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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sowmya, P. Ravichandran, Sathish Kumar Rakshitha Department of Robotics and Artificial Intelligence Karnataka Nitte India Christ University School of Engineering and Technology Department of Computer Science Karnataka Bangalore India Department of Artificial Intelligence and Machine Learning Karnataka Nitte India

ISBN: (纸本)9781665462167

Industry 4.0 is digitized revolution for manufacturers or companies where in new technologies are imbibed into their production system for their day-to-day operations or activities. So that their overall economic needs and efficiency can be improved. In manufacturing industry maintenance of the equipment is the key concern. When the equipment requires maintenance, it has to be done at the earliest, failing which companies will have to face consequences in terms of loss of customers, time and money. Solution is provided to this problem in terms of a technique called predictive maintenance. The content of the article focuses on different predictive maintenance strategies, which help manufacturers to forecast if equipment/component will fail so that its maintenance and repair can be scheduled exactly before the component fails. The results will be useful for manufacturers to understand the importance of industry 4.0 for predictive maintenance. © 2023 IEEE.

关键词： Industry 4.0

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Forecasting of Photovoltaic Power with ARO based AI approach 2

Forecasting of Photovoltaic Power with ARO based AI approach

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Nalini, B.M. Khan, Zabiha Nayana, S. Achyutha, Prasad N. Manjula, G. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bgs College of Engineering and Technology Department of Cse Bangalore India

ISBN: (纸本)9798350382952

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. Systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work. © 2024 IEEE.

关键词： Convolutional neural networks

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One Policy to Run Them All: an End-to-end learning Approach to Multi-Embodiment Locomotion 8

One Policy to Run Them All: an End-to-end Learning Approach ...

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8th Conference on Robot learning, CoRL 2024

作者： Bohlinger, Nico Czechmanowski, Grzegorz Krupka, Maciej Kicki, Piotr Walas, Krzysztof Peters, Jan Tateo, Davide Department of Computer Science Technical University of Darmstadt Germany Institute of Robotics and Machine Intelligence Poznan University of Technology Poland Research Department: Systems AI for Robot Learning Germany IDEAS NCBR Warsaw Poland Hessian.AI Germany Centre for Cognitive Science Germany

Deep Reinforcement learning techniques are achieving state-of-the-art results in robust legged locomotion. While there exists a wide variety of legged platforms such as quadruped, humanoids, and hexapods, the field is still missing a single learning framework that can control all these different embodiments easily and effectively and possibly transfer, zero or few-shot, to unseen robot embodiments. We introduce URMA, the Unified Robot Morphology Architecture, to close this gap. Our framework brings the end-to-end Multi-Task Reinforcement learning approach to the realm of legged robots, enabling the learned policy to control any type of robot morphology. The key idea of our method is to allow the network to learn an abstract locomotion controller that can be seamlessly shared between embodiments thanks to our morphology-agnostic encoders and decoders. This flexible architecture can be seen as a potential first step in building a foundation model for legged robot locomotion. Our experiments show that URMA can learn a locomotion policy on multiple embodiments that can be easily transferred to unseen robot platforms in simulation and the real world. © 2024 Proceedings of machine learning Research.

关键词： Deep reinforcement learning

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A Review of machine learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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An Effective Method for Distributing Workloads in Smart City using Sensor Networks Enabled by Fog

An Effective Method for Distributing Workloads in Smart City...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Nithiyanandam Velvizhi, R. Priyadharshini, S.P. Poongavanam, N. SRM Institute of Science and Technology Kattankulathur Campus Department of Computing Technologies Chengalpattu Kattankulathur India Sathyabama Institute of Science and Technology OMR Department of Computer Science and Engineering Chennai India St' Joseph College of Engineering OMR Department of Artificial Intelligence and Machine Learning Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

By linking sensor nodes to fog nodes in critical locations, the Distributing Workloads in Fog Enabled Smart City (DW-FESC) technique efficiently handles the problem of distribution of workloads in smart cities. To get over the processing limits of IoT devices, this setup makes sure that complicated tasks are sent off to neighbouring fog nodes. The plan uses a load balancer and an updated greedy algorithm to evenly distribute work, which speeds up execution and keeps loads from becoming too heavy. Additionally, it has a deadline-based task execution mechanism that makes sure the most important jobs are done on time. The simulation results show that DW-FESC keeps the variance from the mean execution times to a minimum while considerably improving task execution times in comparison to other methods. With the use of fog computing, DW-FESC improves the dependability, efficiency, and responsiveness of smart city applications, leading to greener city life. © 2024 IEEE.

关键词： Fog computing

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Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-trust Networks

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2312-2327页

作者： Wang, Tingting Fang, Kai Cai, Jijing Wang, Lina Tian, Jinyu Feng, Hailin Li, Jianqing Guizani, Mohsen Wang, Wei Macau University of Science and Technology Faculty of Innovation Engineering School of Computer Science and Engineering Macau999078 China Zhejiang A&F University College of Mathematics and Computer Science Hangzhou311300 China Machine Learning Department United Arab Emirates Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Traditional traffic anomaly-based attack detection methods in Zero-trust Networks (ZTN) suffer from inherent security vulnerabilities, as they neglect considerations regarding their security defenses. Compromising the attack detection model itself can result in the breakdown of normal attack detection capabilities. Ensuring the security of the attack detection model during runtime presents a novel challenge. To address these shortcomings, we propose a novel attack detection model, termed Security within Security: Attack Detection Model with Defenses Against Attacks Capability for Zero-Trust Networks (SWS), aimed at enhancing the security of ZTN. SWS focuses on achieving attack detection in non-secure detection environments, to maintain its detection capability even when under attack. By employing a soft thresholding method, SWS adapts to the dynamic changes in network traffic, thus reducing the interference of attack signals. The incorporation of an attention mechanism enables SWS to concentrate on analyzing the most indicative traffic features of attack behavior. Additionally, we integrate Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (BiLSTM) to enhance the robustness of identifying complex network attack behaviors. The effectiveness of the SWS is validated through ablation studies, model comparisons, experiments conducted over different training epochs, and experiments conducted on various components of the dataset. Experimental results demonstrate that compared to existing attack detection models, SWS achieves improvements in detection accuracy and recall rate by 13.4% and 10.6%, respectively, while reducing the False Positive Rate (FPR) by 16.9%. © 1983-2012 IEEE.

