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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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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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Normal Distribution Function on Descriptor Extraction 11th

Normal Distribution Function on Descriptor Extraction

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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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Exploiting Hybrid Active and Passive Multiple Access via Slotted ALOHA-Driven Backscatter Communications

arXiv

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arXiv 2022年

作者： Gu, Bowen Xie, Hao Li, Dong Liu, Ye Xu, Yongjun The Faculty of Information Technology Macau University of Science and Technology Avenida Wai Long Taipa999078 China The School of Computer Science and Engineering Macau University of Science and Technology Avenida Wai Long Taipa999078 China The College of Artificial Intelligence Nanjing Agricultural University Nanjing210031 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China

In conventional backscatter communication (BackCom) systems, various centralized access schemes, i.e., time division multiple access (TDMA) and frequency division multiple access (FDMA), are generally adopted for multi-user backscattering to avoid transmission collisions. However, as the number of backscatter devices (BDs) proliferates, there will be high overhead, and more seriously, the proactive inter-user coordination is unaffordable for the passive BDs, which are of scarce concern in existing works and remain a challenge to solve. To this end, in this paper, we leverage a slotted ALOHA (SA)based random access for multi-BD BackCom systems, in which each BD is randomly chosen and is allowed to coexist with one active device for hybrid active and passive transmission. To excavate and evaluate the performance, a resource allocation problem for max-min average throughput is formulated, where transmit antenna selection, receive beamforming design, reflection coefficient (RC) adjustment, power control, and access probability determination are jointly optimized. To deal with this intractable problem, we first transform the objective function with the max-min form into an equivalent linear one, and then decompose the resulting problem into three sub-problems. Next, a block coordinate descent (BCD)-based greedy algorithm with maximum ratio combining (MRC), semi-definite programming (SDP), and variable substitution is designed to obtain the suboptimal solution. Besides, we derive the closed-form solutions for RC and transmit power for tractable analysis. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of throughput and fairness. Copyright © 2022, The Authors. All rights reserved.

关键词： Time division multiple access

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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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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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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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Vietnamese event detection based on Chinese information and Vietnamese syntax guidance 20

Vietnamese event detection based on Chinese information and ...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Chen, Long Guo, Junjun Zhang, Yafei Gao, Shengxiang Yu, Zhengtao Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

Current event detection models based on deep learning rely on a sufficient amount of labeled data and only focus on specific in-formation such as trigger words. However, the scarcity of annotation data for Vietnamese news events and the ambiguity of event types have brought great challenges to Vietnamese event detection. According to the fea-ture that sentences expressing the same viewpoint but different languages usually have the same or similar semantic components, this paper proposes a Vietnamese event detection framework that combines Chinese information and Vietnamese syntax. First, use the shared encoder strategy and cross-attention network to integrate Chinese semantic information into Vietnamese, then use graph convolutional network to obtain Vietnamese representation based on Vietnamese dependency syntactic information. Finally, the Vietnamese semantic representation based on Chinese event type information is extracted through the event type perception network to realize Vietnamese news event detection. Experimental results show that under the guidance of Chinese information and Vietnamese syntax, Vietnamese event detection has achieved good results. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Deep learning

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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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A Joint Model with Multi-Granularity Features of Low-resource Language POS Tagging and Dependency Parsing 20

A Joint Model with Multi-Granularity Features of Low-resourc...

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20th Chinese National Conference on Computational Linguistics, CCL 2021

作者： Lu, Shan Mao, Cunli Yu, Zhengtao Gao, Shengxiang Huang, Yuxin Wang, Zhenhan Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China

The study of part-of-speech tags and dependency parsing of low-resource languages plays an important role in promoting low-resource natural language processing tasks. For low-resource language word embedding representation, the existing work does not make full use of character and sub-word level information encoding, resulting in models that cannot use features of different granularities. For this reason, a word embedding representation that integrates multi-granularity features is proposed, and different language models are used separately to obtained on different semantic information at the character, sub-word and word level. And three granular word embeddings are combined to achieve the purpose of enriching semantic information and alleviate the problem of poor performance of the dependency parsing model caused by the scarcity of annotation data. The part-of-speech tagging and dependency parsing model are further jointly trained, so that the models can share knowledge with each other and reduce the linear transmission of part-of-speech tagging errors on the dependency parsing task. Taking Thai and Vietnamese as the research objects, on the Penn Treebank data set, the proposed method is significantly improved compared to the baseline model UAS, LAS, and POS. © 2021 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Embeddings

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