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Decformer: A Multi-model Fusion Method Based on Decomposition and Transformer for Industrial Energy Consumption Prediction 16

Decformer: A Multi-model Fusion Method Based on Decompositio...

引用

16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Jun Li, Dagang Xia, Pengcheng Ni, Yiyang Qian, Yuwen School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Engineering Jiangsu Second Normal University Nanjing210013 China

ISBN: (纸本)9798350390643

As the industrial Internet of Things (IIoT) advances, the need for precise and effective long-term energy consumption forecasting grows more critical. Addressing this challenge relies on the technology of Time Sequence Forecast (TSF). Some ad-vanced deep learning methods have been used for TSF. However, existing work suffers from overly complex model structures and a lack of consideration for characteristics of the specific data. Therefore, we propose a time series prediction method called Decformer based on sequence decomposition and multi-model fusion. For components exhibiting strong periodicity, we use a simplified Transformer model with channel-independence processing to efficiently extract time-dimension feature information. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art time series prediction methods. © 2024 IEEE.

关键词： Prediction models

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Motorcycle driving violation detection based on improved YOLOv8 and DeepSORT

Motorcycle driving violation detection based on improved YOL...

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2024 International Conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Zhang, Long Jia, Wei The School of Computer Science and Information Engineering Hefei University of Technology 193 Tunxi Road Hefei230011 China

ISBN: (纸本)9781510681514

Aiming at the problem that it is not easy to detect motorcycle driving violations on the road, a motorcycle violation detection method with improved YOLOv8 and DeepSORT is proposed. The method uses the YOLOv8 algorithm as a target detection model, introduces a global attention mechanism (GAM Attention) in the backbone network to refine the learning of key semantic information and improve the feature extraction capability of the network;adopts the WIOU (Wise-IoU) loss function to accelerate the convergence speed and improve the regression accuracy;and uses DeepSORT as a target tracking model. ShuffleNetv2, a lightweight network, is introduced as the appearance feature extraction network to reduce the parameters of the model and maintain good accuracy;re-training on the motorcycle re-identification dataset improves the identity switching problem of the motorcycle target in the occlusion scenario. Finally, the tracked motorcycle targets are detected for driving violations. The experimental results show that compared with YOLOv8, the motorcycle and helmet detection accuracies are improved, reaching 98.2% and 98.6%, respectively;the size of the DeepSORT tracking model is reduced by 82%, which enhances the model's portability;and the algorithm has strong robustness in different traffic scenarios. Finally, the motorcycle target's driver and passenger did not wear a safety helmet, adding a parasol, and overloading three driving violations were detected, and the recognition rate reached 98.8%, 100%, and 98.5%, respectively, indicating that the method has some practical value. © 2024 SPIE.

关键词： Motorcycles

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Posture Prediction in Response to Seat Angle Change with Smart Chair

Posture Prediction in Response to Seat Angle Change with Sma...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Esumi, Tsubasa Takemura, Noriko Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Fukuoka Japan

ISBN: (纸本)9781665410205

Long-term desk work with lousy posture causes physical disabilities. To solve this problem, we have developed a smart chair with a controllable seat angle in our previous research. In order to control the seat angle and guide the user to the correct posture using this smart chair, it is necessary to investigate how the posture changes in response to the seat angle change. In this study, we first constructed a dataset by capturing posture data from 30 subjects while changing the seat angle of the smart chair. This dataset trained a model based on a graph neural network to predict posture in response to seat angle changes. We compared the model's prediction performance in the evaluation experiment depending on the training data and the body part. © 2024 IEEE.

关键词： Graph neural networks

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Fake News Detection Using Boosting Ensemble Method 14

Fake News Detection Using Boosting Ensemble Method

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Kaur, Kirandeep Kashyap, Nirbhay Amity School of Engineering and Technology Amity University Computer Science Department Uttar Pradesh Noida201301 India

ISBN: (纸本)9798350344837

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on 'Fake News Detection' dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options. © 2024 IEEE.

关键词： Statistical tests

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Joint Beamforming and Deployment Design for IRS-aided Energy Harvest IoT Network 3

Joint Beamforming and Deployment Design for IRS-aided Energy...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Liu, Zhenwei Zhang, Wenjie Cao, Qi Liang, Yunlong Huang, Wei He, Shiwen School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China School of Computer Science and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350335972

In this paper, we propose a transmission design scheme for the simultaneous wireless information and power transfer (SWIPT) in the intelligent reflecting surface (IRS) aided multi-user interference channel (IFC). Our goal is to maximize the harvested energy of all receivers by jointly optimizing the transmit and reflective beamforming vectors as well as the location of the IRSs while guaranteeing the rate of each user, deployable region of each IRS, and total transmit power constraints. The formulated optimization problem is hard to be solved due to the non-convex coupled of the variables related with location and beamforming vectors as well as the infinite number of constraints. To overcome the difficulty, we first design the location of IRSs by decoupling the larger-scale and small-scale fading of the reflective channels with the local region assumption. Then, based on this, we transform the transmit and reflective beamforming optimization problem into the second-order cone program (SOCP) and the relaxed semi-definite program (SDP) problems, which are convex and solved by utilizing the conventional interior point approach. Numerical results report that the gain is improved by optimizing the location of IRSs. © 2023 IEEE.

关键词： Optimization

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Jain, Anamika Langhe, Anagha Choudhary, Harsh Mishra, Ashutosh School of Computer Engineering and Technology Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798350372229

In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their spread and minimize any damage. However, this task often requires a high amount of labor and experience. This project suggests using image processing techniques to extract characteristics from images of plant leaves and then utilizing machine learning algorithms to classify these leaves as either healthy or diseased. The evaluation of machine learning algorithms, such as Convolutional Neural Networks, was conducted to measure their performance, using their accuracy, precision, and recall. The proposed system displays a level of accuracy in detecting and classifying plant diseases. It focuses on categorizing images of types of plants into disease types as well as healthy ones. These findings highlight the potential benefits of employing machine learning techniques in the detection of plant diseases offering farmers a tool for managing and safeguarding their crops. © 2024 IEEE.

