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Preface to special topic on AI algorithms and cases: to energize digital economy

National science Review 2023年第6期 7-8页

作者：徐宗本申恒涛胡事民 School of Mathematics and Statistics Xi'an Jiaotong University School of Computer Science and Engineering University of Electronic Science and Technology of China Department of Computer Science and Technology Tsinghua University

Since the turn of the century,innovations in science and technology have been in full swing across the globe.A new wave of scientific inventions and industrial transformations are constantly reshaping the economic structure of the *** the forefront of this trend is the digitalization of *** combined with a new form of productivity such as artificial intelligence (AI),this wave of scientific and technological revolution is driving digital transformations and adoptions of smart devices throughout society.

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Fraud Detection in Monetary Transactions 2

Fraud Detection in Monetary Transactions

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Nair, Rekha R. Kaveri, V. Vijeya Sivakumar, Shrinidhi Srinithi Janani, R. Babu, Tina Rajesh Sharma, R. Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Alliance University Bangalore India

ISBN: (纸本)9798350383867

Fraudulent transactions pose significant challenges to the banking industry, leading to significant losses. Conventional identification methods often struggle to keep up with changing fraud techniques. In this study, proposed a new approach using a hybrid algorithm combining Bagged Trees Classifier and Random Forest. Bagged Trees Classifier classifies instances by tree structure, while Random Forest aggregates predictions from multiple trees to increase accuracy in detecting fraudulent behavior the proposed approach aims to overcome existing limitations role by reducing false positives and improving detection accuracy. outline the method for data set partitioning, model selection,training, and evaluation, and demonstrate the effectiveness of ourapproach. By combining a bagged trees classifier with a random forest, the proposed approach provides a robust framework for detecting fraudulent transactions in banking, providing proactiveanti-fraud strategies while reducing financial losses. ©2024 IEEE.

关键词： Random forests

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Deep-Learning-Based Risk Prediction with Urban Sensing Data for Consumer Driving Safety 13

Deep-Learning-Based Risk Prediction with Urban Sensing Data ...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Lin, Kun-Yu Liu, Pei-Yi Hu, Chih-Lin Huang, Sheng-Zhi Chen, Yung-Hui Hui, Lin National Central University Department of Communication Engineering Taoyuan City320317 Taiwan Tamkang University Department of Computer Science and Information Engineering New Taipei City251301 Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taiwan

ISBN: (纸本)9798350355079

With the provision of IoT-driven and user-provided sensing data sources in smart cities, we take advantage of deep learning techniques to analyze the spatio-temporal traffic data and predict traffic risks for driving safety. Our study continues to collect the traffic data from multiple sensing sources, and meanwhile adopts both CNN and LSTM to interpret the data collection in spatial and temporal dimensions. Thus, a novel traffic risk prediction scheme based on CNN and LSTM, named TRP-CL, is proposed to generate a traffic warning map of risks and hazard situations on a grid-scaled city map. Not only a theoretic formation but also an experimental implementation of the TRP-CL scheme are made to show the practical feasibility. © 2024 IEEE.

关键词： crowdsensing intelligent transportation system (ITS) IoT machine learning (ML) risk prediction Traffic data

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Wind energy production using novel HCS algorithm to reach MPPT for small-scale wind turbines under rapid change wind speed

Wind energy production using novel HCS algorithm to reach MP...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

In this paper, a novel algorithm had been proposed to improve the efficiency performance of the wind energy conversion system (WECS) to reach the maximum possible captured power under fast varying wind speed. The permanent magnet synchronous generator (PMSG) had been selected because of its reliability and efficient performance, especially in low-speed conditions environment. The novel algorithm applied the power prediction mode to make optimization for the reached decision to the maximum peak point. The proposed algorithm shows better efficiency performance for different wind speed conditions. The novel algorithm accelerates convergence to detect the maximum peak point without sacrificing efficiency. The novel algorithm’s simplicity reduces the overall time required by 33% when compared to the three-mode algorithm. © 2025 The Author(s).

