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Hybridized Deep learning Model with Optimization Algorithm: A Novel Methodology for Prediction of Natural Gas 22nd

Hybridized Deep Learning Model with Optimization Algorithm: ...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Majed, Hadeer Al-Janabi, Samaher Mahmood, Saif Department of Computer Science University of Babylon Hillah Iraq

ISBN: (纸本)9783031274084

This paper handles the main problem of natural gas through design the hybrid model based on developing one of predict data mining techniques. The model consists of four stages;The first stage collects data from a different source related to natural gas in real-time. The second stage, pre-processing is divided into multi steps including (a) Checking missing values. (b) Computing correlation among features and target. The third stage;building a predictive algorithm (DGSK-XGB). The fourth stage uses five evaluation measures in order to evaluate the results of the algorithm DGSK-XGB. As a results;we found DGSK-XGB give high accuracy reach to 93% compare with the tractional XGBoost;also, it reduces implementation time. And improving the performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Natural gas

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Enhancing Model-Based Traffic Signal Control with data-Driven Adaptive Optimization 22

Enhancing Model-Based Traffic Signal Control with Data-Drive...

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22nd COTA international conference of Transportation Professionals (CICTP) - Intelligence, Connectivity, and Mobility

作者： Zhang, Xuanyu Hu, Fuyu Huang, Wei Sun Yat Sen Univ Guangdong Prov Key Lab Intelligent Transportat Sy Sch Intelligent Syst Engn Guangzhou Peoples R China

ISBN: (数字)9780784484265

ISBN: (纸本)9780784484265

This paper proposes a network traffic control method to enhance model-based traffic control with data-driven online adaptive optimization. A macroscopic traffic flow model is first developed for the model prediction. Then, the model is further enhanced in real time based on the performance measurements using the adaptive optimization or learning algorithm. Integrating the data-driven optimization into the model-based predictive control, the proposed control method is able to identify the key model parameters and optimize the actual network performance. Experiments on a toy network are conducted to test the efficiency of the proposed control method. Simulation results show that the proposed method generates better control performance than the general model-based control method.

关键词： Toys

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Outlier Detection from Mixed Attribute Space Using Hybrid Model 22nd

Outlier Detection from Mixed Attribute Space Using Hybrid Mo...

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Sunitha, Lingam Bal Raju, M. Makka, Shanthi Ramasahayam, Shravya Department of CSE Vardhman College of Engineering Hyderabad India CSE Department Pallavi Engineering College Hyderabad India Software Development Engineer Banglore Flipkart India

ISBN: (纸本)9783031274084

Modern times have seen a rise in the amount of study being done on outlier detection(OD). Considering as setting the appropriate parameters for the majority of the existing procedures needed the guidance of a domain expert, and that the methods now in only use handle categorical or numerical data. Therefore, there is a requirement for both generalized algorithms for mixed datasets and current algorithm that can operate without domain interaction (i.e. automatically). The created system can automatically differentiate outliers from inliers in data having only one data type and data with mixed-type properties, such as data with either quantitative or categorical characteristics. The main objective of the described work is to removing outliers automatically. The current study makes use of the hybrid model called Hybrid Inter Quartile Range (HIQR) outlier detection technique. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Deep learning Models for Classification of Remotely Sensed data of Sugarcane 1

引用

2nd international conference on Advances in data-driven Computing and intelligent Systems, ADCIS 2023

作者： Kambli, Mansi Palkar, Bhakti Computer department K. J. Somaiya College of Engineering Somaiya Vidyavihar University Mumbai India

