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3rd International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Li, Hongyang Zhu, Cui Zhu, Wenjun Zhou, Yunfeng Zhang, Xinyu School of Computer Science Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781643684444

Temporal knowledge graphs (TKGs) have been widely used in various fields, and predicting missing knowledge graph inference has been widely explored. However, the task of reasoning about potential future facts on TKGs is more challenging and has attracted the attention of researchers. As the unknowability of future events complicates inference, a thorough study of the characteristics of historical facts becomes crucial. The study of the concurrency of historical events and the underlying common patterns of relation-ships facilitates reasoning about future facts. In this paper, we propose a novel representation learning model based on Short-Term Sequential Patterns for TKG reasoning, namely STSP. By modeling TKG sequences recurrently and learning representations of entities and relations. Specifically, the STSP encoder uses three main modules. Concurrent facts for each timestamp are modeled using a convolution-based relation-aware GCN. The entity-aware attention module is used to integrate the entity representation of the current timestamp and the previous timestamp. The sliding window mechanism is used to learn different relations sequentially. The entity and relation representations are then handed over to a translation-based decoder for final reasoning. We use four benchmark datasets to evaluate the proposed approach. The experimental results show that STSP out-performs state-of-the-art TKG reasoning methods and obtains substantial performance. © 2023 The Authors.

关键词： Knowledge graph

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Skin Cancer Detection using Machine Learning Framework with Mobile Application 7

Skin Cancer Detection using Machine Learning Framework with ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Emam Ananna, Mariam Nayeem, Jannatul Jahangir Alam, Mohammad Islam, Saiful Daffodil International University Computer Science and Engineering Faculty of Science and Information Technology Bangladesh

ISBN: (纸本)9798350397284

A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. The need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. This study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. Thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset. © 2023 IEEE.

关键词： Feature extraction

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Sports Events Recognition Using Multi Features and Deep Belief Network

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computers, Materials & Continua 2024年第10期81卷 309-326页

作者： Bayan Alabdullah Muhammad Tayyab Yahay AlQahtani Naif Al Mudawi Asaad Algarni Ahmad Jalal Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science Applied CollegeKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or *** recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial *** research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those *** approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton ***,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or *** integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire *** extracted features are passed to the early fusion and DBscan for feature fusion and *** deep belief,network is employed for *** results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

关键词： Machine learning silhouettes extremal regions joint points scalable keypoints

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Algorithm and Processor Architecture for the Implementation of the Minimum Filtering Technique in Complex-Valued CNNs

Algorithm and Processor Architecture for the Implementation ...

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2023 Signal Processing Symposium, SPSympo 2023

作者： Cariow, Aleksandr Paplinski, Janusz West Pomeranian University of Technology Faculty of Computer Science and Information Technology Szczecin Poland

ISBN: (纸本)9788395602078

In this paper, we present a resource-efficient algorithmic solution regarding the fully parallel hardware implementation of the basic filtering operation performed in the complex-valued convolutional neural networks (CNNs). In fact, this basic operation computes the inner products of adjacent complex-valued vectors formed by a sliding time window from the current data stream with the impulse response vector of a 3-tap finite impulse response (FIR) filter. We adapt Winograd's minimal filtering algorithm for a case of the complex-valued data and applied it to develop a fully parallel hardware-oriented algorithm for implementing basic filtering operations in complex-valued CNNs. We also additionally apply the well-known Gauss's trick for multiplying complex numbers, which replaces 4 multiplications and 2 real additions with real 3 multiplications and 3 real additions. A general view of the architecture of the processor unit for the implementation of this operation is also given. A fully parallel hardware implementation of the proposed processor unit provides 50% savings in the number of embedded multipliers compared to a fully parallel hardware implementation of the naive calculation method. © 2023 Warsaw University of technology, Institute of Electronic Systems.

关键词： Complex networks

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Modeling the Impact of Habituation and Breaks in Exploitation Process on Multi-Armed Bandits Performance 27

Modeling the Impact of Habituation and Breaks in Exploitatio...

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27th International Conference on Knowledge Based and Intelligent information and Engineering Sytems, KES 2023

作者： Bortko, Kamil Bartków, Piotr Jankowski, Jaroslaw Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin Poland

Habituation is a common phenomenon in learning, where the response to a repeated stimulus decreases over time. In Multi-Armed Bandits (MAB) algorithms used for visual content delivery optimization, habituation can lead to suboptimal performance. For example, if an agent becomes habituated to a suboptimal arm, it may continue to choose that arm even if better options are available. Habituation can be modeled as a form of"forgetting", where the agent gradually loses confidence in its estimates of the reward probabilities for each arm as time passes. Proposed approach allows updating estimates according to habituation model, while also exploiting the arm with the highest estimated reward probability. The results showed that it is a very good solution to introduce breaks in the multi-armed bandit habit model, which was implemented and tested in our study. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Breaks in Habituation Computational Habituation MAB alghoritm

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STFM: Enhancing Autism Spectrum Disorder Classification Through Ensemble Learning-Based Fusion of Temporal and Spatial fMRI Patterns 20th

STFM: Enhancing Autism Spectrum Disorder Classification Thro...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Zhou, Ziyu Huang, Yiming Wang, Yining Liang, Yin Beijing University of Technology Beijing China Faculty of Information Technology School of Computer Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819970247

