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A Collaboration Federated Learning Framework with a Grouping Scheme against Poisoning Attacks 6

A Collaboration Federated Learning Framework with a Grouping...

引用

6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Liu, Chuan-Kang Chiang, Chi-Hui National Chin-Yi University of Technology Dept. of Artificial Intelligence and Computer Engineering Taichung Taiwan Chia Nan University of Parmacy and Science Dept. of Information Management Tainan Taiwan

ISBN: (纸本)9798350301953

Federated learning has been regarded as emerging machine learning framework due to its privacy protection. In the IoT trend, federated learning enables edge clients to predict or classify local detected data with a global model that is computed by a FL server through the aggregation of all local models trained by a base FL algorithm. However, meanwhile, its distributed nature also brings several security challenges. Poisoning attacks are the main security risks that can easily and efficiently affect the accuracy of the global learning model. Previous work proposed a voting strategy which can predict the label of the input robustly no matter the attacks the malicious users use. However, its accuracy also easily falls down as the number of malicious user increases while the number of groups is fixed. This paper proposes a new attack defense algorithm against poisoning attacks in federated learning. This paper uses ID-distribution features to group all clients, including normal and malicious ones. The main idea of this proposed scheme is to put those potential malicious clients in specified groups. Hence, the resulting vote output can accurately classify the dataset inputs, regardless of the number of the groups the learning framework has. Our analytical results also show that our scheme exactly perform better compared to original voting scheme. © 2023 IEEE.

关键词： Classification (of information)

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Design of Coffee Yield Estimation System Using computer Vision by Crowd Counting Model

Design of Coffee Yield Estimation System Using Computer Visi...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Kuei-Chung Lin, Yu-Lung International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Taiwan Feng Chia University Dept. of Information Engineering and Computer Science Taiwan

ISBN: (纸本)9798350324174

For coffee farmers, the yield of coffee is an issue that will affect their income. At present, the method used to estimate the coffee production is usually to estimate the production of the entire coffee garden by weighing the coffee till the harvest;however, this method does not have a way to understand the reasons of affecting coffee production in each period of the planting process. In this paper, we propose an intelligent system using a crowd counting model to estimate the yield of coffee, so that farmers can observe changes in the amount of coffee during the planting process from CCTVs remotely. Experimental results show that the proposed method can obtain estimated results with mean absolute error about 6.91. © 2023 IEEE.

关键词： computer vision

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Advanced Machine Learning Techniques for Enhancing Data Security in Cloud Computing Systems 5

Advanced Machine Learning Techniques for Enhancing Data Secu...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

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Heart arrhythmia detection based on ECG wave classification using neural network and Fuzzy Inference system 2023

Heart arrhythmia detection based on ECG wave classification ...

引用

15th International Conference on Contemporary Computing, IC3 2023

作者： Waseem, Zoha Haq, Zeeshan Ali Mahto, Anil Kumar Dept. of Computer Science & Engineering SEST Jamia Hamdard New Delhi India Dept of CSE & IT Jaypee Institute of Information Technology UP Noida India

ISBN: (纸本)9798400700224

Detecting the Arrhythmia correctly is an important task in cardiovascular disease detection. Identifying the correct type of arrhythmia provides a fast and effective way for diagnosis. In this paper a neural network based (ANN & CNN) and fuzzy logic based classifiers (ANFIS) are presented and their performance is evaluated using performance evaluation parameters. For this study, MITBIH and PTB diagnostic database are used which provided the total sample size of 1,23,998 ECG waveforms. For classification purpose, features are extracted such as Skewness, Kurtosis, Mean, Standard Deviation, Shannon Entropy, and Energy. In this study, it has been observed that ANFIS based classifier performs better than neural network based classifier. © 2023 ACM.

关键词： Electrocardiograms

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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Sengendo, John Granelli, Fabrizio University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798350368369

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the 'look-back period' and 'forecast horizon' significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network. © 2025 IEEE.

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Optimizing Network Traffic Prediction for Network Digital Twins: The Impact of Look-Back Period and Forecast Horizon

Optimizing Network Traffic Prediction for Network Digital Tw...

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IEEE Conference on Wireless Communications and Networking

作者： John Sengendo Fabrizio Granelli Dept. of Information Engineering and Computer Science University of Trento Trento ITALY

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Over recent years, many advancements in networks have taken place with now the advent of 6G. With these growing advancements, challenges in managing networks also emerge. Network Digital Twins (NDTs) is one of the potential technology in solving many of these challenges because of the capability to virtually replicate the physical network elements. One of the key challenges is predicting network traffic especially with the exponentially growing number of devices in the network. In this paper, we study and show how varying the “look-back period” and “forecast horizon” significantly affects traffic predictions. Look-back period or window size is how much of the historical data is used by a prediction algorithm to make predictions. Forecast horizon is how far in the future we can predict. These highly impact how accurately traffic is predicted in networks and as well significantly determine how well Digital Twins (DTs) for networks accurately reflect traffic of the physical network.

