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Unified Object Detection Model for Image Style Convertible Modalities

Unified Object Detection Model for Image Style Convertible M...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Li, Mu-Chuan Chen, Xiu-Zhi Chen, Yen-Lin National Taipei University of Technology Dept. of Computer Science & Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

Object detection is an important task in computer vision. In earlier studies, object detection mostly works on single-modal. But recently, in order to improve detection efficiency, researchers start adding more modals for improving the robustness of detection result. In this paper, we assume that all input modals which can convert to image style are able to complete object detection task through a unified model. We choose YOLOv7 as our baseline model and use RGB and infrared images from the public dataset Camel to test our hypothesis. Based on our experiment results, training RGB and IR images together obtains better performance than training individually, which shows that all image style modalities are possibly handled by a unified model and even achieve better results. © 2023 IEEE.

关键词： Object detection

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A Study of the ADHD-200 Repository for Attention Deficit Categorization Using Deep Neural Networks 5

A Study of the ADHD-200 Repository for Attention Deficit Cat...

引用

5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Mengi, Mehak Malhotra, Deepti Central University of Jammu Dept. of Computer Science & Information Technology Samba181143 India

ISBN: (纸本)9798350305449

Attention Deficit Hyperactivity Disorder(ADHD) is a socio-behavioral disorder, affecting nearly 1% of people worldwide. Presently, the only traditional approach for diagnosing ADHD is standardized tests which prolong the procedure and raise the expense of care. The goal of this article was to explore the potential of Neural Networks in classifying ADHD patients and controlled children. For this purpose, the study utilizes Personal characteristic information (PCI) attributes extracted from a sizable collection. ADHD-200 is a multi-site repository that gathered information from several sources including eight different research and clinical institutes. The methodology and results involve two steps: In order to distinguish ADHD from typically developed(TD), a neural network model (NN) was first constructed in Python using the TensorFlow framework. Second, by employing the same repository, the experimental design utilizes the four distinct hidden layer Neural Network models configurations and demonstrated that the Neural Network with configuration(1024-512-256-2) performed best, averaging 75% accuracy. With regard to the ADHD-200 dataset, its accuracy is much greater than any other that is currently known. This study determined the best PCI characteristic for ADHD classification performance. In the future, additional discriminative features such as neuroimaging may enhance performance and ultimately enable the clinical diagnosis of ADHD. © 2023 IEEE.

关键词： Neural network models

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Applying the Genetic Algorithm Concept for Determining the Inheritance Relationship between Objects in Object Oriented Languages 3

Applying the Genetic Algorithm Concept for Determining the I...

引用

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Amarif, Mabroukah Sebha University of Libya Faculty of Information Technology Dept. of Computer Sciences Sebha Libya

ISBN: (纸本)9798350319897

The object-oriented programming model is based on the concept of objects where data is linked to their operations within a specific classification named as a class. The class is usually used to describe a number of specific programming objects. The most important characteristic of object-oriented programming languages is the inheritance property, which is the ability to build a new class of objects on the basis of an old one that was created previously. The new class inherits all the attributes (data) and behavior (methods) of the old one without the need to reformulate or list them. This process reduces the amount of redundant data and minimizes the size of the code. However, the process of determining the common characteristics of a group of objects is often done in a traditional sequential and repetitive manner during the analysis stage of collecting and sorting different system data. Unfortunately, it's full costs of time and effort. This paper presents a proposed algorithm based on the concept of the genetic algorithm to automatically determine the inheritance relationship between the largest possible numbers of objects and thus obtaining an abstract class from which the rest of the relevant classes can inherit. The proposed algorithm can also contribute to facilitate the design of a database which consists of large data set and their link to each other. © 2023 IEEE.

关键词： Genetic algorithms

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Dynamic Asset Allocation Using Trading Agent

Dynamic Asset Allocation Using Trading Agent

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： You, Shingchern D. Liu, Chien-Hung Yen, Ching-Cheng National Taipei Universiyt of Technology Dept. of Computer Science and Information Engineering Taipei Taiwan

ISBN: (纸本)9798350324174

We study the use of PPO (Proximal Policy Optimization) algorithm for trading ETFs belonging to different asset classes. The studied asset classes include common stocks, bonds, REIT (Real Estate Investment Trust), gold, and future contracts on agricultural commodities. When properly training the PPO agent with the proposed ratio allocation strategy, the agent outperforms the static, periodical rebalancing approach. © 2023 IEEE.