This paper presents the novel spatial and temporal fusion model (STFM), an effective approach for Autism Spectrum Disorder (ASD) detection and classification tasks using foundational machine learning models. Utilizing ensemble learning principles, STFM improves the classification performance by integrating weak classifiers. The process begins with the sliding window method applied to fMRI data, constructing brain networks through Pearson correlation calculation between brain regions. This infuses the network with both temporal and spatial patterns. Then, bidirectional LSTM (Bi-LSTM) and 2DCNN are applied for temporal and spatial feature extraction respectively. The model further ensures smoother data variations between patterns through interpolation, and utilizes a basic cross attention mechanism for fusion of patterns. The fused patterns are then classified by a simple SVM classifier. The presented STFM model demonstrates a remarkable classification accuracy of 70.42%, surpassing most fundamental machine learning models in ASD detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Brain

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Automatic Timbre Transformation using Enhanced Diffusion Model

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IEEE Access 2025年 13卷 91642-91654页

作者： Wu, Cheng-Han Cheewaprakobkit, Pimpa Shih, Timothy K. Lin, Yu-Cheng Liu, Bing-Ze National Central University Department of Computer Science and Information Engineering Taoyuan City32001 Taiwan Asia Pacific International University Faculty of Information Technology Saraburi18180 Thailand Yuan-Ze University Department of Computer Science and Engineering Taoyuan City32003 Taiwan

We present a novel timbre transfer model that uses an enhanced diffusion architecture to convert music from various instruments into Erhu timbre. The Erhu, a traditional Chinese instrument, is difficult to simulate due to its rich vibrato and smooth note transitions. Existing Musical Instrument Digital Interface systems struggle to capture its nuanced dynamics. Our model integrates a Pitch Encoder, a Loudness Encoder, and a Diffusion Decoder. The encoders extract pitch features and dynamic loudness variations, guiding the decoder in generating realistic Erhu timbre. By extracting general musical features, the system generalizes to unseen input types without retraining. Evaluations based on pitch accuracy, cosine similarity, and Fréchet Audio Distance show that our model achieves 96% pitch accuracy and high fidelity in Erhu timbre reproduction. This study demonstrates the potential of diffusion-based timbre transfer models in music generation and provides new directions for future work on both music generation and timbre transfer. © 2013 IEEE.

关键词： Deep learning

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Using Supervised Machine Learning Model For Predicting Fake Deliveries 6

Using Supervised Machine Learning Model For Predicting Fake ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Sadek, Mohamed A. Fouad, Khaled M. Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Egypt

ISBN: (纸本)9798350373875

The home delivery service is a crucial part of the delivery process. Still, it has the highest financial and trustworthiness costs because it's the only part that directly touches the customer. If it works, the customer will be happy, and the company will make money, but if it doesn't, the customer will be skeptical, and the product will not be trusted. One big issue with the delivery process that this study will try to address is the prevalence of fraudulent or inaccurate deliveries. Making the most money possible from each journey is the primary goal of most riders. The delivery management system allows riders to falsely record orders as completed, allowing them to receive many orders simultaneously. Because the consumer usually gets a message or notification saying their item has been delivered when it hasn't, this weakens their faith in the organization and messes up the delivery process. Return to Origin (RTO) rates rose, and consumer confidence in the brand fell due to the fraudulent shipments. The suggested approach is based on ML models that are supervised. © 2024 IEEE.

关键词： Sales

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A Case Study of Intelligent Street Lighting in Rural Areas for Energy Savings 33rd

A Case Study of Intelligent Street Lighting in Rural Areas f...

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33rd International Conference on Organization and technology of Maintenance, OTO 2024

作者： Križanović, Višnja Pracny, Patrik Pejković, Ana Grgić, Krešimir Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9783031805967

The aim of this paper is to analyze the implementation of intelligent lighting within the concept of smart energy based on the possibility of saving and efficient use of energy, which is largely based on non-renewable sources. In selected case studies of rural environments, the possibility of implementing smart lighting was analyzed through simulation procedures, from the point of view of energy saving, showing the importance of this aspect in the context of building smart energy lighting systems. The conclusions in the paper show that the analyzed solution provides useful guidelines for the development of the concept of smart lighting, bringing benefits and reducing costs. Considerations given in the paper show a quantitative assessment of energy savings through the replacement of traditional lighting systems in rural areas. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Energy saving

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Blockchain-Driven Secure Data Sharing Framework for Edge Computing Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 978-997页

作者： Fuad A.M.Al-Yarimi Ramzi Salah Khaled Mohamoud Department of Computer Science King Khalid UniversityMuhayel Aseer 62529Saudi Arabia Faculty of Information Science and Technology University Kebangassan MalaysiaBangi 43600Malaysia Department of Information System King Khalid UniversityMuhayel Aseer 62529Saudi Arabia

This study examines secure and effective data sharing methods for edge computing *** methods of sharing data at the edge have issues with security,speed,and *** goal is to develop a Blockchain-based Secure Data Sharing Framework(BSDSF)capable of improving data integrity,latency,and overall network efficiency for edge-cloud computing *** proposes using blockchain technology with Byzantine Fault Tolerance(BFT)and smart contract-based validation as a new method of secure data *** has a two-tiered consensus protocol to meet the needs of edge computing,which requires instantaneous *** employs Byzantine fault tolerance to deal with errors and protect against *** contracts automate validation and consensus operations,while edge computing processes data at the attack *** validation and failure detection methods monitor network quality and dependability,while system security ensures secure communication between *** is an important step toward digital freedom and trust by protecting security and improving transaction *** framework demonstrates a reduction in transaction latency by up to 30％and an increase in throughput by 25％compared to traditional edge computing models,positioning BSDSF as a pivotal solution for fostering digital freedom and trust in edge computing environments.

关键词： Byzantine Fault Tolerance(BFT) blockchain-driven edge computing networks consensus contract data validation contract node validation contract

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