ISBN: (数字)9789819995219

ISBN: (纸本)9789819995202

The traditional machine learning algorithms are giving way to approaches for deep learning in computer vision, which refers to a computer's capacity to infer meaning from digital images and videos. Sugarcane categorization is important for agricultural management and monitoring. Traditional crop categorization methods based on manual inspection or restricted ground-based data gathering are time-consuming and frequently inaccurate. As a result, an automated and efficient strategy is suggested that requires the use of remote sensing data and the capabilities of deep learning algorithms. A dataset made from multispectral Sentinel imagery is used for the classification of sugarcane. This approach seeks to separate sugarcane-growing regions from other regions in Sentinel-2 images using VGG19, MobileNetV2, and CNN as feature extractors. These findings illustrate the feature extraction utilizing deep learning models with an SVM classifier for sugarcane. By considering variables such as distinct spectral bands, temporal fluctuations, and potential difficulties in separating sugarcane from other land cover types, the objective is to construct and check working of deep learning models for categorizing sugarcane locations using Sentinel-2 data. The sugarcane classification can further be used to find dense and sparse vegetation after the classification is done with deep learning models. The outcomes of this study will help to improve sugarcane categorization techniques and will help farmers, researchers, and agricultural stakeholders make better crop management, yield estimation, and resource optimization decisions in sugarcane farming. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Multimodal Identity Recognition of Spiking Neural Network Based on Brain-Inspired Reward Propagation 22

Multimodal Identity Recognition of Spiking Neural Network Ba...

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22nd IEEE international Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Yang Zhang, Hongxia Li, Yahui Wang, Quanqiang Liu, Cheng Henan University Henan Key Laboratory of Big Data Analysis and Processing Henan Kaifeng475004 China Henan University School of Computer and Information Engineering Henan Kaifeng475004 China Huaihe Hospital of Henan University Department of Clinical Laboratory Henan Kaifeng475004 China

ISBN: (纸本)9798331509712

Deep learning has strongly promoted the research progress of unimodal feature learning such as face recognition and speech recognition, but it still needs to be strengthened in multimodal data representation learning, especially the problem of high power consumption, which is not conducive to the real-time application of mobile devices under limited resources. In order to realize multimodal identity recognition for low-energy edge smart devices, this paper proposes a multimodal identity recognition method of spiking neural network based on brain-like mechanism (SNN-MIR-BiM). Simulating the attention mechanism of the cognitive system to specific information improves the response of multimodal features, mimics the regulation mechanism of the neurotransmitter dopamine in the nervous system, and releases reward signals from the recognition results, which affects the neuronal state and regulates the synaptic weights through global propagation, and then optimizes the network structure. Experimental results on large-scale audio and video datasets show that the proposed method can reach 98.94% recognition accuracy, and the energy consumption of the model is about 1/10 of that of CNN under the same architecture, which verifies the effectiveness of the attention mechanism and reward propagation in network design. This research is of great practical significance in the industry applications of unmanned autonomous systems such as human-computer interaction of intelligent machines and intelligent security. © 2024 IEEE.

关键词： Face recognition

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Quantized Transformer Language Model Implementations on Edge Devices 22

Quantized Transformer Language Model Implementations on Edge...

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22nd IEEE international conference on Machine learning and Applications, ICMLA 2023

作者： Ur Rahman, Mohammad Wali Abrar, Murad Mehrab Copening, Hunter Gibbons Hariri, Salim Shao, Sicong Satam, Pratik Salehi, Soheil University of Arizona Electrical and Computer Engineering TucsonAZ85721 United States University of Arizona Computer Science TucsonAZ85721 United States University of Arizona Systems and Industrial Engineering TucsonAZ85721 United States University of North Dakota Electrical Engineering and Computer Science Grand ForksND58202 United States

ISBN: (纸本)9798350345346

Large-scale transformer-based models like the Bidi-rectional Encoder Representations from Transformers (BERT) are widely used for Natural Language Processing (NLP) applications, wherein these models are initially pre-trained with a large corpus with millions of parameters and then fine-tuned for a downstream NLP task. One of the major limitations of these large-scale models is that they cannot be deployed on resource- constrained devices due to their large model size and increased inference latency. In order to overcome these limitations, such large-scale models can be converted to an optimized FlatBuffer format, tailored for deployment on resource-constrained edge devices. Herein, we evaluate the performance of such FlatBuffer transformed MobileBERT models on three different edge devices, fine-tuned for Reputation analysis of English language tweets in the Rep Lab 2013 dataset. In addition, this study encompassed an evaluation of the deployed models, wherein their latency, performance, and resource efficiency were meticulously assessed. Our experiment results show that, compared to the original BERT large model, the converted and quantized MobileBERT models have 160x smaller footprints for a 4.1 % drop in accuracy while analyzing at least one tweet per second on edge devices. Furthermore, our study highlights the privacy-preserving aspect of TinyML systems as all data is processed locally within a serverless environment. © 2023 IEEE.

