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11th International Conference on Image and Graphics, ICIG 2021

作者： Yin, Jianhua Liu, Cong Jiang, Jun Wen, Jie Yang, Lihang Zhu, Shilong School of Computer Science and Technology Harbin Institute of Technology Shenzhen518055 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China College of Big Data and Information Engineering Guizhou University Guizhou Guiyang550025 China

ISBN: (纸本)9783030873578

The triplet-loss function is widely used in fields of descriptor extraction in recent years and owing to its good performance in various databases. However, some recent works make less effort on the relationship of adjacent descriptors from the same sample, which leads to the instability of descriptors and results in the mismatching problem in practical applications. To solve this problem, we introduce the topological relationship with the Normal Distribution Function (NDF) into the triplet loss function. The loss function establishes the relationship of descriptors from the same sample and considers the interrelation of descriptors from different types of samples. Furthermore, to increase the calculation speed, we normalize the algorithm NDF. Finally, we propose the triplet-loss function on three databases. These results demonstrate that our algorithm obtains better performance than state-of-the-art methods. © 2021, Springer Nature Switzerland AG.

关键词： Distribution functions

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Role of artificial intelligence in the Next Generation Wearable Devices

Role of Artificial Intelligence in the Next Generation Weara...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Virendra Kumar Patel Jignasaben Babubhai Fayaz Ahmed Fayaz Kiran Dhobal Praveen Kumar Rai Ashok Rachapalli G L Bajaj Institute of Technology And Management Gr. Noida Faculty of Computer Science Shri C.J. Patel College of Computer Studies Sankalchand Patel University Mehsana Gujarat NIELIT & Lovely Professional University Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Science & Engineering GL Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

The famous category of gadgets now includes whole new and quickly developing devices thanks to wearable technology. Wearable technology has the ability to provide artificial intelligence (AI) techniques with a wealth of useful data, in addition to being attractive and outfitted with cutting-edge hardware technologies like communication modules and networking in next generation. Numerous projects have previously been completed using a variety of AI approaches, including monitored, unattended, semi-supervised, and reinforcement learning (RL). This article examines contemporary wearable apps that have used AI to accomplish their goals. Users cover specific examples of both supervised and unsupervised applications in medical diagnosis. Additionally, instances fusing RL, wearables, and the web of things are discussed. Wearables application samples for particular industries, including the healthcare, commercial, & sporting fields, are also provided. Implications in medicine range from personal exercise to mobility disorders and mental wellness. The use of wearables in the workplace includes improving productivity of employees. The main goal of sports programs is to improve the client satisfaction through exercise or competitive *** biggest issues with wearable device creation and creation as well as the computational cost of applying AI techniques are discussed. Finally, prospects and difficulties facing wearable technology in the next are discussed.

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Innovative Urban Design Simulation: Utilizing Agent-Based Modelling through Reinforcement Learning 23

Innovative Urban Design Simulation: Utilizing Agent-Based Mo...

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Proceedings of the 2023 6th International Conference on Computational intelligence and Intelligent Systems

作者： Ayse Glass Jorg R. Noennig Burak Bek Roman Glass Eylul K. Menges Iryna Okhrin Pramod Baddam Mariela Rossana Sanchez Gunalan Senthil René Jäkel Digital City Science Hafencity Universitat Hamburg Germany University Grenoble Alpes France Faculty of Architecture izmir Institute of Technology Turkiye ScaDS.AI - Center for Scalable Data Analytics and Artificial Intelligence Technical University of Dresden Germany and Center for Information Services and High-Performance Computing (ZIH)Technical University of Dresden Germany Center for Information Services and High-Performance Computing (ZIH) Technical University of Dresden Germany

