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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Schnepf, Jonas Vetter, Paula Temel, Tarik Scheuermann, Bernd Schmidt-Thieme, Lars University of Applied Sciences Karlsruhe Faculty of Management Science and Engineering Karlsruhe Germany Institute of Computer Science University of Hildesheim Information Systems and Machine Learning Lab Hildesheim Germany

ISBN: (纸本)9781665480451

Enterprise Resource Planning (ERP) systems are used to support and to control the business processes of a company or organization. Such systems integrate the data across the entire company into a complete system that is capable of enhancing the key operations in virtually any department. Commonly, running an ERP system helps companies operate more efficiently, however, this also leads to problems. Employees are able to enrich themselves through insider knowledge or by exploiting incomplete or incorrect permission settings. This is referred to as occupational fraud. Since ERP systems keep records of all executed business activities and log system events and permission checks, they provide a variety of different data sources that can be used to detect occupational fraud. This paper reviews existing literature in the area of fraud detection and fraud cases based on ERP data including business data and system data. Using the most widespread ERP system from SAP, the potential and the suitability of the various data sources with respect to fraud detection is examined. © 2022 IEEE.

关键词： Enterprise resource planning

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Progressive Frequency-Aware Network for Laparoscopic Image Desmoking

arXiv

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arXiv 2023年

作者： Zhang, Jiale Huang, Wenfeng Liao, Xiangyun Wang, Qiong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Laparoscopic surgery offers minimally invasive procedures with better patient outcomes, but smoke presence challenges visibility and safety. Existing learning-based methods demand large datasets and high computational resources. We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN framework for laparoscopic image desmoking, combining the strengths of CNN and Transformer for progressive information extraction in the frequency domain. PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for capturing local high-frequency information and Locally-Enhanced Axial Attention Transformers (LAT) for efficiently handling global low-frequency information. PFAN efficiently desmokes laparoscopic images even with limited training data. Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000, and visual quality on the Cholec80 dataset and retains only 629K parameters. Our code and models are made publicly available at: https://***/jlzcode/PFAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Smoke

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VGG Models in Image Captioning: Which Architecture Delivers Better Descriptions?

VGG Models in Image Captioning: Which Architecture Delivers ...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Ahmad Maaz Shaheer Abbas Raja Hashim Ali Iftikhar Ahmed Talha Ali Khan Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi Pakistan Department of Business University of Europe for Applied Sciences Think Campus Potsdam Germany

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

The recent advancements and a flurry of deep learning architectures in the fields of computer vision and natural language processing have greatly benefited the task of creating natural language descriptions for images in recent years. These advancements have proved to be the turning point with popular practical applications in artificial intelligence, as well as for driving cross-disciplinary developments. However, not many studies have investigated the performance of different convolutional and LSTM architectures for image captioning, motivating our study to bridge this gap. This study compares the performance of VGG16 and VGG19 models on the Flickr8k dataset. Image features are extracted using pretrained VGG16 and VGG19 models, textual input is tokenized, and a unique Encoder-Decoder architecture is built. We determine the effect of training length on captioning proficiency by conducting comprehensive tests with varied epochs. The BLEU ratings reveal detailed insights into the models' language generating capabilities, demonstrating comparable performance across VGG16 and VGG19. Our work fills this gap by assessing the efficiency of several neural designs on a given dataset. The findings highlight the complexities of model convergence, and the need for different evaluation measures and defined benchmarks in the picture captioning sector. We determine the relationship between training epochs and model performance in this study, and provides useful observations for researchers and practitioners. We systematically examine the design of various convolutional neural networks, and provide a basis for future research in the ever-advancing world of image captioning.

关键词： Training computer architecture Benchmark testing Performance gain Feature extraction Turning Natural language processing Complexity theory Long short term memory Convergence

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Image Encryption Algorithm Based on New Fractional Beta Chaotic Maps

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computer Modeling in Engineering & sciences 2022年第7期132卷 119-131页

作者： Rabha W.Ibrahim Hayder Natiq Ahmed Alkhayyat Alaa Kadhim Farhan Nadia M.G.Al-Saidi Dumitru Baleanu Institute of Electrical and Electronics Engineers Kuala Lumpur59200Malaysia Information Technology College Imam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq Department of Computer Technical Engineering College of Technical EngineeringIslamic UniversityNajaf192122Iraq Department of Computer Sciences University of TechnologyBaghdad10066Iraq Department of Applied Sciences University of TechnologyBaghdad10066Iraq Department of Mathematics Cankaya UniversityBalgatAnkara06530Turkey Institute of Space Sciences Magurele-BucharestR76900Romania Department of Medical Research China Medical UniversityTaichung40402Taiwan

