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8th International Congress on Information and Communication Technology, ICICT 2023

作者： Lecca, Paola Re, Angela Lombardi, Giulia Latorre, Roberta Valeria Sorio, Claudio Faculty of Computer Science Smart Data Factory Laboratory Free University of Bozen-Bolzano Bolzano Italy Member of National Group for Mathematical Analysis Probability and their Applications Francesco Severi’ National Institute of High Mathematics Rome Italy Department of Applied Science and Technology Politecnico di Torino Turin Italy Department of Mathematics University of Trento Trento Italy Department of Medicine University of Verona Verona Italy

ISBN: (纸本)9789819930906

Recent research emphasises the significance of identifying the latent geometry—also known as the geometry underlying a complex network—which is determined by the manifold class, curvature, and dimension. Geometry’s explanation of the organisational principles of complex systems and the potential that network nodes might be categorised according to their distances in this geometry are what confers geometry its significance. In this study, we analysed the network of genes resulting differentially expressed as a consequence of the modulation of protein tyrosine phosphatase receptor type G (PTPRG) in chronic myeloid leukaemia (CML) cell model K562 transcriptome. We found that the latent geometry of this network is hyperbolic, and that clustering according to the angular coordinates of its nodes in hyperbolic space classifies genes by functional pathway classes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational geometry

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Snapshot boosting: a fast ensemble framework for deep neural networks

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science China(Information sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

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Implementing and Analyzing Behaviour of ML based Tamil Songs Recommendation System 2

Implementing and Analyzing Behaviour of ML based Tamil Songs...

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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Garg, Shalini Pooja Chaoudhary, Shailee Lohmor Dixit, Rinku Govil, Nikhil Design and Technology University Mit Art MH Pune India Galgotias University School of Cse Delhi - NCR Gr. Noida India New Delhi Institute of Management Department of Aiml - Data Science New Delhi India Gla University Department of Computer Engineering & Applications U.P. Mathura India

ISBN: (纸本)9781665491990

Music gives listeners a chance to relax, relieve fatigue and connect one culture with another. In such a situation, if the favorite song-music is heard one after another and according to the emotion, it gives a unique and wonderful experience to the listener. It is determined based on the emotion and interest, ratings provided by listeners and search patterns. In this research article, the proposed method presents Tamil songs compilation and recommendation system with the help of these factors. The behavior of this recommendation system is also analyzed in this system. The proposed system is developed in Java programming language by adopting the techniques of Machine Learning. Through this system, listeners have the choice to play category wise songs. As per the listening history and patterns, upcoming songs are predicted to the listeners. This system also provides ratings to each song. This developed application can also be used commercially as it has been validated that the ratings provided by the listeners are in line with IMDb ratings. © 2023 IEEE.

关键词： Recommender systems

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Revolutionizing IoT Network Security with Deep Learning-Anomaly Detection Model 4

Revolutionizing IoT Network Security with Deep Learning-Anom...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vyas, Manish Vijayaganth, R. Chandhok, Jigyasha Srivastava, Aviral Arumugam, S. Tiwari, Mohit College Name & Address: Vikrant Institute of Technology and Management Computer Science Department Indore India Artificial Intelligence and Data Science M.Kumarasamy College of Engineering Thalavapalayam Karur639113 India Tula's Institute of Engineering and Management Naac Grade A+ Accredited Institute Computer Science & Engineering UttarakhandDhoolkot Dehradun India Amity University Rajasthan NH 11 C kant kalwar Jaipur300202 India Periyar Maniammai Institute of Science & Technology Periyar Nagar Vallam Computer Science and Applications Thanjavur613403 India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering A-4 Rohtak RoadPaschim Vihar Delhi India

ISBN: (纸本)9798350300093

The term 'Internet of Things' (IoT) is used to describe the collection of data and the connectivity of items to the web that requires little to no human interaction. The IoT is a network of interconnected devices that can collect and disseminate information. Increased security and privacy worries accompany the launch of new devices due to the proliferation of Internet connections and the development of cutting-edge technology like the IoT. These days, the IoT is used everywhere, but especially in logistics, manufacturing, and healthcare. While these emerging IoT applications greatly enhance the usefulness of smart objects, they also present new security risks. Because of this, adapting existing intrusion detection systems (IDS) for use with IoT networks is a topic of intense study. Many IDS experts have found success with machine learning (ML) and deep learning (DL) techniques. By combining deep extraction through the Convolutional autoencoder with deep learning to identify the best features, this work delivers an improved IDS that can be used for anomaly detection. Improves to the deep learning approach include an evaluation of hyperparameter effectiveness, a stage of feature pruning using an autoencoder neural network, and an examination of the sturdiness of the most effective deep neural networks for circumstances exaggerated by Gaussian noise over some of the features in question. Despite the noise, the results show that the formed IoT dataset is useful for anomaly detection with deep learning methods. © 2023 IEEE.