关键词： Economic and social effects

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Grammar Error Correction Using Natural Language Processing

Grammar Error Correction Using Natural Language Processing

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Geetha, M. Vijaya, K. Subramanian, K.M. Rajesh Kanna, P. Rajeshkumar, G. Karthika, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India Kongu Engineering College Department of Computer Science and Design Erode India Shadan College of Engineering and Technology Department of Computer Science and Engineering Hyderabad India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institue of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India

ISBN: (纸本)9798350313987

In the digital era, mos t of the communication is happening through online platforms. English plays a vital role for better understanding. Good grammar helps in framing a better sentence. This demands for the development of an automatic Grammar Error Correction (GEC) tool. Many research works have been initiated to make Grammar Error Correction possible by using various machine learning algorithms. Some of the algorithms are Naive Bayes, BERT, Support Vector machine, and ULMFIT. Among these algorithms BERT gives a comparatively best accuracy rate. The fundamental goal of this research work is to compare these algorithms with respect to accuracy Here, NLP is used for analyzing the grammatical corrections in the sentence. Further, this study can be extended to overcome the drawbacks faced by the BERT algorithm such as increase in time cons umption for training and cos t. © 2023 IEEE.

关键词： Bidirectional Encipher Representations from Instruments Grammar Error Correction Natural Language Processing Universal Language Model Fine-Tuning

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Intelligent cache and buffer optimization for mobile VR adaptive transmission in 5G edge computing networks

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Digital Communications and Networks 2024年第5期10卷 1234-1244页

作者： Junchao Yang Ali Kashif Bashir Zhiwei Guo Keping Yu Mohsen Guizani Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology School of Artificial IntelligenceChongqing Technology and Business UniversityChongqing400067China Department of Computing and Mathematics Manchester Metropolitan UniversityUK Woxsen School of Business Woxsen UniversityHyderabad 502345India Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Graduate School of Science and Engineering Hosei UniversityTokyo184-8584Japan Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence(MBZUAI)United Arab Emirates

Virtual Reality(VR)is a key industry for the development of the digital economy in the *** VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of *** this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive *** support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint *** tile-based content is proactively cached in the MEC nodes based on the popularity of the VR ***,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client ***,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are ***,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.

关键词： Virtual reality Adaptive transmission Edge cache Buffer management 5G Mobile edge computing

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GITA: graph to visual and textual integration for vision-language graph reasoning 24

GITA: graph to visual and textual integration for vision-lan...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yanbin Wei Shuai Fu Weisen Jiang Zejian Zhang Zhixiong Zeng Qi Wu James T. Kwok Yu Zhang Department of Computer Science and Engineering Southern University of Science and Technology and Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology and Australia Institute for Machine Learning University of Adelaide Tencent Australia Institute for Machine Learning University of Adelaide Department of Computer Science and Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

ISBN: (纸本)9798331314385

Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in the textual format, they overlook the rich vision modality, which is an intuitive way for humans to comprehend structural information and conduct general graph reasoning. The potential benefits and capabilities of representing graph structures as visual images (i.e., visual graph) are still unexplored. To fill the gap, we innovatively propose an end-to-end framework, called Graph to vIsual and Textual IntegrAtion (GITA), which incorporates visual graphs into general graph reasoning. Besides, we construct the Graph-based Vision-Language Question Answering (GVLQA) dataset from existing graph data, which is the first vision-language dataset for general graph reasoning. Extensive experiments on the GVLQA dataset and five real-world datasets show that GITA outperforms mainstream LLMs on general graph reasoning. Moreover, experimental results demonstrate the effectiveness of the layout augmentation on visual graphs and pretraining on the GVLQA dataset.

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PROCEDURAL FAIRNESS THROUGH DECOUPLING OBJECTIONABLE DATA GENERATING COMPONENTS 12

PROCEDURAL FAIRNESS THROUGH DECOUPLING OBJECTIONABLE DATA GE...

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12th International Conference on learning Representations, ICLR 2024

作者： Tang, Zeyu Wang, Jialu Liu, Yang Spirtes, Peter Zhang, Kun Department of Philosophy Carnegie Mellon University United States Computer Science and Engineering Department University of California Santa Cruz United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates ByteDance Research China

We reveal and address the frequently overlooked yet important issue of disguised procedural unfairness, namely, the potentially inadvertent alterations on the behavior of neutral (i.e., not problematic) aspects of data generating process, and/or the lack of procedural assurance of the greatest benefit of the least advantaged individuals. Inspired by John Rawls's advocacy for pure procedural justice (Rawls, 1971;2001), we view automated decision-making as a microcosm of social institutions, and consider how the data generating process itself can satisfy the requirements of procedural fairness. We propose a framework that decouples the objectionable data generating components from the neutral ones by utilizing reference points and the associated value instantiation rule. Our findings highlight the necessity of preventing disguised procedural unfairness, drawing attention not only to the objectionable data generating components that we aim to mitigate, but also more importantly, to the neutral components that we intend to keep unaffected. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

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