In this study,a new algorithm of fractional beta chaotic maps is proposed to generate chaotic sequences for image *** proposed technique generates multi random sequences by shuffling the image pixel *** technique is used to blur the pixels connecting the input and encrypted images and to increase the attack *** proposed algorithm makes the encryption process sophisticated by using fractional chaotic maps,which hold the properties of *** fractional beta sequences are utilized to alter the image pixels to decryption *** experimental results proved that the proposed image encryption algorithm successfully encrypted and decrypted the images with the same *** output findings indicate that our proposed algorithm has good entropy and low correlation *** translates to enhanced security against different attacks.A MATLAB programming tool was used to implement and assess the image quality measures.A comparison with other image encryption techniques regarding the visual inspection and signal-to-noise ratio is provided.

关键词： Fractional calculus fractional beta function chaotic function image encryption

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A Qualitative Analysis of Interaction Techniques in a Virtual Reality Instruction Environment: Experiences From a Case Study

A Qualitative Analysis of Interaction Techniques in a Virtua...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： André Helgert Anna Groeneveld Sabrina C. Eimler Institute of Computer Sciences University of Applied Sciences Ruhr West Bottrop Germany

ISBN: (纸本)9781665457262

As they can immerse people into virtual worlds free from external distraction, virtual reality (VR) applications are an important factor in research today and can be useful in various scenarios such as intervention training, simulations and in learning environments. However, many people are not used to interacting and navigating in VR and may not have much prior technical experience, which can become a distracting factor to the content and can influence success negatively. Thus, benefits of VR-environments cannot be fully exploited. Aiming to counteract the problem that people cannot take advantage of the benefits of VR-applications, an instruction room for training representative interaction patterns, i.e. interaction and locomotion mechanics, was designed. Students (N = 12) were videotaped going through six stations of the instruction room with different interaction tasks. They were prompted to think aloud to capture their thoughts (e.g., intuitiveness, usability and expansion ideas) while interacting, and interviewed afterwards for an overall assessment. A qualitative content analysis helped to identify patterns. Interactions like localization field triggers for instructions and answering a quiz were regarded as highly intuitive, giving feedback with a drum keyboard was reported as most entertaining.

关键词： Training Location awareness Solid modeling Navigation Keyboards Virtual reality Usability

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Integrated Control and Modeling of a PV-Wind Hybrid System for DC Electric Micro-Networks 3

Integrated Control and Modeling of a PV-Wind Hybrid System f...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Santhosha, B.M. Negi, Harendra Singh Balaraju, V. Vekariya, Daxa Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Visvesvaraya Technological University Department of Electrical and Electronics Engineering Ballari Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Dr. Ntr College of Agricultural Engineering Department of Basic Engineering and Applied Sciences Andhra Pradesh Bapatla522101 India Parul Institute of Engineering and Technology Parul University Department of Computer Science & Engineering Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350361186

The main objective of this work is to model, control and simulate an electrical microgrid. To achieve this objective, models of the subsystems or associated elements (generators, power interfaces, electrical loads, etc.) are built, the control strategies necessary for the integration of each subsystem in the microgrid are implemented, and it is proposed an optimization algorithm that allows the economic dispatch of the system to be carried out under different generation and demand conditions. Finally, after simulating the model, its behavior is validated in a considered operating scenario. The work environment selected to develop this project is Simulink, a tool integrated into MATLAB, due to the ease it offers when modeling and simulating dynamic systems, allowing you to customize the included libraries and export data to MATLAB for subsequent analysis. At the end of this work, it is intended to have a simulated environment to validate the behavior of the proposed microgrid, which allows considering different generation and demand scenarios, and an optimization algorithm that allows carrying out the economic dispatch of the system under the operating conditions. considered, without taking into account the randomness of generation sources and energy demand. © 2024 IEEE.

关键词： Wind power

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Handwritten Alphabet Recognition Using Convolutional Neural Networks

Handwritten Alphabet Recognition Using Convolutional Neural ...

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Automatics and Informatics (ICAI), International Conference

作者： Yasir Jamal Sakina Juzar Neemuchwala Rand Kouatly Ahmed Hassan Nafeesath Parappurath Puthiyapurayil Meerah Karunanithi Faculty of Computer Science & Engg. GIK Institute of Engg. Sciences & Tech Topi Pakistan Department of Business University of Europe for Applied Sciences Potsdam Germany Department of Computer Science Berlin School of Business and Innovation Berlin Germany

