function Jules Verne University of Picardie Amiens France Pediatric functional exploration of the nervous system department CHU Picardie Amiens France Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Computing Sciences Unit Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Institute of Electrical and Biomedical Engineering Private University for Health Sciences Medical Informatics and Technology Hall in Tyrol Austria Faculty of Mathematics and Computer Science University of Münster Münster Germany

In FEM-based EEG and MEG source analysis, the subtraction approach has been proposed to simulate sensor measurements generated by neural activity. While this approach possesses a rigorous foundation and produces accurate results, its major downside is that it is computationally prohibitively expensive in practical applications. To overcome this, we developed a new approach, called the localized subtraction approach. This approach is designed to preserve the mathematical foundation of the subtraction approach, while also leading to sparse right-hand sides in the FEM formulation, making it efficiently computable. We achieve this by introducing a cut-off into the subtraction, restricting its influence to the immediate neighborhood of the source. In this work, this approach will be presented, analyzed, and compared to other state-of-the-art FEM right-hand side approaches. Furthermore, we discuss how to arrive at an efficient and stable implementation. We perform validation in multi-layer sphere models where analytical solutions exist. There, we demonstrate that the localized subtraction approach is vastly more efficient than the subtraction approach. Moreover, we find that for the EEG forward problem, the localized subtraction approach is less dependent on the global structure of the FEM mesh, while the subtraction approach is sensitive to the mesh structure even at a distance to the source. Additionally, we show the localized subtraction approach to rival, and in many cases even surpass, the other investigated approaches in terms of accuracy. Especially for the MEG forward problem, where Biot-Savart’s law is employed, we show the localized subtraction approach and the subtraction approach to produce highly accurate approximations of the volume currents close to the source. The localized subtraction approach thus reduces the computational cost of the subtraction approach to an extent that makes it usable in practical applications without sacrificing the rigorousness and

关键词： Mesh generation

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Automatic Features Extraction Using Autoencoder in Intrusion Detection system

Automatic Features Extraction Using Autoencoder in Intrusion...

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2018 International Conference on Electrical Engineering and computer science, ICECOS 2018

作者： Kunang, Yesi Novaria Nurmaini, Siti Stiawan, Deris Zarkasi, Ahmad Jasmir, Firdaus Intelligent System Research Group Faculty of Computer Science Department Universitas Sriwijaya Palembang Indonesia Computer Networking Information System Faculty of Computer Science Departement Universitas Sriwijaya Palembang Indonesia

ISBN: (纸本)9781538657201

Intrusion Detection system (IDS) can detect attacks by analysing the patterns of data traffic in the network. With a large amount of data that is processed in the IDS, then need to do a feature extraction to reduce the computational cost of processing raw data in IDS. Feature extraction will transform features to the lower dimension to accelerate the learning process and improve the accuracy. This research on automatic feature extraction using simple autoencoder and SVM to classify attacks on IDS. We use various functions activation and loss to see how far this feature extraction feature can improve accuracy. We use Datasets KDD Cup' 99 NSL-KDD and to evaluate the effectiveness of the mechanisms of detection after extraction features process. In the proposed model, the activation functions autoencoder hyperparameter ReLU activation and loss function cross-entropy gives best accuracy value than other functions. © 2018 IEEE.

关键词： Feature extraction

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Evaluation of The Application of Hospital Management information system (SIMRS) in RSUD Dr. Kanujoso Djatiwibowo Using The HOT-Fit Method

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Journal of Physics: Conference Series 2021年第1期1726卷

作者： Adani Setiorini Sri R Natasia Yuyun Tri Wiranti Dean A Ramadhan Department of Information System Majoring in Mathematics and Information Technology Kalimantan Institute of Technology Balikpapan Indonesia Department of Computer Science Faculty of Mathematics and Natural Sciences IPB University Bogor Indonesia

Hospital Management information system (SIMRS) is a system that can process and integrate the entire hospital service process flow in the form of a network of coordination, reporting and administrative procedures to obtain accurate data and information. At the RSUD Dr. Kanujoso Djatiwibowo (RSKD) of Balikpapan City, the application of SIMRS experienced problems such as the high number of troubleshooting cases and the redundancy of patient medical record numbers. This case is considered to inhibit the performance of hospital services related to efficiency. Based on this problem, the evaluation of SIMRS in the RSKD was carried out by using the constructed Human, Organization, Technology-Fit (HOT-Fit) model. This model involves eight variables which consists of system Quality, information Quality, and Service Quality, system Use, User Satisfaction, Structure, and Environment, and Net Benefits. The results indicate that the proposed model either directly or indirectly affects the benefits obtained from implementing the SIMRS. Further implications of the results toward improvement of the system are provided.

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Empirical study of sperm swarm optimization algorithm

Empirical study of sperm swarm optimization algorithm

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作者： Shehadeh, Hisham A. Ahmedy, Ismail Idris, Mohd Yamani Idna Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

This paper gives an empirical study to estimate the performance of our proposed optimization method called Sperm Swarm Optimization (SSO). The SSO is evaluated frequently with different mathematical benchmark models utilized in the scope of optimization. Various asymmetric parameters and settings are chosen for these benchmark functions. The acquired results are compared with the results of four methods, such as Genetic Algorithms (GA), Parallel Genetic Algorithm (PGA), Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO). The outcomes present that the proposed approach outperforms other approaches in terms of quality of result because of using the technique of inherently continuous to update the sperm location. In addition, it uses different types of mutations which are utilized to increase the method convergence. © Springer Nature Switzerland AG 2019.

关键词： Genetic algorithms

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