关键词： Investments

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A Survey on Eliminating Botnet and Intrusion Attacks through Machine Learning

A Survey on Eliminating Botnet and Intrusion Attacks through...

引用

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Faseela, M.H. Manjith, B.C. Dept. Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9798350325188

With the rising linkage and dependence on computer networks, the threat of intrusions and botnets has become a critical concern for organizations and those involved. Malicious actors are in control of unauthorized access or actions that harm the security and integrity of computer systems and insecure devices. The identification of attacks and botnets is of utmost importance in ensuring the security of networks and safeguarding confidential information. In recent years, there has been a growing interest among researchers and organizations in developing unique evaluation methods that can adapt to emerging threats and apply intelligent algorithms such as sparse modelling, transfer learning, distributed computing, or meta-learning capable of detecting, classifying, and predicting previously unknown attack patterns, which can result in machine learning and deep learning models that are more accurate and efficient. This review paper compares the performance of machine learning and deep learning approaches such as Naïve Bayes, Support Vector Machines (SVM), and Logical Regression for intrusion and botnet attacks in various network platforms. This paper provides a comprehensive comparison of machine-learning approaches used in the cyber realm for botnet and intrusion detection and encompasses an analysis of the datasets utilized, the specific cyber issues addressed, and the resultant outcomes. Furthermore, it offers valuable insights into the challenges and advancements within the realm of cybersecurity, thereby facilitating the development of novel approaches and technologies aimed at enhancing digital safety. © 2023 IEEE.

关键词： Botnet & Intrusion detection Cybersecurity Machine learning & Deep learning Techniques

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Detection of Pre Shoplifting Suspicious Behavior Using Deep Learning 16

Detection of Pre Shoplifting Suspicious Behavior Using Deep ...

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Shrestha, Sujan Taniguchi, Yoji Tanaka, Tetsuo Graduate school of Engineering Kanagawa Institute of Tehnology Course of Information and computer sciences Atsugi Japan Daiichi Institute of Technology Artificial Intelligence and Data Science Department of Information Tokyo Japan Kanagawa Institute of Technology Faculty of Information Technology Dept. of Information and Computer Sciences Atsugi Japan

ISBN: (纸本)9798350377903

Shoplifting presents a significant challenge for retailers, leading to substantial financial losses and security concerns. Current surveillance systems effectively identify shoplifting incidents postoccurrence;however, the capability to detect and prevent such activities preemptively remains limited. This research focuses on identifying behaviors that likely precede shoplifting events. To this end, we introduce a novel benchmark dataset, specifically designed for the early detection of suspicious behavior that may lead to shoplifting. This dataset is curated by selecting and segmenting relevant sequences from the UCF101-Crime dataset, encompassing suspicious behavior, actual shoplifting incidents, and normal activities. Our approach addresses the limitations of existing datasets where the brief duration of shoplifting events may skew neural network performance. The dataset includes clips of only a few seconds each, for which we propose a robust neural network architecture combining the strengths of convolutional neural networks (CNN) for efficient feature extraction and Bidirectional long short-term memory (BiLSTM) networks for capturing temporal dependencies within action sequences. Our model achieves an impressive classification accuracy of 86.21 % on the newly created dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid Framework for Parking Slot Allocation Using Double Auction and Cooperative Game Theory in Fog-Based Systems 3

Hybrid Framework for Parking Slot Allocation Using Double Au...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Bairi, Pijush Swain, Sujata Bandyopadhyay, Anjan Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India Haldia Institute of Technology Dept. of Information Technology Haldia India

ISBN: (纸本)9798331523893

The rapid growth of urban transportation necessitates innovative strategies for efficient parking management. Fog-based smart parking systems help reduce latency and improve resource use, but challenges remain in achieving fair and optimal slot allocation. This study presents a hybrid framework that combines a multi-attribute-based double auction mechanism with cooperative game theory. The double auction allocates parking slots fairly, considering both economic and non-price factors like proximity and reputation, while cooperative game theory fosters user coalitions to boost efficiency and satisfaction. By utilizing fog nodes for real-time processing, the system minimizes cloud reliance, ensuring low latency and scalability. Simulations demonstrate significant gains in efficiency, fairness, and responsiveness, showcasing the framework's potential to enhance resource use and promote collaboration for smarter urban mobility. © 2025 IEEE.