关键词： Accuracy Costs Heuristic algorithms Memory management Adaptive algorithms Telecommunication traffic Traffic control Predictive models Prediction algorithms Digital twins

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An Automated and Generalizable Technique for Left Ventricle Segmentation in 2D Echocardiography Utilizing Generative Adversarial Network

An Automated and Generalizable Technique for Left Ventricle ...

引用

IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Sajjad Afrakhteh Noreen Fatima Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Accurately segmenting the left ventricle (LV) is crucial for understanding normal heart anatomy and identifying abnormal or diseased structures based on the estimated ejection fraction (EF). A significant challenge in LV segmentation is ensuring generalizability across diverse datasets with various acquisition settings. Addressing this challenge is a key focus, and we aim to provide a solution to ensure a generalizable segmentation. This study utilizes Pix2Pix Generative Adversarial Networks (GANs) for robust and adaptable left ventricle (LV) segmentation in 2D echocardiography images. Specifically, through the integration of adversarial loss, our model learns to generate anatomically accurate segmentations, which helps to enhance accuracy and adaptability across diverse echocardiography datasets. Our evaluation involved two datasets: the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset and the EchoNet dataset, both captured from a 4-chamber (4CH) view. To assess generalizability, we train the model using the EchoNet training set and test it on the CAMUS data test set, and vice versa. When trained on the EchoNet dataset and tested on CAMUS, the method achieved a mean absolute error of 4.6%, and a correlation coefficient of 0.98 for EF estimation, which outperforms the state of the arts. Conversely, training on CAMUS and testing on EchoNet resulted in a mean absolute error of 9% and a correlation coefficient of 0.75.

关键词： Training Correlation coefficient Heart Image segmentation Adaptation models Accuracy Ultrasonic imaging Echocardiography Generative adversarial networks Testing

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APC System: A system for the Blind and Deaf Who Face Visual and dept. Perception Challenges

APC System: A system for the Blind and Deaf Who Face Visual ...

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2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Mubeen, Suraya Ashritha, G.V. Bandru, Sanjeev Sireesha, Marri Bhaskar, Nuthanakanti Kumar, Voruganti Naresh Cmr Technical Campus Dept. of Electronics and Communication Engineering Telangana Hyderabad India Telangana Hyderabad India Sreenidhi Institute of Science and Technology Dept. of Information Technology Telangana Hyderabad India Cmr Technical Campus Dept. of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350306927

In this paper, APC System (Assistive Path Control System) is proposed to provide blind and deaf individuals with the assistance they need to navigate their environment by using the "third eye"technology. As part of the technology, a hand glove and a stick are used to identify obstacles using ultrasonic sensors and to provide haptic feedback through vibrations in order to detect obstacles. Wearers of the glove are alerted through a buzzer and provided with feedback on distance and direction of obstacles in their path, while those of the stick are given feedback on the change in elevation and dept. over time. The use of vibrations as a form of feedback is crucial to the functioning of deaf people. Testing of the technology has shown promising results, and future work will focus on optimizing accuracy and accessibility by using machine learning algorithms in order to improve the system. As a primary goal of the APC System is to enhance the quality of life for blind and deaf individuals by providing them with a tool that will help them navigate their environment in the most effective manner. © 2023 IEEE.

关键词： Feedback

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AI for Discovering the Role of Cognitive and Brain Reserves in Parkinson's Disease Classification 3

AI for Discovering the Role of Cognitive and Brain Reserves ...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Siviero, Ilaria Pirastru, Alice Valè, Nicola Blasi, Valeria Menegaz, Gloria Baglio, Francesca Galazzo, Ilaria Boscolo Storti, Silvia F. University of Verona Dept. of Computer Science Verona Italy Irccs Fondazione Don Carlo Gnocchi Onlus Milan Italy Politecnico di Milano Dept. of Electronics Information and Bioengineering Milan Italy University of Verona Dept. of Engineering for Innovation Medicine Verona Italy

ISBN: (纸本)9798350378009

This study explores the combined impact of cognitive and brain reserves with resting-state functional magnetic resonance imaging (rs-fMRI) based functional connectivity on Parkinson's Disease (PD) classification. A machine learning pipeline is presented aimed at discriminating between 52 healthy controls and 43 subjects with PD using a support vector machine (SVM) classifier trained using a 5-fold cross-validation approach. Augmenting our approach with an eXplainable artificial intelligence (XAI) tool, specifically the SHapley Additive exPlanation (SHAP) method for feature ranking, we explained the underlying mechanisms guiding the model decision. The results showed an average accuracy of 94.74% using the top 20 features with the highest SHAP importance score. Specific connections, such as those governing visual central and dorsal attention, emerged as key discriminative features, significantly impacting on the model's ability to classify PD subjects. © 2024 IEEE.

关键词： Neurodegenerative diseases

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