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Federated learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices 1

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3rd EAI international conference on Edge Computing and IoT, EAI ICECI 2022

作者： Wen, Wenkan Liu, Yiwen Gao, Yanxia Zhu, Zhirong Shi, Yuanquan Peng, Xiaoning School of Computer and Artificial Intelligence Huaihua University Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Hunan Huaihua418000 China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Hunan Huaihua418000 China

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： machine learning

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Tibetan medical named entity recognition study for Tibetan clinical electronic medical records 3

Tibetan medical named entity recognition study for Tibetan c...

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3rd international conference on Computer Science and Communication Technology, ICCSCT 2022

作者： Cuo, Zhuoma Jia, Cairang Duanzhu, Sangjie Zhuoma, Yangmao Zhaxi, Zhuoma The State Key Laboratory of Tibetan Intelligent Information Processing and Application Qinghai Xining810008 China

ISBN: (纸本)9781510661240

In current years, there are many open corpus and research results for Chinese clinical natural language processing (for short NLP) of Biomedical and Traditional Chinese Medicine. However, the research in this area for Traditional Tibetan Medicine lags behind. We know that Traditional Tibetan Medicine has its own unique set of theory system in treating and saving people. Therefore, it is imperative to speed up the research on the Tibetan Clinical Natural Language processing. Medical named entity recognition is an important subtask of Clinical Natural Language processing. So Tibetan medical named entity recognition is an urgent and basic research work for Traditional Tibetan Medicine. Due to the scarcity of labeled datasets, the Medical Named Entity Recognition task of Traditional Tibetan Medicine clinical text is still an unvisited researching area. In this work, we firstly manually construct a labeled dataset for this task and then explore this area with deep learning approaches by designing a Tibetan Lattice-LSTM-CRF neural network architecture. To further improve the model performance, we also incorporate both syllable and word level pre-trained representation. The final empirical results show that the proposed models could produce accuracy rate, recall rate and F1 values of 91.89%, 93.15% and 92.52%, respectively on our test set, which shows the validity of the model in the paper. © 2022 SPIE.

关键词： Natural language processing systems

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms

Diabetes Prediction Using Nature-Inspired Optimization Algor...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： J. Vijaya Divyanshu Tirkey Priyank Singh Aditya Goel Department of DSAI International Institute of Information Technology Naya Raipur chhattisgarh

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance.

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Internet of Things: A Survey on Fused machine learning-Based Intrusion Detection Approaches 1

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3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Gupta, Priyanka Yadav, Lokesh Tomar, Deepak Singh Department of Computer Science & Engineering Maulana Azad National Institute of Technology Bhopal India

ISBN: (数字)9789811908408

ISBN: (纸本)9789811908392

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT is to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, health care, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats;detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT intrusion detection system is presented. The deep learning methods are explored to provide an effective security solution for IoT intrusion detection systems. Then, the advantages and disadvantages of the methodology are discussed. Further, the open issues for future trends are provided. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Classification and Prediction of Incipient Faults in Transformer Oil by Supervised machine learning using Decision Tree

Classification and Prediction of Incipient Faults in Transfo...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Raymon Antony Raj Sarathkumar D Sampath Kumar Venkatachary Leo John Baptist Andrews Department of Electrical and Electronics Engineering Kongu Engineering College Erode district Tamilnadu India Grant Thornton Botswana Plot Acument Park Fair Grounds Gaborone Botswana Department of Information Technology Faculty of Engineering and Technology Botho University Gaborone Botswana

Transformer condition evaluation is a crucial technique for displaying a transformer’s health index. Key gases (KG), which dissolve in oil and become inseparable, are developed because of the different nascent flaws formed in the transformer enclosure. The exact defect that caused the gases to evolve may be found using the composition of KG present in the transformer. Five KG, including hydrogen H 2 , methane (CH 4 ), ethane (C 2 H 2 ), ethylene C 2 H 4 ), and acetylene (C 2 H 2 ), are employed in this study as an input to anticipate the real incipient defect. In the inquiry that follows, classification is carried out using a Decision Tree (DT) and the data is trained using a Supervised machine learning (SML). The dataset is made up of 180 different input KG compositions and defects that are trained, identified, and predicted using a predictive model. 16 more datasets with KG composition are utilized for prediction. TPR versus FNR, PPV vs FDR, AUC, parallel coordinates plot, confusion matrix, and lowest classification error are used to assess the statistical correctness. The accuracy of the projected data using DT and optimized DT is 62.9%, however the accuracy of the optimized DT with principal component analysis (PCA) is only 55%. Therefore, the suggested DT fault classification approach may be utilized to accurately forecast the onset of faults.

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Underwater Target Detection Based on machine learning 3

Underwater Target Detection Based on Machine Learning

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3rd IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Zhang, Wen Wu, Yanqun Lin, Yonggang Ma, Lina Han, Kaifeng Chen, Yu Liu, Chen Natl Univ Def Technol Coll Meteorol & Oceanol Changsha 410073 Peoples R China

ISBN: (纸本)9781728188232

Underwater target detection is an important part of acoustic signal processing. It is mainly composed of bad channel detection, beamforming, classification, tracking and localization. In this paper, the possibility of realizing bad channel detection, classification, tracking and localization based on machine learning or Deep learning was explored. And they were all implemented separately and successfully using machine learning or Deep learning.

