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Image Recognition-Based Indoor Positioning System Using Federated Learning 100

Image Recognition-Based Indoor Positioning System Using Fede...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Yu, Heng-Shao Jhuang, Yu-Chiao Leu, Jenq-Shiou University of Science and Technology Dept. of Electronic and Computer Engineering Taipei Taiwan

ISBN: (纸本)9798331517786

With the advancement of artificial intelligence (AI), indoor positioning systems have become increasingly important for various applications, leading to the development of diverse indoor positioning methods. Among various approaches, image-based indoor positioning methods have demonstrated relatively good positioning accuracy. However, maintaining user privacy continues to be a significant challenge. To tackle this issue, we propose a Federated Learning (FL) approach and develop an FL-based indoor image recognition positioning system. We conducted experiments to validate our method using two real-world datasets and compare the results to a Non-FL approach. Furthermore, we evaluated the accuracy of our method across various communication rounds, the number of client devices, and the amount of data per client device. Our experimental results indicate that the proposed method effectively preserves client privacy while achieving accuracy similar to the Non-FL approach. By employing FedAvg and FedOpt algorithms with the MobileNet model, our system ultimately reaches 94% accuracy. © 2024 IEEE.

关键词： Federated learning

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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Key Vertex Pairs Extraction for Graph Classification Based on Probability Distribution Learning 6

Key Vertex Pairs Extraction for Graph Classification Based o...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Huang, Jianming Kasai, Hiroyuki WASEDA University Dept. of Computer Science and Communication Engineering Tokyo Japan

ISBN: (纸本)9798350344349

Graph classification is a hot topic of machine learning for graph-structured data, and it is also a very potential and valuable research. However, the difficulty of graph classification is challenging and special, which is quite different from the normal classification problems. One of the most difficult points of graph classification is that the numbers of vertex neighbors in graphs are usually variable, which makes the number of weights uncertain and ambiguous. Recent work such like the graph attention network apply the transformer on the graph neural network. However, the learned attentions cannot strictly reveal the importance of each part of graph, which makes the model less explainable. Moreover, for small datasets, it performs less effectively because of the excessive parameters. In order to overcome these difficulties, we propose a lightweight model with an edge weighting function based on the probability distributions of node pair features learned by the Gaussian mixture model. Although the proposed framework is simple, the experimental results shows its effectiveness on small datasets. © 2024 IEEE.

关键词： Graph neural networks

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Optimization of Cost-based Dynamic Economic Dispatch Problem using Mathematical Programming

Optimization of Cost-based Dynamic Economic Dispatch Problem...

引用

2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Mahmood, Mou Chowdhury, Nahid-Ur-Rahman Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Dept. of Electrical and Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This paper introduces a robust and effective methodology for addressing the dynamic economic dispatch (DED) optimization problem, which is pivotal in power system operations and planning. DED is basically an enhanced version of static economic dispatch (SED) problem that involves scheduling a set of generation units over a typical operational period, often spanning 24 hours, to cover the predicted load demand with the on/off status of each generation unit predetermined. The literature distinguishes between two main types of DED: cost-based and price-based. This paper focuses on the cost-based approach, utilizing quadratic constrained programming (QCP) to model the problem effectively. QCP is particularly suited for this task as it optimally manages the ramp rate constraints and capacities of the available generation units maintaining the minimum operating cost. To demonstrate the effectiveness of the proposed methodology, simulations were conducted using two distinct scenarios involving 5-unit and 10-unit power dispatch strategies. The results, obtained through the general algebraic modeling system (GAMS), clearly presents the superiority of GAMS in achieving optimal solutions when compared to other existing metaheuristic techniques. © 2024 IEEE.

关键词： Quadratic programming

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A Comprehensive Review of Media Forensics and Deepfake Detection Technique 10

A Comprehensive Review of Media Forensics and Deepfake Detec...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kandari, Megha Tripathi, Vikas Pant, Bhaskar Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9789380544472

In recent years, as the use of the internet and social media has advanced, so the number of fake media content also grows rapidly. Media forensic techniques are becoming essential to analyze and extract information from various types of media, such as images, videos, and audio recordings, to be used as evidence in legal proceedings. As of recent trends, social media is being flooded with sophisticated deepfakes. So evolving deepfake detection technique is required to curb the flow of misinformation via deepfake videos. This paper intends to present a review of evolving media forensics and deepfake detection techniques. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Social networking (online)

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Early Prediction of Diabetes using Several Machine Learning Algorithms 7

Early Prediction of Diabetes using Several Machine Learning ...