关键词： Urban transportation

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Benchmarking the Elliptic Curve Digital Signature Algorithm and RSA in Key Signing and Verification Operations with Parallelism

Benchmarking the Elliptic Curve Digital Signature Algorithm ...

引用

2023 Congress in computer Science, computer Engineering, and Applied Computing, CSCE 2023

作者： Dimitoglou, George Jim, Carol Hood College Dept. of Computer Science & Information Technology FrederickMD United States

ISBN: (纸本)9798350327595

Cryptographic techniques play a crucial role in securing information, and selecting an appropriate cryptographic technique involves considering factors such as encryption strength and computational overhead. Asymmetric cryptographic protocols and algorithms, such as RSA and those based on elliptic curve cryptography, are widely used for their flexibility, encryption strength, and key management. This study focuses on comparing the performance of the Elliptic Curve Digital Signature Algorithm (ECDSA) and RSA, specifically analyzing the impact of parallelism on signing and verification operations. The experimental results demonstrate that ECDSA is faster in signing but slower in verification compared to RSA. However, RSA's performance declines rapidly with increasing key sizes, while ECDSA exhibits a more graceful degradation. The study highlights the advantages of using elliptic curves in terms of reduced key sizes and the potential for ECDSA to become a preferred solution as security requirements increase. The paper's uniqueness is based on the impact of parallel processing on the performance of both cryptosystems. Future work involves further investigating the observed performance patterns and examining the implications of post-quantum resistance. © 2023 IEEE.

关键词： Cryptography Elliptic curves parallel processing performance RSA

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A Novel Data Hiding Scheme based on Block Features Enhanced AMBTC 4

A Novel Data Hiding Scheme based on Block Features Enhanced ...

引用

4th Asia Service Sciences and Software Engineering Conference, ASSE 2023

作者： Lin, Chia-Chen Oka, Hizrawan Dwi Zhu, Enting Dept. of Computer Science and Information Engineering National Chin-Yi University of Technology Taiwan

ISBN: (纸本)9798400708534

In this paper, we first improve Chen et al.'s ternary representation-based Absolute Moment Block Truncation Coding (AMBTC), then we combine block features and AMBTC to design our novel block feature enhanced-AMBTC-based data hiding scheme called BFI-AMBTC-based DH scheme. In our scheme, AMBTC blocks are classified into three types: complex, smooth, and flat according to the distribution of pixels in a block. For each block type, we propose different data hiding strategies according to their unique features. Experiments confirm that our hybrid data hiding strategies work well. The image quality of stego images with our hybrid data hiding strategies not only has been significantly improved but also is much closer to that offered by the conventional AMBTC compared with other existing AMBTC-based data hiding schemes. In addition, the hiding capacity achieved by our BFI-AMBTC-based DH scheme is confirmed almost two times Ou and Sun's scheme. © 2023 ACM.

关键词： Steganography

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Image Encryption using Modified RC4 Algorithm 3

Image Encryption using Modified RC4 Algorithm

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2023

作者： Hasan, Mahmoud H. S. Alasmarya Islamic University College of Information Technology Dept. Computer Science Zliten Libya

ISBN: (纸本)9798350319897

In this paper, we propose a new method for image encryption based on a modified RC4 stream cipher algorithm that scrambles image pixels before applying the XOR encryption function. We compare the performance of the proposed modified RC4 algorithm with that of the standard RC4 algorithm by analyzing histograms, calculating the correlation coefficient between adjacent pixels, and measuring information entropy. The results demonstrate that the proposed algorithm outperforms the standard RC4 algorithm in terms of resistance to statistical attacks. © 2023 IEEE.

关键词： Security of data

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