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

Instruction of hand-written alphabet recognition using an intelligent adaptive Optical Character Recognition (OCR) system is one of the vital pattern recognition problems, which has a very broad spectrum of frequent applications in various fields, such as language comprehension and archival document investigation. Instruction Recognition has seen a lot of progress in the data recognition field, but the handwritten alphabet recognition follows a separate track and computer with this can be a challenge due to the different styles and shapes of handwriting which are not marked. The project provides room for further work in which the Convolutional Neural Network (CNN) model can be trained to interpret handwritten alphabet characters. In connection with the exact naming of the handwritten images in the A-Z collection, deep neural networks are applied. AI-generated images of the outcome corroborate the effectiveness and accuracy of the constructed CNN algorithm, which precisely identifies handwritten characters of the alphabet with an accuracy above 96-97 percent. Insights from this analysis are pivotal in making this research area a success and CNNs are a powerful tool that can be exploited for this purpose. The presentation seems to enjoy the fact that their results are useful in many areas which includes digitization of historical documents, automatic online form processing, as well as well as customized learning system.

关键词： Handwriting recognition Accuracy Shape Computational modeling Optical character recognition Transfer learning computer architecture Switches Convolutional neural networks Tuning

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Impacts of Image Compression on the Detection Quality of a Novel Real-Time Image Processing Platform

Impacts of Image Compression on the Detection Quality of a N...

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International Conference on Intelligent Transportation

作者： Jannik Mehrke Georg Volk Yannik Stumpp Oliver Bringmann Anestis Terzis Institute of Communication Technology Ulm University of Applied Sciences Dept. of Computer Engineering University of Tübingen

Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.

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Exploring the Synergy of Artificial and Computational Intelligence for Technological Advancements 1

Exploring the Synergy of Artificial and Computational Intell...

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1st International Conference on Data, Computation and Communication, ICDCC 2024

作者： Palarimath, Suresh Maran, Pyingkodi Valsalan, Prajoona Wilfred Blessing, N.R. Kavitha, S.J. Steffi, S. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Engineering Dhofar Univesity Electrical and Computer Engineering Department Salalah Oman College of Computing and Information Sciences Univesity of Technology and Applied Sciences Ibri Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798331532956

Due to the ongoing advancements in science and technology, it seems that artificial intelligence and computers have encountered a limitation in their progress. Contemporary scientific research requires more sophisticated interconnection technology to ensure the continued progress of science and technology. Amidst this setting, the technology of Internet of Things (IoT) has progressively been visible to individuals. This technology will significantly impact human lifestyle and quality of life, and the Internet of Things has seen substantial progress. Information technology is intricately intertwined with the lives of citizens. The integration of information technology with the IoT is aimed at maximizing the technological advancement of the Internet of Things, with Internet technology serving as the central component. The use of artificial intelligence and computer intelligence in Internet technology is versatile, and issues pertaining to artificial intelligence and computing intelligence are progressively emerging. Artificial intelligence and computational intelligence play a significant role in the Internet of Things. However, their application process is challenging and lacks creativity, necessitating ongoing improvements at the application level. This study examines the use of artificial intelligence and computational intelligence in the Internet of Things. © 2024 IEEE.

关键词： Artificial intelligence computational intelligence Internet of things

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Deep Learning in Remote Sensing for Climate-Induced Disaster Resilience: A Comprehensive Interdisciplinary Approach

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 145-160页

作者： Padmaja, S.M. Naveenkumar, R. Kumari, N. P. Lavanya Pimo, Er. S. John Bindhu, M. Konduri, Bhagavan Jangir, Pradeep Department of Electrical and Electronics Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India Department of Computer Science and Systems Engineering AUCE Andhra University Visakhapatnam India Department of Computer Science and Engineering St. Xavier’s Catholic College of Engineering Nagercoil India Department of ECE Sri Venkateswara College of Engineering Sriperumbudur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Climate-induced disasters pose significant threats to human lives, infrastructure, and ecosystems. Deep learning techniques, combined with remote sensing, offer powerful tools for predicting, detecting, and mitigating the impacts of such disasters. This study presents a comprehensive interdisciplinary approach using a hybrid cascaded CNN-RNN model to perform spatio-temporal analysis, enhanced image segmentation with U-Net and SegNet architectures, and a hybrid CNN-LSTM-XGBoost ensemble for multi-hazard prediction. The proposed CNN-RNN model achieves an accuracy of 96.78%, significantly outperforming traditional models in predicting flood-prone areas, wildfires, and droughts. Key metrics such as Intersection over Union (IoU), precision, recall, and F1-scores are used to evaluate model performance, highlighting the model’s ability to accurately identify disaster-prone areas and improve disaster resilience. This research not only demonstrates the efficacy of deep learning for climate-induced disaster prediction but also paves the way for future integration of socio-economic data to refine disaster risk assessment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate-induced disasters CNN-RNN Deep learning Disaster resilience Hybrid ensemble Multi-hazard prediction Remote sensing SegNet Spatio-temporal analysis U-Net

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