引用

7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rexie, J.A.M. Santhosh, P. Solomon, P. Nikhil Vishnu, P. Atul Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397253

One of the main issues diabetes poses to the medical profession globally is that its consequences are escalating swiftly. Elevated blood glucose levels cause diabetes, also referred to as diabetes mellitus or simply diabetes. On the basis of physical and chemical exams, a number of standard approaches can be used to diagnose diabetes. But, Doctors face a difficult task in predicting diabetes that affects the kidneys, eyes, heart, nerves, feet and other parts of the *** diagnosis analyses illness prognosis and diagnosis using a doctor's training and expertise, although this might be vulnerable to error. Machine learning and data science approaches have the potential to enrich other scientific disciplines by providing new perspectives on well-known issues. One such initiative is to assist in making predictions based on medical data. 'Machine learning' describes the processes by which computers tend to learn from experience, and is a recent area of data science. The aim of this research is to develop a system to accurately diagnose diabetes in patients at an early stage by comparing the results of several machine learning algorithms. Use four different supervised machine learning techniques: Random Forests RF, Logistic LR Regression,Decision DT Trees and (SVM) Support Vector Machines. The main objective of this research project is to develop a prognostic tool for early detection and prediction of *** model is also deployed into a web application using Python Flask, and the web application is built using HTML and CSS. Anyone can input features into the web application, and the model - which was previously developed using machine learning techniques - will then predict whether or not they will be diagnosed with diabetes. © 2023 IEEE.

关键词： Diagnosis

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PraaKrum: A Practical Byzantine-Resilient Federated Learning Algorithm 16

PraaKrum: A Practical Byzantine-Resilient Federated Learning...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Kukreja, Prabhleen Mahendran, V. Indian Institute of Technology Tirupati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

Federated Learning (FL) is considered as a suitable paradigm for intelligent data analytics over Internet of Thing (IoT) devices. While the data-privacy preserving feature of FL is useful, the lack of data auditing abilities of the participating IoTs creates a potential attack surface. To this end, a potential attacker can perform data/model poisoning attacks on the FL. To protect from such attacks, a popular theoretically proven algorithm for FL namely, Krum [1] was proposed. From an implementation perspective, the Krum algorithm needs to be provided with apriori information such as the total number of attackers in each round, which is impractical. It is not feasible to know, in advance, which of the participating IoT clients are attackers. Furthermore, in the presence of mobile Parameter Server (PS) which stays in the WiFi coverage area consisting of stationary clients for an unknown time, the implementation of Krum algorithm in such practical setting is not investigated, to the best of our knowledge. With an aim to provide a practical implementation of a resilient FL framework, for the first time, this paper investigates the sensitivity of the attackers information towards the robustness offered by the Krum-based FL algorithm. The insights gained from the sensitivity analysis are used to develop a novel and Practical algorithm of Krum (PraaKrum) for FL for a WiFi network with mobile PS. The developed analysis is helpful in both understanding the key resilient characteristics offered by the Krum algorithm and providing the research community with a guideline towards the practical implementation of the attacker-resilient Krum-based FL algorithm in a mobile wireless setting. © 2024 IEEE.

关键词： Byzantine-resilience Federated learning insensitivity result Krum algorithm WiFi wireless IoT

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A Deep Learning Approach to Analyze the Relationship Between Gender, Height, Weight, and Basal Metabolic Rate from Face Images 2

A Deep Learning Approach to Analyze the Relationship Between...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Siam, Shykul Islam Chowdhury, S.A.H. Ahmed, Nafi Biswas, Swapneel Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Ahsanullah University of Science & Technology Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798331517984