ISBN: (纸本)9798400709067

data-driven design for cities is improving the quality of everyday life of citizens and optimizes the usage of resources. A new aspect is artificial intelligence, which Smart Cities could greatly benefit from. A central problem for urban designers is the unavailability of data to make relevant decisions. Agent-based simulations enable a view of the dynamic properties of the urban system, generating data in its course. However, the simulation must remain sufficiently simple to remain in the realm of computability. The research question of this paper is: How can we make agents behave more realistically to analyze citizens’ mobility behavior? To solve this problem, we first created a simulated virtual environment, where agents can move freely in a small part of a city, the harbor area in Hamburg, Germany. We assumed that happiness is a crucial motivating factor for the movement of citizens. A survey of 130 citizens provided the weights that govern the simulated environment and the happiness score assignation of places. As an AI method, we then used Reinforcement Learning as a general model and Q-learning as an algorithm to generate a baseline. Through randomly traversing the model environment a baseline was created. We are in the process of enhancing Reinforcement Learning with a Deep Q-Network to make the actors learn. Early experiments show a significant improvement over a tabular Q-learning approach. This paper contributes to the literature of urban planning, and data-driven architectural design. The main contribution is replacing the inefficient search for a global maximum of the happiness function, with an efficient local solution global maximum. This has implications for further research in the generation of synthetic data through simulations.

关键词： agent-based modeling artificial intelligence city simulations smart cities synthetic data urban design

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A Comparative Analysis of Different Feature Extraction Techniques for Motor Imagery Based BCI System 1st

A Comparative Analysis of Different Feature Extraction Techn...

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1st International Conference on artificial intelligence and Computer Visions, AICV 2020

作者： Selim, Sahar Tantawi, Manal Shedeed, Howida Badr, Amr Faculty of Media Engineering and Technology German University in Cairo Cairo Egypt Faculty of Computer and Information Science Ain Shams University Cairo Egypt Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt

ISBN: (纸本)9783030442880

Brain Computer Interface (BCI) system based on Motor Imagery (MI) signals is one among the most prospering systems in the field of BCI. Motor imagination does not involve motor output from the human. This can be used to help motor disabled people to accomplish elementary tasks by themselves. One of the primary difficulties facing MI-based BCIs is extracting discriminative features from the EEG signal. This study seeks to minimize the number of features input to classifier by finding out the best feature extraction techniques that can achieve high system accuracy with minimum computation cost. To achieve this purpose, this study compares five different feature extraction techniques;Root Mean Square (RMS), Renyi entropy, Shannon entropy, Katz fractal dimension and Common Spatial Patterns (CSP), regarding accuracy and execution time. To ensure that these techniques are reliable;they are applied on three benchmark datasets;BCI Competition III datasets IVa and IIIa, and BCI Competition IV dataset IIa. The extracted features are examined with two classifiers;Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA). Experimental results showed that CSP is the best feature extraction technique compared to other examined techniques, yet Renyi entropy has the least computation time which is a critical issue for an online system. © 2020, Springer Nature Switzerland AG.

关键词： Feature extraction

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ROBUST EARLY-LEARNING: HINDERING THE MEMORIZATION OF NOISY LABELS 9

ROBUST EARLY-LEARNING: HINDERING THE MEMORIZATION OF NOISY L...

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9th International Conference on Learning Representations, ICLR 2021

作者： Xia, Xiaobo Liu, Tongliang Han, Bo Gong, Chen Wang, Nannan Ge, Zongyuan Chang, Yi Trustworthy Machine Learning Lab School of Computer Science The University of Sydney Australia Department of Computer Science Hong Kong Baptist University Hong Kong School of Computer Science and Engineering Nanjing University of Science and Technology China ISN State Key Laboratory School of Telecommunications Engineering Xidian University China Medical AI Group Faculty of Engineering Monash University Australia Airdoc Research Monash University Australia School of Artificial Intelligence Jilin University China

The memorization effects of deep networks show that they will first memorize training data with clean labels and then those with noisy labels. The early stopping method therefore can be exploited for learning with noisy labels. However, the side effect brought by noisy labels will influence the memorization of clean labels before early stopping. In this paper, motivated by the lottery ticket hypothesis which shows that only partial parameters are important for generalization, we find that only partial parameters are important for fitting clean labels and generalize well, which we term as critical parameters;while the other parameters tend to fit noisy labels and cannot generalize well, which we term as non-critical parameters. Based on this, we propose robust early-learning to reduce the side effect of noisy labels before early stopping and thus enhance the memorization of clean labels. Specifically, in each iteration, we divide all parameters into the critical and non-critical ones, and then perform different update rules for different types of parameters. Extensive experiments on benchmark-simulated and real-world label-noise datasets demonstrate the superiority of the proposed method over the state-of-the-art label-noise learning methods. © 2021 ICLR 2021 - 9th International Conference on Learning Representations. All rights reserved.