关键词： agriculture classification crop management feature extraction Image processing machine learning

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Decentralized Deep Reinforcement Learning for Intelligent Transportation Systems 2

Decentralized Deep Reinforcement Learning for Intelligent Tr...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Bhide, Suvarna Chakurkar, Priti School of Computer Engineering and Technology Dr. Vishwanath Karad Mit World Peace University Pune India

ISBN: (纸本)9798350370188

This study introduces a novel MA2C (Multi-Agent Advantage Actor-Critic) approach for adaptive traffic signal control in complex urban traffic networks. MA2C addresses the scalability issue of centralized reinforcement learning (RL) by distributing the control to local RL agents. One method is proposed to reduce the learning difficulty of each agent. The effectiveness of MA2C can be evaluated in real-world traffic simulators using evaluation metrics of queue length and intersection delay. Further research can explore its efficiency by comparing it to other multi-agent reinforcement learning algorithms and its practical implications for traffic management systems, contributing to enhanced traffic flow, reduced congestion, and improved city transportation efficiency. © 2024 IEEE.

关键词： Reinforcement learning

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Hierarchy Affective Knowledge Enhanced Emotion Recognition Model 15th

Hierarchy Affective Knowledge Enhanced Emotion Recognition M...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Chen, Sufen Nie, Xiaofang Zeng, Xueqiang School of Information Engineering Nanchang Institute of Technology Jiangxi Nanchang330099 China School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China

ISBN: (纸本)9789819700677

Fine-grained emotion recognition models are better at capturing diverse emotions than traditional approaches. However, the huge number of category and interconnections of Fine-grained emotions pose challenges for traditional models. Existing fine-grained emotion recognition models overlook leveraging deep sentiment knowledge, such as the emotion hierarchy, in prediction models. To address this problem, this paper proposes a novel Emotion Recognition model based on Hierarchy Graph Convolution Networks (HGCN-EC). The HGCN-EC model comprises three modules: a semantic information module, an emotion hierarchy knowledge module, and a knowledge fusion module. The semantic information module extracts the semantic features from textual data;the emotion hierarchy knowledge module organizes fine-grained emotions into a tree-like hierarchy and calculates the transition probabilities between emotions using Bayesian statistical inference;the knowledge fusion module leverages a graph convolutional network to effectively integrate the hierarchy knowledge with the semantic information for emotion prediction. Comparative experiments on the GoEmotions dataset show that the proposed HGCN-EC outperforms baseline methods in the task of fine-grained emotion recognition. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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Resilience-Centric Trust Evaluation in Cloud Computing: A Comparative Assessment of Cloud Trust Evaluation Methods Using the RAA Metric

Resilience-Centric Trust Evaluation in Cloud Computing: A Co...

引用

2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： John, Jomina John Singh, K. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528126

In cloud computing environments, trust evaluation models are critical for assessing the reliability and security of cloud service providers. The accuracy of trust evaluations may be impacted by the failure of traditional trust measurements to take resilience under dynamic settings into consideration. In order to assess the flexibility and resilience of trust assessment models in cloud computing, this research presents a new metric called the Resilience-Aware Assessment. The Risk Assessment offers a thorough assessment of trust models in a variety of cloud environments by taking into account elements including system performance during high stress, reaction to malevolent activity, and recovery time following interruptions. In order to compare various trust evaluation models already in use, this study evaluates them using the RAA in addition to other traditional criteria like accuracy, scalability, and convergence time. The findings show that the RAA metric, especially in situations with uncertainty and volatility, successfully draws attention to trust model strengths and shortcomings that are missed by traditional metrics. The integration of the RAA metric into multi-criteria decision-making frameworks for cloud service selection is also covered in the article, offering a more adaptable and robust method for evaluating trust. Future research will examine how the RAA measure can be used to improve autonomous trust management systems in edge-cloud contexts as well as how it can be applied in real-time cloud orchestration. © 2024 IEEE.

关键词： Cloud security

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A Deep Q-Learning Approach to Intrusion Detection and Prevention Systems: Enhancing Cybersecurity through Intelligent Adaptation 4

A Deep Q-Learning Approach to Intrusion Detection and Preven...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Chandre, Pankaj Shinde, Manoj Bapurao Tilwant, Shruti Vilas Ghandat, Arun Bhau Shendkar, Bhagyashree D Dhotre, Prashant Mit Art Design and Technology University Mit School of Computing Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798331518981

A Deep Q-Learning approach to Intrusion Detection and Prevention Systems (IDPS) offers a cutting-edge solution for enhancing cybersecurity by leveraging intelligent machine learning models. This method dynamically adapts to evolving threats, learning optimal defense strategies through a reward-punishment system. The presented architecture integrates sensor-based traffic monitoring, feature extraction, and real-time decision-making to strengthen network resilience. The system leverages sensor nodes to monitor traffic and uses traffic analyzers to extract critical features from network data. A Deep Q-Learning model is employed to analyze these features, dynamically adjusting security policies by learning through reward and punishment mechanisms. The system interacts with network infrastructure, such as firewalls, routers, and switches, to mitigate threats in real time. Threat intelligence is continuously updated in a dedicated database, while an action executor enforces security measures and policies. This intelligent adaptation ensures proactive threat prevention and network resilience. © 2024 IEEE.

关键词： Cybersecurity

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