In response to existing limitations, a novel convolutional neural network (CNN) model tailored for facial attribute estimation is introduced in this study. The methodology encompasses meticulous data preprocessing techniques, including dataset splitting, resizing, cleaning, augmentation, and cropping, to ensure the integrity of the dataset. Through rigorous real-world testing, remarkable accuracy in predicting gender, height, weight, and basal metabolic rate (BMR) from facial photos is demonstrated by our CNN architecture. Notably, unlike conventional approaches where BMR is directly estimated from facial images, three output neurons are employed by our model to simultaneously predict gender, height, and weight, which are sub-sequently utilized for BMR calculation. A primary contribution is made through the development of a comprehensive methodology that advances the reliability of facial attribute estimation by achieving an outstanding accuracy of 98.50% on our dataset named Face-ete and a commendable accuracy of 88.29% on another dataset. Our model outperforms the second-best model by percentage relative improvement factors of 2.19 and 4.28 in terms of BMR prediction accuracy for the 'Faceere' and the other dataset, respectively. This work represents a significant step forward in the field of facial attribute estimation. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring the Efficacy of Modified Transfer Learning in Identifying Parkinson’s Disease Through Drawn Image Patterns 2

Exploring the Efficacy of Modified Transfer Learning in Iden...

引用

2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Daiyan, Nabil Haque, Md Rakibul Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331508227

Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by the death of dopaminergic neurons, leading to various movement disorder symptoms. Early diagnosis of PD is crucial to prevent adverse effects, yet traditional diagnostic methods are often cumbersome and costly. In this study, a machine learning-based approach is proposed using hand-drawn spiral and wave images as potential biomarkers for PD detection. Our methodology leverages convolutional neural networks (CNNs), transfer learning, and attention mechanisms to improve model performance and resilience against overfitting. To enhance the diversity and richness of both spiral and wave categories, the training dataset undergoes augmentation to increase the number of images. The proposed architecture comprises three phases: utilizing pre-trained CNNs, incorporating custom convolutional layers, and ensemble voting. Employing hard voting further enhances performance by aggregating predictions from multiple models. Experimental results show promising accuracy rates. For spiral images, weighted average precision, recall, and F1-score are 90%, and for wave images, they are 96.67%. After combining the predictions through ensemble hard voting, the overall accuracy is 93.3%. These findings underscore the potential of machine learning in early PD diagnosis, offering a non-invasive and cost-effective solution to improve patient outcomes. ©2024 IEEE.

关键词： Convolutional neural networks

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Automated Explanation Quality Assessment based on Similarity Calculations of Representative Explanations of Different Qualities 7

Automated Explanation Quality Assessment based on Similarity...

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7th IEEE Eurasian Conference on Educational Innovation, ECEI 2024

作者： Chou, Chih-Yueh Chou, Yu-Ting Huang, Yu-Cheng Yuan Ze University Dept. of Computer Science & Engineering Taoyuan Taiwan

ISBN: (纸本)9798350307207

Peer instruction is instructional in guiding students to learn by answering questions, and explaining and discussing their answers with peers. Researchers recommended asking students to write down their answers and explanations before discussion to prevent social loafing. In addition, text-based explanations can be recorded and analyzed. The quality of students' explanations varies, ranging from superficial and low-quality to detailed and in-dept. high-quality explanations. In tradition, the qualities of students' explanations were assessed by experts. Recently, machine learning classification models have been developed and applied to classify texts. However, the level of explanations of questions are question-dependent. Thus, each question needs its classification model. Therefore, a feature transformation was applied in this study so that the explanations of different questions could be combined and applied to train the same classification model. An automated explanation quality assessment mechanism was developed based on the similarity of representative explanations of different qualities. Students' text-based explanations were collected and assessed by experts into four levels, ranging from 0 (worst) to 3 (best). The four-level classifications were merged into binary classifications of low (0 and 1) and high (2 and 3). Different classification models, including Support Vector Machine (SVM), Naive Bayes (NB), K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Bidirectional Encoder Representations from Transformers (BERT) were applied to train models and evaluate the accuracy of the models. In addition, three ensemble learning algorithms, including voting, stacking, and boosting, were applied to combine models chosen from SVM, NB, KNN, LR, and RF. The results showed that RF and RF+KNN+NB with stacking model showed the best accuracy (75.3%) among all four-level classification models whereas RF with boosting model showed the best accuracy (9

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