关键词： Windmill

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Distributed Self-Localization with Improved Optimization with Machine Learning in IoT Applications 1

Distributed Self-Localization with Improved Optimization wit...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Jaber, Zahraa Hameed Ihsan, Mohammed Gokulakrishnan, S. Alshaibani, Hanaa Ali Alsalamy, Fatima H. Al-Aboudy, Hussein National University of Science and Technology Dhi Qar Iraq The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India Altoosi University College Department of Computer Science Najaf Iraq Al-Mustaqbal University Medical Laboratories Techniques Department Hillah Iraq Mazaya University College Department of Computer Techniques Engineering DhiQar Iraq

ISBN: (纸本)9798350367003

The Internet of things (IoT) is one of the most trending technologies which is used to monitor a huge number of devices worldwide. Device localization and optimal path selection is very essential in this technology to maintain the communication standard of the devices. To reduce the delay and power utilization of the devices and to attend high efficiency these parameters are needed to get concentrated. For that in this article distributed self-localization with an improved optimization model is developed using machine learning (DSLIOM) algorithms. The core modules of this article are efficient data processing analysis and improved optimization algorithm. The parameters which are calculated to analyses the performance are data success rate, network throughput, routing overhead, data loss rate and delay. From the result it is proven that this DSLIOM attends better performance than earlier works in terms of data success rate and the network throughput. © 2024 IEEE.

关键词： Optimization algorithms

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Enhanced Fraud Detection in Ethereum Transactions: Fusion of Modified Genetic Algorithms and Deep Learning with Limited Attributes

Enhanced Fraud Detection in Ethereum Transactions: Fusion of...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Kumar, Alok Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Ethereum smart contracts, the new way of transactions and a popular name in the world of cryptocurrencies, have gathered a huge base of research and scientific attention. They are so helpful that they allow us to eliminate the need for a separate third-party library to allow unknown parties to see contract details on a computer. But since we can all see that online commerce is growing day by day and will continue to grow, it can never be fully free from scams and unethical operations. So, to correctly detect all such unethical and malicious transactions, this paper used a deep learning model. And to further enhance the model, this study used metaheuristic optimization as well. It employs an algorithm called Genetic Algorithm and to provide better optimization in the explorations phase of Cuckoo Search (CS) to achieve its goal of detecting fraudulent transactions. The algorithm covers the loopholes in the CS strategy. Furthermore, to provide strong grounds for research in our paper, this proposed model was compared with various types of approaches such as Light Gradient Boosting Machine, Support Vector Classification, Multi-Layer Perceptron, XGBoost, Logistic Regression, and Random Forest. The proposed model outperforms other significant models like SVC, KNN, LGBM Classifier, RF etc. with the accuracy of 98.6%. © 2024 IEEE.

关键词： Genetic algorithms

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Mathematical analysis of AMRes: unlocking enhanced recognition across audio-visual domains

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International Journal of Information technology (Singapore) 2024年 1-20页

作者： Zoughi, Toktam Deypir, Mahmood Department of Electrical and Computer Engineering Shariaty College Technical and Vocational University (TVU) Tehran Iran Faculty of Computer Engineering Shahid Sattari Aeronautical University of Science and Technology Tehran Iran

This research presents AMRes (Adaptive Windowed Convolutional Neural Network and Multiple Residual Network), a novel method that demonstrates remarkable resilience against overfitting, even in scenarios with limited training data. With meticulous design comprising up to 19 convolutional layers, it enables deep learning models to effectively extract and analyze vital information from input data while preserving it through deep layers. Through rigorous mathematical and empirical validation, the proposed method exhibits adept handling of intra- and inter-speaker variability, efficient training with limited data, and potential applicability beyond speech recognition in image-related applications, showcasing its generality. This paper establishes a solid foundation for advancements in multi-faceted recognition systems. The proposed method excels at proficiently modeling speech signals and achieving highly accurate speech recognition. Thorough evaluations utilizing well-established databases such as Switchboard, Timit, and FarsDAT for speech, and MNIST for image data underscore the method's versatility and efficacy across diverse data contexts. Comparative analysis against state-of-the-art techniques in speech recognition demonstrates the propose method's competitive and often superior performance. Notably, this method significantly reduces phoneme recognition errors by approximately 8% on notable datasets. This research provides a solid foundation for advancing the field and highlights the proposed method's potential in improving recognition systems, encouraging further exploration. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Audio-Visual Domains Deep learning Formal Verification Limited Training Data Mathematical Analysis Mathematical Foundations Speech recognition