关键词： Iterative methods

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Innovations in Wearable Radio Frequency technology: Designing for Connectivity

Innovations in Wearable Radio Frequency Technology: Designin...

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Information Technologies and Communications (GCITC), Global Conference on

作者： S. Parameswari A. Rajasekar K. Divya Smita Wagholikar P Ashok Soundara Rajan C Department of Electronics and Communication Engineering Sairam Institute of Technology Chennai Tamilnadu India Department of Artificial Intelligence and Data Science Sairam Institute of Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India Symbiosis Institute of Digital and Telecom Management (SIDTM) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India

The number of wireless gadgets made to be worn on the body has significantly increased in recent years. This is in part because wearable technology has been intruding on several facets of our lives, thanks to substantial advancements in processing, hardware design, and communication breakthroughs. For instance, more and more wearable technology is being developed to interface with our bodies and track vital signals like heart rate and blood pressure. Even with these advancements, battery technology has not kept up. In order to provide a better user experience, much effort has been made to identify novel materials and creative charging techniques. So this research work focusses on the recent advancements, designs and prototypes that are developed for application specific wearable devices.

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Boosting Anomaly Detection in Financial Transactions: Leveraging Deep Learning with Isolation Forest for Enhanced Accuracy

Boosting Anomaly Detection in Financial Transactions: Levera...

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artificial intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Prateek Kumar Bansal Divya Nimma Nripendra Narayan Das BVN Prasad Paruchuri Harishchander Anandaram M. Karthik Institute of Business Management GLA University Mathura India PhD in Computational Science Data Analyst in UMMC University of Southern Mississippi USA Department of Information Technology Manipal University Jaipur India Department of CSE KL University(Deemed to be) Vijayawada India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Anomaly detection in financial transactions is paramount for safeguarding against fraudulent activities that pose significant risks to financial institutions and customers alike. Traditional methods often struggle to accurately identify complex and evolving patterns of fraud, necessitating innovative approaches that leverage advanced techniques such as deep learning and isolation forest. In this study, we propose a novel framework for boosting anomaly detection in financial transactions by integrating deep learning with isolation forest to achieve enhanced accuracy. Firstly, it employ an autoencoder, a type of neural network, to learn complex representations of normal transaction patterns and reconstruct input data. The autoencoder's ability to capture subtle variations in transaction attributes enables it to effectively distinguish between normal and anomalous instances based on the reconstruction error. Furthermore, we augment the anomaly detection process by incorporating isolation forest, a tree-based algorithm that isolates anomalies in the feature space by recursively partitioning data subsets. By combining the representation learning capabilities of deep learning with the outlier detection prowess of isolation forest, our framework offers a comprehensive solution for detecting fraudulent activities in financial transactions. Through experimentation on real-world financial datasets, we demonstrate the superior performance of our proposed framework compared to existing methods. The proposed method is implemented in Python software and has an accuracy of about 99.12% which is 1.49% higher than other existing methods like Conv-LSTM, Convolutional Neural Network (CNN)-LSTM, and CNN-GRU (Gated Recurrent Unit). Moreover, the integration of deep learning with isolation forest enables our framework to adapt to evolving patterns of fraud, ensuring robust and reliable anomaly detection in dynamic financial environments. Overall, our study contributes to the advancement

关键词： Deep learning Accuracy Autoencoders Forestry Boosting Software Fraud Software reliability Convolutional neural networks Anomaly detection