关键词： BERT Embedded Systems loT Machine learning Natural Language Processing Privacy Reputation Polarity Social Media TinyML

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Finding Local Explanations Through Masking Models 22nd

Finding Local Explanations Through Masking Models

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22nd international conference on intelligent data engineering and automated learning

作者： Angiulli, Fabrizio Fassetti, Fabio Nistico, Simona Univ Calabria DIMES Dept Arcavacata Di Rende Italy

ISBN: (纸本)9783030916077;9783030916084

Among the XAI (eXplainable Artificial Intelligence) techniques, local explanations are witnessing increasing interest due to the user need to trust specific black-box decisions. In this work we explore a novel local explanation approach appliable to any kind of classifier based on generating masking models. The idea underlying the method is to learn a transformation of the input leading to a novel instance able to confuse the black-box and simultaneously minimizing dissimilarity with the instance to explain. The transformed instance then highlights the parts of the input that need to be (de-)emphasized and acts as an explanation for the local decision. We clarify differences with existing local explanation methods and experiment our approach on different image classification scenarios, pointing out advantages and peculiarities of the proposal.

关键词： eXplainable Artificial Intelligence Local explanations for machine learning Deep learning

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Toxic Comment Classification 22nd

Toxic Comment Classification

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Naseeba, B. Sai, Pothuri Hemanth Raga Karthik, B. Venkata Phani Chitteti, Chengamma Sai, Katari Avanija, J. VIT-AP University Amaravati India Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9783031274084

Presently in the world social media is ruling among the people. It is used by the people to share the views and contact others. It became the powerful weapon to the people. Users nowadays leave countless comments on various social media platforms, news portals, and forums. Some of the remarks are harmful or aggressive in nature. Because manually moderating comments is impractical due to the large volume of them, most systems rely on machine learning models to detect toxicity automatically. So, to protect people from the illegal comments and fake conversations and toxic words in the comments and posts. We used the Deep learning models like Convolution Neural Networks (CNNs), LSTMs, to detect the toxic words in the comments based on the toxic percentage. We conclude our work with a detailed list of existing research gaps and recommendations for future research topics connected to the subject of online harmful comment classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multilayer neural networks

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22nd IFIP TC 14 international conference on Entertainment Computing, ICEC 2023

22nd IFIP TC 14 International Conference on Entertainment Co...

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22nd IFIP TC 14 international conference on Entertainment Computing, ICEC 2023

ISBN: (纸本)9789819982479

The proceedings contain 48 papers. The special focus in this conference is on Entertainment Computing. The topics include: Seligman’s PERMA Model in Video Games – The Positive Influence of Video Games on Well-Being;the Meaning of Life (in Video Games);Method for Training Player Emotion in a Custom FPS Game Using Heart Rate;split Screen - Split Mind: An Innovative Format for Narrative Reflexive, and Pedagogical Games;accessibility Issues Within Serious Games in engineering Education for Visual Impairment;persuasive System Design for Climate Change Awareness;reasoning of intelligent Virtual Agents with Exceptional Behavior in Rare Scenarios;Game ON! a Gamified Approach to Household Food Waste Reduction;acquiring and Assessing 21st Century Skills Through Game Development: A Case Study with an Escape Game Template;semiotic Structuring in Movie Narrative Generation;girls vs. Men - The Prevalence of Gender-Biased Language in Popular YouTube Videos;i-Cube, an (In)tangible Interface for Multi-user Generative Art Creation;content Design for learning Support in Museum: Research on Exhibition Support System Using Tactile Senses;Exploring the Influence of Collision Anxiety on Player Experience in XR Games;Mutually Imperceptible Skill Adjustment in VR for Making Hyakunin Isshu Karuta Inclusive;Design of Collaborative learning Experience with AR–AR Collaboration Game for Children at Museum;Unity-VRlines: Towards a Modular eXtended Reality Unity Flight Simulator;augmented Adventures: Using Different Perspectives to Design Novel Tabletop Game Experiences with Augmented Reality;visualising Game Engine Subsystem Coupling Patterns;enhancing Stockfish: A Chess Engine Tailored for Training Human Players;a Serious Video Game on Cybersecurity;preface.

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