关键词： machine learning deep learning bad channel detection beamforming classification target tracking source localization

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Indian Currency Classification and Counterfeit Detection Using Deep learning and Image processing Approach 3rd

Indian Currency Classification and Counterfeit Detection Usi...

引用

3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Muttreja, Ritvik Patel, Himanshu Goyal, Mayank Kumar, Santosh Singh, Anurag IIIT Naya Raipur Raipur India

ISBN: (纸本)9789811908392

These days, there is rampant usage of fake currency notes in every country, which is not good for the economy of that country. Further, the other side of the issue is the problem of visually impaired people to recognize bank notes. To address both of these problems, in this work we propose a deep learning model, which not only recognizes the real and fake Indian currency notes but also tells its correct denomination. We have employed various deep learning architectures for this task. In specific, we explored the applications of pre-trained deep learning models, like VGG16, GoogLeNet and MobileNet for currency classification and fake currency detection. The use of pre-trained deep learning models is motivated by the fact that these models are based on transfer learning and hence not data-hungry. As we had limited datasets available for this work, the pre-trained deep learning models were expected to give better performance. After evaluating the proposed approach on around 2572 images belonging to 6 denominations of Rs. 10, 20, 50, 100, 500, 2000, it was found that VGG16 obtained the highest classification as well fake detection accuracy of 98.08% and 97.95%, respectively. We further followed a conventional image processing-based approach and analyzed the discriminative feature differences between a real and a fake note. Various methods of preprocessing of the input image like edge detection, intensity mapping and HSV space conversion are done and the differences between the real and fake notes were analyzed, which can be used further for fake currency detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Study of machine learning Techniques to Mitigate Fraudulent Transaction in Credit Cards 3rd

Study of Machine Learning Techniques to Mitigate Fraudulent ...

引用

3rd international conference on machine Intelligence and signal processing, MISP 2021

作者： Sikder, Sayan Sarkar, Shubhasree Varghese, Eric G. Bhattacharjee, Pritam Department of CSE University of Engineering & Management West Bengal Kolkata700156 India Knowledge Sourcing Intelligence Uttar Pradesh Noida201301 India School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9789811908392

Two very important aspects that associate with the growth of human civilization are consumerism and the urge of minimizing human efforts, not only physical but also intellectual, for regular functioning. Due to the substantial growth of consumerism, a significant increase in credit card transactions can be observed in recent years and that certainly comes with rampant credit card fraud issues as well. This study suggests popular machine learning methods to mitigate fraudulent credit card transactions and explores the effectiveness of it. Six strategically different and popular classifiers, namely—random forest, XGBoost, decision tree, K-nearest neighbors, Naïve Bayes approach, and logistic regression, are employed to study their applicability in the credit card fraud detection problem. Out of the 6 algorithms taken into consideration for this particular problem, random forest classifier has turned out to be the best one and Naive Bayes has turned out to be the worst one in prediction according to conventional parameters such as accuracy, recall, specificity, and precision. This paper also intends to present a basic framework to select the best among state-of-the-art models to address the credit card fraudulency problem. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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The sparse learning of the support vector machine 3

The sparse learning of the support vector machine

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2021 3rd international conference on Artificial Intelligence Technologies and Applications, ICAITA 2021

作者： Cui, Lipeng Shen, Jie Yao, Song Tianjin Light Industry Vocational Technical College Tianjin300350 China

The sparse model plays an important role in many aeras, such as in the machine learning, image processing and signal processing. The sparse model has the ability of variable selection, so they can solve the over-fitting problem. The sparse model can be introduced into the field of support vector machine in order to get classification of the labels and sparsity of the variables simultaneously. This paper summarizes various sparse support vector machines. Finally, we revealed the research directions of the sparse support vector machines in the future. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Support vector machines

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Enhancing Interactive Storytelling: A Computational Approach to Autonomously Generating Role-Playing Game Scripts with Natural Language processing

Enhancing Interactive Storytelling: A Computational Approach...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Qijun Zhong Beijing World Youth Academy Beijing China

In the realm of role-playing games (RPGs), the crafting of rich and detailed narratives is crucial, with legendary games like Dungeons & Dragons (D&D) setting the bar for creativity and player-driven storytelling. This research explores the innovative intersection of artificial intelligence and RPGs by developing a method for the automated generation of RPG scripts, harnessing the capabilities of advanced natural language processing (NLP). Our study presents an elaborate NLP framework, utilizing a comprehensive collection of D&D source materials, to methodically extract key character features (such as attributes, races, and classes) using machine learning techniques. This framework is further enhanced by integrating sentiment analysis and a sophisticated bi-directional recurrent neural network equipped with a multi-head attention mechanism. This combination is instrumental in accurately modeling narrative elements like event sequences, causal relationships, and thematic links. The culmination of this process is the automated generation of diverse and engaging RPG scripts, each characterized by distinct character dynamics, immersive settings, and compelling story arcs. The effectiveness and impact of these generated scripts were evaluated through a user study that gathered both qualitative and quantitative data. The findings demonstrate our system's proficiency in creating narratives that are not only contextually diverse but also resonate deeply with RPG conventions, indicating a significant advancement in interactive storytelling. This research not only reinforces the role of AI in augmenting game design but also underscores the broader potential of AI in creative content generation, marking a new era in the synergy between computational technology and artistic narrative creation.

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