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Application of deep learning technique to the sea surface height prediction in the South China Sea

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Acta Oceanologica Sinica 2021年第7期40卷 68-76页

作者： Tao Song Ningsheng Han Yuhang Zhu Zhongwei Li Yineng Li Shaotian Li Shiqiu Peng College of Computer and Communication Engineering China University of Petroleum(East China)Qingdao 266580China State Key Laboratory of Tropical Oceanography South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou 511458China Key Laboratory of Science and Technology on Operational Oceanography Chinese Academy of SciencesGuangzhou 511458China Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf Bubei Gulf UniversityQinzhou 535011China Department of Artificial Intelligence Faculty of Computer SciencePolytechnical University of MadridBoadilla del Monte 28660MadridSpain

A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs,in which the spatial features are“learned”by convolutional operations while the temporal features are tracked by long short term memory(LSTM).Trained by a reanalysis dataset of the South China Sea(SCS),ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in *** results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4%averaged over a 15-d prediction *** particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean *** the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction,and could be an alternative way in the operational prediction for ocean environments in the future.

关键词： deep learning sea surface height prediction convolutional operation long short term memory

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Utilizing IOT for Radiation Monitoring and Warning

Utilizing IOT for Radiation Monitoring and Warning

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Disruptive Technologies (ICDT), International Conference on

作者： Praful Sambhare S. Rukmani Devi J. Ramesh Kumar Arul Mary Rexy V Nallabothula Priyanka L. N. Jayanthi Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai India Department of ECE K.S.R. College of Engineering Tiruchengode India Department of Commerce Saveetha College of Liberal Arts and Sciences SIMATS Kuthambakkam India Doctor of pharmacy Krishna Teja pharmacy college Tirupati India

Children and newborns are more vulnerable to radiation exposure due to fast cell division, which may interfere with organs and cause skin problems. A system that detects hazardous radiation quickly and efficiently is in great demand. Many new and improved radiation protection and warning technologies are being offered. This research classifies radiation and its effects on newborns to show the development of an IoT-enabled system that utilizes artificial intelligence for radiation monitor and alarms. To help people escape hazardous zones, the suggested system uses audio/visual notifications. Gather the automated sensor system along with a real-time dataset of sensor values and their effects on newborns. Support vector machines, additional trees, bagged classification algorithms, random forests, logistic regression, and boosting adaptive classifiers classify radiation effects based on sensor measurement. This experiment shows the boosting adaptive classifier has the highest precision.

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Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense

arXiv

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arXiv 2025年

作者： Zhang, Haibo Yao, Zhihua Sakurai, Kouichi Saitoh, Takeshi Department of Artificial Intelligence Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Japan Faculty of Economics and Business Administration The University of Kitakyushu Japan Department of Informatics Faculty of Information Science and Electrical Engineering Kyushu University Japan

In the rapidly evolving field of artificial intelligence, machine learning emerges as a key technology characterized by its vast potential and inherent risks. The stability and reliability of these models are important, as they are frequent targets of security threats. Adversarial attacks, first rigorously defined by Ian Goodfellow et al. in 2013, highlight a critical vulnerability: they can trick machine learning models into making incorrect predictions by applying nearly invisible perturbations to images. Although many studies have focused on constructing sophisticated defensive mechanisms to mitigate such attacks, they often overlook the substantial time and computational costs of training and maintaining these models. Ideally, a defense method should be able to generalize across various, even unseen, adversarial attacks with minimal overhead. Building on our previous work on image-to-image translation-based defenses, this study introduces an improved model that incorporates residual blocks to enhance generalizability. The proposed method requires training only a single model, effectively defends against diverse attack types, and is well-transferable between different target models. Experiments show that our model can restore the classification accuracy from near zero to an average of 72% while maintaining competitive performance compared to state-of-the-art methods. Significantly, our model operates more efficiently, reducing the time needed to process individual images and speeding up the training process to achieve faster convergence. Robustness tests further confirm stable performance under varying attack strengths, demonstrating the model’s practical value in real-world adversarial settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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