关键词： Internet of things

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A Study on AI applications for Orthodontics and Malocclusion Detection Approaches

A Study on AI Applications for Orthodontics and Malocclusion...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Ankitha A Nayak Venugopala P S Ashwini B Padmashree G Department of Computer Science and Engineering Nitte NITTE (Deemed to be University) NMAM Institute of Technology Udupi India Dept. of Artificial Intelligence and Data Science NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Computer Science and Engineering NMAM Institute of Technology NITTE (Deemed to be University) Udupi India Department of Data Science and Computer Applications Manipal Academy of Higher Education Manipal Institute of Technology Manipal India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Orthodontics is a dental specialty focused on treating, diagnosing, and preventing malocclusion and other dental irregularities. In a complex human digestive system, the anatomy of teeth has a significant role. Moreover, maintaining a healthy dental anatomy is essential to ensure a vibrant lifestyle. Orthodontics plays a vital role in the health of teeth structure by providing solutions for crooked teeth, tooth decay, periodontal disease, TMJ syndrome, neck shoulder pain, headaches, and pleasing facial appearance. This paper showcases a comprehensive review of the background and evolution of the orthodontics area through the years, emphasizing more methodologies for fixing malocclusion. The field of orthodontics needs significant manual intervention throughout the entire process, starting from the initial detection of the problem to the final stage of rectifying the fault. This paper presents a fair comparative visualization of manual work cut down with technical advancements in orthodontics and malocclusion detection techniques, spanning from the Bio-mechanical to Artificial Intelligent era. For a better descriptive analysis, we have analyzed the various existing works in orthodontics and malocclusion detection connecting to a set of well-defined research questions. The study of available algorithms, technologies data sets, and gaps for orthodontics and malocclusion detection technique is outlined concerning observed results in the survey.

关键词： Surveys Reviews Pain Teeth Manuals Very large scale integration Dentistry Artificial intelligence Robots Periodic structures

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Research on recognition algorithm for gesture page turning based on wireless sensing

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Intelligent and Converged Networks 2023年第1期4卷 15-27页

作者： Lin Tang Sumin Wang Meng Zhou Yinfan Ding Chao Wang Shengbo Wang Zhen Sun Jie Wu Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai Institute for Advanced Communication and Data ScienceSchool of Communication and Information EngineeringShanghai UniversityShanghai 200444China School of Electronic Information Engineering Gannan University of Science and TechnologyGanzhou 341000China Affiliated High School of Peking University Beijing 100086China Peking University&National Engineering Laboratory for Big Data Analysis and Applications Beijing 100871China Department of Computer and Information Sciences Temple UniversityPhiladelphiaPA 19122-6096USA

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human *** extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be *** the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide *** algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification *** experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

关键词： Wi-Fi signal Channel State Information(CSI) wireless sensing human behavior recognition

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Enhanced Residual U-Net with Attention for Optic Disc and Cup Segmentation in Fundus Images 1st

Enhanced Residual U-Net with Attention for Optic Disc and...

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1st International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024

作者： Kamath, Ritesh V. Kedari, Bhavana Girisha, S. Savitha, G. Shreesha, S. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9783031714832

Glaucoma, a progressive visual disease, damages the optic nerve, potentially resulting in irreversible vision loss. Effective treatment plans and prompt diagnosis are essential for positive patient outcomes. The principal glaucoma-related indication typically revolves around an odd ratio between the diameters of the optic cup and disc. In this regard, fundus images are analyzed for diagnosis of glaucoma. However, image quality, similar characteristics across classes, class imbalance issues, uneven shapes of optic disc and curves, and other factors make correct segmentation of optic disc and curve difficult from fundus images. To address these issues, the work proposes an enhanced Residual U-Net architecture capable of semantically segmenting the optic disc and cup from retinal fundus images. The proposed model has an Attention Module that can effectively extract the necessary feature map for segmentation from the input images. This improves the model’s capacity to pick out important features and eliminate extraneous noise. Using a publicly available dataset, the study analyses the efficacy of the proposed model using both quantitative and qualitative measures. The effectiveness of the model is examined in relation to various loss functions. To emphasize the significance of the suggested model, a comparative analysis between the proposed and the traditional model is also carried out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Patient treatment

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Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

arXiv

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arXiv 2024年

作者： Yao, Quanming Zhang, Yongqi Wang, Yaqing Yin, Nan Kwok, James Yang, Qiang Department of Electronic Engineering the Tsinghua University China Department of Data Science and Analytics Thrust the Hong Kong University of Science and Technology Guangzhou China Beijing Institute of Mathematical Sciences and Applications China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. © 2024, CC BY.

关键词： Contrastive Learning

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Novel machine learning investigation for Buongiorno fluidic model with Sutterby nanomaterial

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Tribology International 2024年 199卷

作者： Khan, Maryam Pervaiz Chang, Chuan-Yu Raja, Muhammad Asif Zahoor Shoaib, Muhammad Department of Computer Science and Information Engineering Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Data Science and AI Applications Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Yunlin 64002 Taiwan AI center Yuan Ze University Taoyuan320 Taiwan

The nanomaterials are frequently employed in a variety of heat transfer applications arising in energy generation, engine cooling, extrusion procedures, heat exchanger, thermos-chemical systems, manufacturing structures, powered plants etc. Experts and researchers in these fields often encounter such materials, leading to discernible impacts on velocity, temperature and concentration profiles. The objective of this study is to represent the mathematical structures for Sutterby nanomaterial fluidic model involving porous medium BFM-SNM using nonlinear autoregressive exogeneous networks backpropagated with Levenberg-Marquardt technique (NARX-LMT). The mathematical design of the model is originally presented by PDEs, which are converted into ODEs system through suitable modifications with alternative transformation incorporating numbers, i.e., Deborah, Prandtl and Reynold, as well as parameters, i.e., magnetic thermophoresis, radiation and temperature. The synthetic data is produced numerically by simulating Adams numerical method for BFM-SNM and the supervised computing paradigm of NARX-LMT is applied to the obtained datasets, and the results of NARX-LMT consistently align with numerical observations for each variant of the presented model, exhibiting negligible errors. The NARX-LMT performance on exhaustive experimentation is effectively illustrated through iterative convergence curves on mean squared error, control metrics of the optimization, distribution of error on histograms and regression outputs for Sutterby nanomaterial fluidic model. © 2024 Elsevier Ltd

关键词： Ordinary differential equations

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