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Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images

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Multimedia Tools and Applications 2025年第7期84卷 3459-3485页

作者： Kumar, S. Aanjan Muchahari, Monoj Kumar Poonkuntran, S. Kumar, L. Sathish Dhanaraj, Rajesh Kumar Karthikeyan, P. Department of Computer Application and Science Institute of Engineering and Management West Bengal Kolkata India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India Pune India Department of Electronics & Communication Engineering Velammal College of Engineering & Technology Tamil Nadu Madurai625009 India

Today, malaria is a dangerous disease caused by Plasmodium parasites and transmitted by the bite of Anopheles mosquitoes. It is estimated that more than 200,000 cases of malaria will occur every day worldwide, and more than 400,000 people will die from malaria. Early diagnosis of malaria is important to mitigate the morbidity and mortality of the disease. In this paper, a fully trained capsule neural network combined with a convolutional neural network model is proposed for malaria detection from blood smear microscopic images. In malaria diagnosis, the hybrid CapsNet model can detect malaria in blood samples. The hybrid model is optimized based on malaria data in microscopic images of infected and uninfected blood. Images are processed and enhanced through rotation before being fed into the hybrid CapsNet model. The CapsNet hybrid model is examined for malaria data with a learning rate of 0.07 and a batch size of 20. Hybrid CapsNet model shows improved results in detecting malaria from microscopic images measured against the currently available standard malaria test. Constraints like malaria parasite detection rate, accuracy, F1 index and blood smear classification is used to gauge the attainment of the hybrid CapsNet model. Detection rate of 99%, accuracy of 99.08% and FAR of 0.97% is attained by the hybrid CapsNet model as contrary to the traditional state-of-the-art model, the DSCN-Net, where the detection rate was 98%, the accuracy was 97.2%, and the FAR was 0.99%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diseases

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Lightweight image super-resolution reconstruction based on mixed attention and global inductive bias network

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Multimedia Tools and Applications 2024年 1-17页

作者： Cai, Yuxi Wen, Xiaopeng Wang, Tongguan College of Computer Science Sichuan University Chengdu China Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China College of Informatics Huazhong Agricultural University Wuhan China

Convolutional neural network has significantly advanced the field of image super-resolution reconstruction in recent years. The insufficient ability to model global information of hierarchical features, incomplete attention to information, and excessive parameters make some based on convolutional methods face challenges. To address these issues, this paper designs the lightweight image super-resolution reconstruction based on Mixed Attention and Global Inductive bias Network(MAGIN), which directly captures the global information of phased features from the horizontal and vertical directions respectively to produce global inductive bias information. Furthermore, the global spatial attention block is proposed, which can realize the perception of global feature space without redundant operation. To realize more comprehensive attention for features, a mixed attention block arises, which enables the network to attend to features across different dimensions: channel, spatial, and pixel levels. Extensive experimental results demonstrate that the MAGIN delivers more satisfactory results than some other advanced lightweight reconstruction algorithms on several standard test datasets under the condition of considering the network’s performance, parameter and computational complexity. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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ICEL: Learning with Inconsistent Explanations 48

ICEL: Learning with Inconsistent Explanations

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Biao Wu, Xiaoyu Yuan, Bo Southern University of Science and Technology Department of Computer Science & Engineering China Rams Lab Huawei China

ISBN: (纸本)9781728163277

Generating the heatmaps is one of the explanation methods to show what regions the model use to predict in vision tasks. GradCAM is a popular approach to provide such heatmaps. However, GradCAM is post-hoc, and its heatmaps are not always meeting human annotations. Inspired by CGC (Contrastive GradCAM consistency), we propose ICEL (InConsistent Explanation Learning) method which introduces inconsistent explanation loss measured by cosine similarity on heatmaps. We show that our method can preserve classification accuracy, while the heatmaps generated by explanation methods are more consistent with human annotations, and the computational complexity is reduced from O(n2) to O(n), compared with CGC. © 2023 IEEE.

关键词： computer vision

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